{ "Contributors": [ "Sujan Reddy A" ], "Source": [ "scitldr" ], "URL": [ "https://github.com/allenai/scitldr" ], "Categories": [ "Summarization" ], "Reasoning": [], "Definition": [ "In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words." ], "Input_language": [ "English" ], "Output_language": [ "English" ], "Instruction_language": [ "English" ], "Domains": [ "Scientific Research Papers" ], "Positive Examples": [ { "input": "Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point.", "output": "We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.", "explanation": "The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct." }, { "input": "Many real-world problems, e.g. object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors, matrices or tensors. We present a novel approach for learning to predict sets with unknown permutation and cardinality using deep neural networks. Specifically, in our formulation we incorporate the permutation as unobservable variable and estimate its distribution during the learning process using alternating optimization. We demonstrate the validity of this new formulation on two relevant vision problems: object detection, for which our formulation outperforms state-of-the-art detectors such as Faster R-CNN and YOLO, and a complex CAPTCHA test, where we observe that, surprisingly, our set based network acquired the ability of mimicking arithmetics without any rules being coded.", "output": "We present a novel approach for learning to predict sets with unknown permutation and cardinality using feed-forward deep neural networks.", "explanation": "The generated output is appropriate because the abstract focusses on designing a Deep Neural Network model for learning to predict sets with unknown permutation and cardinality." } ], "Negative Examples": [ { "input": "Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point.", "output": "The paper proposes variance regularizing adversarial learning for training gans.", "explanation": "This summary is not appropriate as the abstract does not state anything about gans." }, { "input": "Many real-world problems, e.g. object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors, matrices or tensors. We present a novel approach for learning to predict sets with unknown permutation and cardinality using deep neural networks. Specifically, in our formulation we incorporate the permutation as unobservable variable and estimate its distribution during the learning process using alternating optimization. We demonstrate the validity of this new formulation on two relevant vision problems: object detection, for which our formulation outperforms state-of-the-art detectors such as Faster R-CNN and YOLO, and a complex CAPTCHA test, where we observe that, surprisingly, our set based network acquired the ability of mimicking arithmetics without any rules being coded.", "output": "The paper proposes a deep neural network model to deal with structured outputs such as vectors, matrices or tensors.", "explanation": "This summary is not appropriate because proposes a solution to deal with sets with unknown permutation and cardinality." } ], "Instances": [ { "id": "task668-bc8e02e4859c4854a07315b376de7523", "input": "Learning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax. For example, long-range dependencies induced by using the same variable or function in distant locations are often not considered. We propose to use graphs to represent both the syntactic and semantic structure of code and use graph-based deep learning methods to learn to reason over program structures.\n\n In this work, we present how to construct graphs from source code and how to scale Gated Graph Neural Networks training to such large graphs. We evaluate our method on two tasks: VarNaming, in which a network attempts to predict the name of a variable given its usage, and VarMisuse, in which the network learns to reason about selecting the correct variable that should be used at a given program location. Our comparison to methods that use less structured program representations shows the advantages of modeling known structure, and suggests that our models learn to infer meaningful names and to solve the VarMisuse task in many cases. Additionally, our testing showed that VarMisuse identifies a number of bugs in mature open-source projects.", "output": [ "Programs have structure that can be represented as graphs, and graph neural networks can learn to find bugs on such graphs" ] }, { "id": "task668-732d9c12a1694d18a133b96885ed40a2", "input": "While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage requirements. Consequently, model size reduction has become an utmost goal in deep learning. A typical approach is to train a set of deterministic weights, while applying certain techniques such as pruning and quantization, in order that the empirical weight distribution becomes amenable to Shannon-style coding schemes. However, as shown in this paper, relaxing weight determinism and using a full variational distribution over weights allows for more efficient coding schemes and consequently higher compression rates. In particular, following the classical bits-back argument, we encode the network weights using a random sample, requiring only a number of bits corresponding to the Kullback-Leibler divergence between the sampled variational distribution and the encoding distribution. By imposing a constraint on the Kullback-Leibler divergence, we are able to explicitly control the compression rate, while optimizing the expected loss on the training set. The employed encoding scheme can be shown to be close to the optimal information-theoretical lower bound, with respect to the employed variational family. Our method sets new state-of-the-art in neural network compression, as it strictly dominates previous approaches in a Pareto sense: On the benchmarks LeNet-5/MNIST and VGG-16/CIFAR-10, our approach yields the best test performance for a fixed memory budget, and vice versa, it achieves the highest compression rates for a fixed test performance.", "output": [ "This paper proposes an effective method to compress neural networks based on recent results in information theory." ] }, { "id": "task668-17fd616db88d4277b97786d7ad67330b", "input": "There has been recent interest in improving performance of simple models for multiple reasons such as interpretability, robust learning from small data, deployment in memory constrained settings as well as environmental considerations. In this paper, we propose a novel method SRatio that can utilize information from high performing complex models (viz. deep neural networks, boosted trees, random forests) to reweight a training dataset for a potentially low performing simple model such as a decision tree or a shallow network enhancing its performance. Our method also leverages the per sample hardness estimate of the simple model which is not the case with the prior works which primarily consider the complex model's confidences/predictions and is thus conceptually novel. Moreover, we generalize and formalize the concept of attaching probes to intermediate layers of a neural network, which was one of the main ideas in previous work \\citep{profweight}, to other commonly used classifiers and incorporate this into our method. The benefit of these contributions is witnessed in the experiments where on 6 UCI datasets and CIFAR-10 we outperform competitors in a majority (16 out of 27) of the cases and tie for best performance in the remaining cases. In fact, in a couple of cases, we even approach the complex model's performance. We also conduct further experiments to validate assertions and intuitively understand why our method works. Theoretically, we motivate our approach by showing that the weighted loss minimized by simple models using our weighting upper bounds the loss of the complex model.", "output": [ "Method to improve simple models performance given a (accurate) complex model.", "The paper proposes a means of improving the predictions of a low-capacity model which shows benefits over existing approaches." ] }, { "id": "task668-8a3dad5b7dcd4c6e828456c1da3c99e1", "input": "We present a neural architecture search algorithm to construct compact reinforcement learning (RL) policies, by combining ENAS and ES in a highly scalable and intuitive way. By defining the combinatorial search space of NAS to be the set of different edge-partitionings (colorings) into same-weight classes, we represent compact architectures via efficient learned edge-partitionings. For several RL tasks, we manage to learn colorings translating to effective policies parameterized by as few as 17 weight parameters, providing >90 % compression over vanilla policies and 6x compression over state-of-the-art compact policies based on Toeplitz matrices, while still maintaining good reward. We believe that our work is one of the first attempts to propose a rigorous approach to training structured neural network architectures for RL problems that are of interest especially in mobile robotics with limited storage and computational resources.", "output": [ "We show that ENAS with ES-optimization in RL is highly scalable, and use it to compactify neural network policies by weight sharing.", "The authors construct reinforcement learning policies with very few parameters by compressing a feed-forward neural network, forcing it to share weights, and using a reinforcement learning method to learn the mapping of shared weights.", "This paper combines ideas from ENAS and ES methods for optimisation, and introduces the chromatic network architecture, which partitions weights of the RL network into tied sub-groups." ] }, { "id": "task668-d835a73cb03e40e5b2b7adea07786767", "input": "Deep neural networks have been tremendously successful in a number of tasks.\n One of the main reasons for this is their capability to automatically\n learn representations of data in levels of abstraction,\n increasingly disentangling the data as the internal transformations are applied.\n In this paper we propose a novel regularization method that penalize covariance between dimensions of the hidden layers in a network, something that benefits the disentanglement.\n This makes the network learn nonlinear representations that are linearly uncorrelated, yet allows the model to obtain good results on a number of tasks, as demonstrated by our experimental evaluation.\n The proposed technique can be used to find the dimensionality of the underlying data, because it effectively disables dimensions that aren't needed.\n Our approach is simple and computationally cheap, as it can be applied as a regularizer to any gradient-based learning model.", "output": [ "We propose a novel regularization method that penalize covariance between dimensions of the hidden layers in a network.", "This paper presents a regularization mechanism which penalizes covariance between all dimensions in the latent representation of a neural network in order to disentangle the latent representation" ] }, { "id": "task668-c54682a7ae574a2694e76425ccb8febb", "input": "We capitalize on the natural compositional structure of images in order to learn object segmentation with weakly labeled images. The intuition behind our approach is that removing objects from images will yield natural images, however removing random patches will yield unnatural images. We leverage this signal to develop a generative model that decomposes an image into layers, and when all layers are combined, it reconstructs the input image. However, when a layer is removed, the model learns to produce a different image that still looks natural to an adversary, which is possible by removing objects. Experiments and visualizations suggest that this model automatically learns object segmentation on images labeled only by scene better than baselines.", "output": [ "Weakly-supervised image segmentation using compositional structure of images and generative models.", "This paper creates a layered representation in order to better learn segmentation from unlabeled images.", "This paper proposes a GAN-based generative model that decomposes images into multiple layers, where the objective of the GAN is to distinguish real images from images formed by combining the layers.", "This paper proposes a neural network architecture around the idea of layered scene composition" ] }, { "id": "task668-d62925f5ecb1413c9d11eb97dbdaa4f4", "input": "Variational Autoencoders (VAEs) have proven to be powerful latent variable models. How- ever, the form of the approximate posterior can limit the expressiveness of the model. Categorical distributions are flexible and useful building blocks for example in neural memory layers. We introduce the Hierarchical Discrete Variational Autoencoder (HD-VAE): a hi- erarchy of variational memory layers. The Concrete/Gumbel-Softmax relaxation allows maximizing a surrogate of the Evidence Lower Bound by stochastic gradient ascent. We show that, when using a limited number of latent variables, HD-VAE outperforms the Gaussian baseline on modelling multiple binary image datasets. Training very deep HD-VAE remains a challenge due to the relaxation bias that is induced by the use of a surrogate objective. We introduce a formal definition and conduct a preliminary theoretical and empirical study of the bias.", "output": [ "In this paper, we introduce a discrete hierarchy of categorical latent variables that we train using the Concrete/Gumbel-Softmax relaxation and we derive an upper bound for the absolute difference between the unbiased and the biased objective." ] }, { "id": "task668-6172b93973664f8cb167c2d770c5e7b5", "input": "Variational inference (VI) and Markov chain Monte Carlo (MCMC) are approximate posterior inference algorithms that are often said to have complementary strengths, with VI being fast but biased and MCMC being slower but asymptotically unbiased. In this paper, we analyze gradient-based MCMC and VI procedures and find theoretical and empirical evidence that these procedures are not as different as one might think. In particular, a close examination of the Fokker-Planck equation that governs the Langevin dynamics (LD) MCMC procedure reveals that LD implicitly follows a gradient flow that corresponds to a variational inference procedure based on optimizing a nonparametric normalizing flow. This result suggests that the transient bias of LD (due to too few warmup steps) may track that of VI (due to too few optimization steps), up to differences due to VI’s parameterization and asymptotic bias. Empirically, we find that the transient biases of these algorithms (and momentum-accelerated versions) do evolve similarly. This suggests that practitioners with a limited time budget may get more accurate results by running an MCMC procedure (even if it’s far from burned in) than a VI procedure, as long as the variance of the MCMC estimator can be dealt with (e.g., by running many parallel chains).", "output": [ "The transient behavior of gradient-based MCMC and variational inference algorithms is more similar than one might think, calling into question the claim that variational inference is faster than MCMC." ] }, { "id": "task668-e6d6b66653d74feb8982b24759d1f666", "input": "We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit setting, and adapt them to the $Q$-learning setting. We propose an exploration strategy based on upper-confidence bounds (UCB). Our experiments show significant gains on the Atari benchmark.", "output": [ "Adapting UCB exploration to ensemble Q-learning improves over prior methods such as Double DQN, A3C+ on Atari benchmark" ] }, { "id": "task668-3e00eaf5a90d4d4b958c8f9de7ed6bb9", "input": "Machine learning algorithms are vulnerable to poisoning attacks: An adversary can inject malicious points in the training dataset to influence the learning process and degrade the algorithm's performance. Optimal poisoning attacks have already been proposed to evaluate worst-case scenarios, modelling attacks as a bi-level optimization problem. Solving these problems is computationally demanding and has limited applicability for some models such as deep networks. In this paper we introduce a novel generative model to craft systematic poisoning attacks against machine learning classifiers generating adversarial training examples, i.e. samples that look like genuine data points but that degrade the classifier's accuracy when used for training. We propose a Generative Adversarial Net with three components: generator, discriminator, and the target classifier. This approach allows us to model naturally the detectability constrains that can be expected in realistic attacks and to identify the regions of the underlying data distribution that can be more vulnerable to data poisoning. Our experimental evaluation shows the effectiveness of our attack to compromise machine learning classifiers, including deep networks.", "output": [ "In this paper we propose a novel generative model to craft systematic poisoning attacks with detectability constraints against machine learning classifiers, including deep networks. " ] }, { "id": "task668-d236aaf34a8d419cae0004bcb5b1e77b", "input": " We present a probabilistic framework for session based recommendation. A latent variable for the user state is updated as the user views more items and we learn more about their interests. We provide computational solutions using both the re-parameterization trick and using the Bouchard bound for the softmax function, we further explore employing a variational auto-encoder and a variational Expectation-Maximization algorithm for tightening the variational bound. Finally we show that the Bouchard bound causes the denominator of the softmax to decompose into a sum enabling fast noisy gradients of the bound giving a fully probabilistic algorithm reminiscent of word2vec and a fast online EM algorithm.\n", "output": [ "Fast variational approximations for approximating a user state and learning product embeddings" ] }, { "id": "task668-0f7c8aa0037c4c8fb147c917da870371", "input": "We introduce a parameter sharing scheme, in which different layers of a convolutional neural network (CNN) are defined by a learned linear combination of parameter tensors from a global bank of templates. Restricting the number of templates yields a flexible hybridization of traditional CNNs and recurrent networks. Compared to traditional CNNs, we demonstrate substantial parameter savings on standard image classification tasks, while maintaining accuracy.\n Our simple parameter sharing scheme, though defined via soft weights, in practice often yields trained networks with near strict recurrent structure; with negligible side effects, they convert into networks with actual loops. Training these networks thus implicitly involves discovery of suitable recurrent architectures. Though considering only the aspect of recurrent links, our trained networks achieve accuracy competitive with those built using state-of-the-art neural architecture search (NAS) procedures.\n Our hybridization of recurrent and convolutional networks may also represent a beneficial architectural bias. Specifically, on synthetic tasks which are algorithmic in nature, our hybrid networks both train faster and extrapolate better to test examples outside the span of the training set.", "output": [ "We propose a method that enables CNN folding to create recurrent connections" ] }, { "id": "task668-b117fce9080c4f59a11a9fce6c451844", "input": "This paper improves upon the line of research that formulates named entity recognition (NER) as a sequence-labeling problem. We use so-called black-box long short-term memory (LSTM) encoders to achieve state-of-the-art results while providing insightful understanding of what the auto-regressive model learns with a parallel self-attention mechanism. Specifically, we decouple the sequence-labeling problem of NER into entity chunking, e.g., Barack_B Obama_E was_O elected_O, and entity typing, e.g., Barack_PERSON Obama_PERSON was_NONE elected_NONE, and analyze how the model learns to, or has difficulties in, capturing text patterns for each of the subtasks. The insights we gain then lead us to explore a more sophisticated deep cross-Bi-LSTM encoder, which proves better at capturing global interactions given both empirical results and a theoretical justification.", "output": [ "We provide insightful understanding of sequence-labeling NER and propose to use two types of cross structures, both of which bring theoretical and empirical improvements." ] }, { "id": "task668-76e0a181398e4b3aa7005668b3f8e685", "input": "Do GANS (Generative Adversarial Nets) actually learn the target distribution? The foundational paper of Goodfellow et al. (2014) suggested they do, if they were given sufficiently large deep nets, sample size, and computation time. A recent theoretical analysis in Arora et al. (2017) raised doubts whether the same holds when discriminator has bounded size. It showed that the training objective can approach its optimum value even if the generated distribution has very low support. In other words, the training objective is unable to prevent mode collapse. The current paper makes two contributions. (1) It proposes a novel test for estimating support size using the birthday paradox of discrete probability. Using this evidence is presented that well-known GANs approaches do learn distributions of fairly low support. (2) It theoretically studies encoder-decoder GANs architectures (e.g., BiGAN/ALI), which were proposed to learn more meaningful features via GANs, and consequently to also solve the mode-collapse issue. Our result shows that such encoder-decoder training objectives also cannot guarantee learning of the full distribution because they cannot prevent serious mode collapse. More seriously, they cannot prevent learning meaningless codes for data, contrary to usual intuition.", "output": [ "We propose a support size estimator of GANs's learned distribution to show they indeed suffer from mode collapse, and we prove that encoder-decoder GANs do not avoid the issue as well.", "The paper attempts to estimate the size of the support for solutions produced by typical GANs experimentally. ", "This paper proposes a clever new test based on the birthday paradox for measuring diversity in generated sample, with experiment results interpreted to mean that mode collapse is strong in a number of state-of-the-art generative models.", "The paper uses birthday paradox to show that some GAN architectures generate distributions with fairly low support." ] }, { "id": "task668-83f38974cec54d3b847c8ce694553f62", "input": "While real brain networks exhibit functional modularity, we investigate whether functional mod- ularity also exists in Deep Neural Networks (DNN) trained through back-propagation. Under the hypothesis that DNN are also organized in task-specific modules, in this paper we seek to dissect a hidden layer into disjoint groups of task-specific hidden neurons with the help of relatively well- studied neuron attribution methods. By saying task-specific, we mean the hidden neurons in the same group are functionally related for predicting a set of similar data samples, i.e. samples with similar feature patterns.\n We argue that such groups of neurons which we call Functional Modules can serve as the basic functional unit in DNN. We propose a preliminary method to identify Functional Modules via bi- clustering attribution scores of hidden neurons.\n We find that first, unsurprisingly, the functional neurons are highly sparse, i.e., only a small sub- set of neurons are important for predicting a small subset of data samples and, while we do not use any label supervision, samples corresponding to the same group (bicluster) show surprisingly coherent feature patterns. We also show that these Functional Modules perform a critical role in discriminating data samples through ablation experiment.", "output": [ "We develop an approach to parcellate a hidden layer in DNN into functionally related groups, by applying spectral coclustering on the attribution scores of hidden neurons." ] }, { "id": "task668-55c91d8a61444ad7bce3cdd1a44bcaf2", "input": "In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most salient regions. To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the most salient regions in a principled manner. The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion. MAA samples multiple subsets from the video snippet features according to a set of latent discriminative probabilities and takes the expectation over all the averaged subset features . Theoretically, we prove that the MAA module with learned latent discriminative probabilities successfully reduces the difference in responses between the most salient regions and the others . Therefore, MAAN is able to generate better class activation sequences and identify dense and integral action regions in the videos . Moreover, we propose a fast algorithm to reduce the complexity of constructing MAA from $O(2^T)$ to $O(T^2)$. Extensive experiments on two large-scale video datasets show that our MAAN achieves a superior performance on weakly-supervised temporal action localization.\n\n\n", "output": [ "A novel marginalized average attentional network for weakly-supervised temporal action localization " ] }, { "id": "task668-1423c4e8bc064151bcab8ef6c6400475", "input": "Multi-step greedy policies have been extensively used in model-based Reinforcement Learning (RL) and in the case when a model of the environment is available (e.g., in the game of Go). In this work, we explore the benefits of multi-step greedy policies in model-free RL when employed in the framework of multi-step Dynamic Programming (DP): multi-step Policy and Value Iteration. These algorithms iteratively solve short-horizon decision problems and converge to the optimal solution of the original one. By using model-free algorithms as solvers of the short-horizon problems we derive fully model-free algorithms which are instances of the multi-step DP framework. As model-free algorithms are prone to instabilities w.r.t. the decision problem horizon, this simple approach can help in mitigating these instabilities and results in an improved model-free algorithms. We test this approach and show results on both discrete and continuous control problems.", "output": [ "Use model free algorithms like DQN/TRPO to solve short horizon problems (model free) iteratively in a Policy/Value Iteration fashion." ] }, { "id": "task668-d4fff056323342598da081bcd2ed3b9c", "input": "The goal of imitation learning (IL) is to learn a good policy from high-quality demonstrations. However, the quality of demonstrations in reality can be diverse, since it is easier and cheaper to collect demonstrations from a mix of experts and amateurs. IL in such situations can be challenging, especially when the level of demonstrators' expertise is unknown. We propose a new IL paradigm called Variational Imitation Learning with Diverse-quality demonstrations (VILD), where we explicitly model the level of demonstrators' expertise with a probabilistic graphical model and estimate it along with a reward function. We show that a naive estimation approach is not suitable to large state and action spaces, and fix this issue by using a variational approach that can be easily implemented using existing reinforcement learning methods. Experiments on continuous-control benchmarks demonstrate that VILD outperforms state-of-the-art methods. Our work enables scalable and data-efficient IL under more realistic settings than before.", "output": [ "We propose an imitation learning method to learn from diverse-quality demonstrations collected by demonstrators with different level of expertise." ] }, { "id": "task668-21da32e345554548b589b8e1a7df385e", "input": "Learning knowledge graph embeddings (KGEs) is an efficient approach to knowledge graph completion. Conventional KGEs often suffer from limited knowledge representation, which causes less accuracy especially when training on sparse knowledge graphs. To remedy this, we present Pretrain-KGEs, a training framework for learning better knowledgeable entity and relation embeddings, leveraging the abundant linguistic knowledge from pretrained language models. Specifically, we propose a unified approach in which we first learn entity and relation representations via pretrained language models and use the representations to initialize entity and relation embeddings for training KGE models. Our proposed method is model agnostic in the sense that it can be applied to any variant of KGE models. Experimental results show that our method can consistently improve results and achieve state-of-the-art performance using different KGE models such as TransE and QuatE, across four benchmark KG datasets in link prediction and triplet classification tasks.", "output": [ "We propose to learn knowledgeable entity and relation representations from Bert for knowledge graph embeddings." ] }, { "id": "task668-3a4990b23acd454293400d060f351ab9", "input": "Word alignments are useful for tasks like statistical and neural machine translation (NMT) and annotation projection. Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT. However, most approaches require parallel training data and quality decreases as less training data is available. We propose word alignment methods that require little or no parallel data. The key idea is to leverage multilingual word embeddings – both static and contextualized – for word alignment. Our multilingual embeddings are created from monolingual data only without relying on any parallel data or dictionaries. We find that traditional statistical aligners are outperformed by contextualized embeddings – even in scenarios with abundant parallel data. For example, for a set of 100k parallel sentences, contextualized embeddings achieve a word alignment F1 that is more than 5% higher (absolute) than eflomal.", "output": [ "We use representations trained without any parallel data for creating word alignments." ] }, { "id": "task668-e606b66cb4b04c33b7364b6e4bf37ba2", "input": "CNNs are widely successful in recognizing human actions in videos, albeit with a great cost of computation. This cost is significantly higher in the case of long-range actions, where a video can span up to a few minutes, on average. The goal of this paper is to reduce the computational cost of these CNNs, without sacrificing their performance. We propose VideoEpitoma, a neural network architecture comprising two modules: a timestamp selector and a video classifier. Given a long-range video of thousands of timesteps, the selector learns to choose only a few but most representative timesteps for the video. This selector resides on top of a lightweight CNN such as MobileNet and uses a novel gating module to take a binary decision: consider or discard a video timestep. This decision is conditioned on both the timestep-level feature and the video-level consensus. A heavyweight CNN model such as I3D takes the selected frames as input and performs video classification. Using off-the-shelf video classifiers, VideoEpitoma reduces the computation by up to 50\\% without compromising the accuracy. In addition, we show that if trained end-to-end, the selector learns to make better choices to the benefit of the classifier, despite the selector and the classifier residing on two different CNNs. Finally, we report state-of-the-art results on two datasets for long-range action recognition: Charades and Breakfast Actions, with much-reduced computation. In particular, we match the accuracy of I3D by using less than half of the computation.\n\n", "output": [ "Efficient video classification using frame-based conditional gating module for selecting most-dominant frames, followed by temporal modeling and classifier." ] }, { "id": "task668-604c16567c3b430882bfb2865b8fb70a", "input": "Reinforcement learning (RL) is frequently used to increase performance in text generation tasks,\n including machine translation (MT), \n notably through the use of Minimum Risk Training (MRT) and Generative Adversarial Networks (GAN). \n However, little is known about what and how these methods learn in the context of MT. \n We prove that one of the most common RL methods for MT does not optimize the \n expected reward, as well as show that other methods take an infeasibly long time to converge.\n In fact, our results suggest that RL practices in MT are likely to improve performance\n only where the pre-trained parameters are already close to yielding the correct translation.\n Our findings further suggest that observed gains may be due to effects unrelated to the training signal, concretely, changes in the shape of the distribution curve.", "output": [ "Reinforcment practices for machine translation performance gains might not come from better predictions." ] }, { "id": "task668-79ac155aafe64ea497f46aaeac7330ad", "input": "We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model. We use this property, applying fully convolutional models to lossless compression, demonstrating a method to scale the VAE-based 'Bits-Back with ANS' algorithm for lossless compression to large color photographs, and achieving state of the art for compression of full size ImageNet images. We release Craystack, an open source library for convenient prototyping of lossless compression using probabilistic models, along with full implementations of all of our compression results.", "output": [ "We scale up lossless compression with latent variables, beating existing approaches on full-size ImageNet images." ] }, { "id": "task668-058d3a1a0a244e3dad899c574299a4f1", "input": "Improving the accuracy of numerical methods remains a central challenge in many disciplines and is especially important for nonlinear simulation problems. A representative example of such problems is fluid flow, which has been thoroughly studied to arrive at efficient simulations of complex flow phenomena. This paper presents a data-driven approach that learns to improve the accuracy of numerical solvers. The proposed method utilizes an advanced numerical scheme with a fine simulation resolution to acquire reference data. We, then, employ a neural network that infers a correction to move a coarse thus quickly obtainable result closer to the reference data. We provide insights into the targeted learning problem with different learning approaches: fully supervised learning methods with a naive and an optimized data acquisition as well as an unsupervised learning method with a differentiable Navier-Stokes solver. While our approach is very general and applicable to arbitrary partial differential equation models, we specifically highlight gains in accuracy for fluid flow simulations.", "output": [ "We introduce a neural network approach to assist partial differential equation solvers.", "The authors aim at improving the accuracy of numerical solvers by training a neural network on simulated reference data which corrects the numerical solver." ] }, { "id": "task668-f7f565d88faf427d9981b4ab2a99447e", "input": "Generative Adversarial Networks (GANs), when trained on large datasets with diverse modes, are known to produce conflated images which do not distinctly belong to any of the modes. We hypothesize that this problem occurs due to the interaction between two facts: (1) For datasets with large variety, it is likely that the modes lie on separate manifolds. (2) The generator (G) is formulated as a continuous function, and the input noise is derived from a connected set, due to which G's output is a connected set. If G covers all modes, then there must be some portion of G's output which connects them. This corresponds to undesirable, conflated images. We develop theoretical arguments to support these intuitions. We propose a novel method to break the second assumption via learnable discontinuities in the latent noise space. Equivalently, it can be viewed as training several generators, thus creating discontinuities in the G function. We also augment the GAN formulation with a classifier C that predicts which noise partition/generator produced the output images, encouraging diversity between each partition/generator. We experiment on MNIST, celebA, STL-10, and a difficult dataset with clearly distinct modes, and show that the noise partitions correspond to different modes of the data distribution, and produce images of superior quality.", "output": [ "We introduce theory to explain the failure of GANs on complex datasets and propose a solution to fix it." ] }, { "id": "task668-9acddfc813364107ad179a52f92a7e24", "input": "With the rapid proliferation of IoT devices, our cyberspace is nowadays dominated by billions of low-cost computing nodes, which expose an unprecedented heterogeneity to our computing systems. Dynamic analysis, one of the most effective approaches to finding software bugs, has become paralyzed due to the lack of a generic emulator capable of running diverse previously-unseen firmware. In recent years, we have witnessed devastating security breaches targeting IoT devices. These security concerns have significantly hamstrung further evolution of IoT technology. In this work, we present Laelaps, a device emulator specifically designed to run diverse software on low-cost IoT devices. We do not encode into our emulator any specific information about a device. Instead, Laelaps infers the expected behavior of firmware via symbolic-execution-assisted peripheral emulation and generates proper inputs to steer concrete execution on the fly. This unique design feature makes Laelaps the first generic device emulator capable of running diverse firmware with no a priori knowledge about the target device. To demonstrate the capabilities of Laelaps, we deployed two popular dynamic analysis techniques---fuzzing testing and dynamic symbolic execution---on top of our emulator. We successfully identified both self-injected and real-world vulnerabilities.", "output": [ "Device-agnostic Firmware Execution" ] }, { "id": "task668-7ba32c9b1af24c91a2161def01bc5a9e", "input": "Interpreting generative adversarial network (GAN) training as approximate divergence minimization has been\n theoretically insightful, has spurred discussion, and has lead to theoretically and practically interesting\n extensions such as f-GANs and Wasserstein GANs. For both classic GANs and f-GANs, there is an original variant of training and a \"non-saturating\" variant which uses an alternative form of generator gradient. The original variant is theoretically easier to study, but for GANs the alternative variant performs better in practice. The non-saturating scheme is often regarded as a simple modification to deal with optimization issues, but we show that in fact the non-saturating scheme for GANs is effectively optimizing a reverse KL-like f-divergence. We also develop a number of theoretical tools to help compare and classify f-divergences. We hope these results may help to clarify some of the theoretical discussion surrounding the divergence minimization view of GAN training.", "output": [ "Non-saturating GAN training effectively minimizes a reverse KL-like f-divergence.", "This paper proposes a useful expression of the class of f-divergences, investigates theoretical properties of popular f-divergences from newly developed tools, and investigates GANs with the non-saturating training scheme." ] }, { "id": "task668-51f6218abedd445abad9846607a97393", "input": "Bottleneck structures with identity (e.g., residual) connection are now emerging popular paradigms for designing deep convolutional neural networks (CNN), for processing large-scale features efficiently. In this paper, we focus on the information-preserving nature of identity connection and utilize this to enable a convolutional layer to have a new functionality of channel-selectivity, i.e., re-distributing its computations to important channels. In particular, we propose Selective Convolutional Unit (SCU), a widely-applicable architectural unit that improves parameter efficiency of various modern CNNs with bottlenecks. During training, SCU gradually learns the channel-selectivity on-the-fly via the alternative usage of (a) pruning unimportant channels, and (b) rewiring the pruned parameters to important channels. The rewired parameters emphasize the target channel in a way that selectively enlarges the convolutional kernels corresponding to it. Our experimental results demonstrate that the SCU-based models without any postprocessing generally achieve both model compression and accuracy improvement compared to the baselines, consistently for all tested architectures.", "output": [ "We propose a new module that improves any ResNet-like architectures by enforcing \"channel selective\" behavior to convolutional layers" ] }, { "id": "task668-0b99e958273c404fa08e4a645464ab35", "input": "Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational complexity and memory requirements. In this paper, we relax this limitation by introducing the MultI-Level Embedding (MILE) framework – a generic methodology allowing contemporary graph embedding methods to scale to large graphs. MILE repeatedly coarsens the graph into smaller ones using a hybrid matching technique to maintain the backbone structure of the graph. It then applies existing embedding methods on the coarsest graph and refines the embeddings to the original graph through a novel graph convolution neural network that it learns. The proposed MILE framework is agnostic to the underlying graph embedding techniques and can be applied to many existing graph embedding methods without modifying them. We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs. Experimental results on five large-scale datasets demonstrate that MILE significantly boosts the speed (order of magnitude) of graph embedding while also often generating embeddings of better quality for the task of node classification. MILE can comfortably scale to a graph with 9 million nodes and 40 million edges, on which existing methods run out of memory or take too long to compute on a modern workstation.", "output": [ "A generic framework to scale existing graph embedding techniques to large graphs.", "This paper proposes a multi-level embedding framework to be applied on top of existing network embedding methods in order to scale to large scale networks with faster speed.", "The authors propose a three-stage framework for large-scale graph embedding with improved embedding quality." ] }, { "id": "task668-9e198df486a64946922d9886162cd883", "input": "It has long been assumed that high dimensional continuous control problems cannot be solved effectively by discretizing individual dimensions of the action space due to the exponentially large number of bins over which policies would have to be learned. In this paper, we draw inspiration from the recent success of sequence-to-sequence models for structured prediction problems to develop policies over discretized spaces. Central to this method is the realization that complex functions over high dimensional spaces can be modeled by neural networks that predict one dimension at a time. Specifically, we show how Q-values and policies over continuous spaces can be modeled using a next step prediction model over discretized dimensions. With this parameterization, it is possible to both leverage the compositional structure of action spaces during learning, as well as compute maxima over action spaces (approximately). On a simple example task we demonstrate empirically that our method can perform global search, which effectively gets around the local optimization issues that plague DDPG. We apply the technique to off-policy (Q-learning) methods and show that our method can achieve the state-of-the-art for off-policy methods on several continuous control tasks.", "output": [ "A method to do Q-learning on continuous action spaces by predicting a sequence of discretized 1-D actions." ] }, { "id": "task668-889e77e694e94adab5dceec9ef2ef4f4", "input": "Humans have the remarkable ability to correctly classify images despite possible degradation. Many studies have suggested that this hallmark of human vision results from the interaction between feedforward signals from bottom-up pathways of the visual cortex and feedback signals provided by top-down pathways. Motivated by such interaction, we propose a new neuro-inspired model, namely Convolutional Neural Networks with Feedback (CNN-F). CNN-F extends CNN with a feedback generative network, combining bottom-up and top-down inference to perform approximate loopy belief propagation. We show that CNN-F's iterative inference allows for disentanglement of latent variables across layers. We validate the advantages of CNN-F over the baseline CNN. Our experimental results suggest that the CNN-F is more robust to image degradation such as pixel noise, occlusion, and blur. Furthermore, we show that the CNN-F is capable of restoring original images from the degraded ones with high reconstruction accuracy while introducing negligible artifacts.", "output": [ "CNN-F extends CNN with a feedback generative network for robust vision." ] }, { "id": "task668-6603f642cc3944a494ed7be7d64be217", "input": "To which extent can successful machine learning inform our understanding of biological learning? One popular avenue of inquiry in recent years has been to directly map such algorithms into a realistic circuit implementation. Here we focus on learning in recurrent networks and investigate a range of learning algorithms. Our approach decomposes them into their computational building blocks and discusses their abstract potential as biological operations. This alternative strategy provides a “lazy” but principled way of evaluating ML ideas in terms of their biological plausibility", "output": [ "We evaluate new ML learning algorithms' biological plausibility in the abstract based on mathematical operations needed" ] }, { "id": "task668-8ee58df47ae446efb10067a379aa8846", "input": "Few-Shot Learning (learning with limited labeled data) aims to overcome the limitations of traditional machine learning approaches which require thousands of labeled examples to train an effective model. Considered as a hallmark of human intelligence, the community has recently witnessed several contributions on this topic, in particular through meta-learning, where a model learns how to learn an effective model for few-shot learning. The main idea is to acquire prior knowledge from a set of training tasks, which is then used to perform (few-shot) test tasks. Most existing work assumes that both training and test tasks are drawn from the same distribution, and a large amount of labeled data is available in the training tasks. This is a very strong assumption which restricts the usage of meta-learning strategies in the real world where ample training tasks following the same distribution as test tasks may not be available. In this paper, we propose a novel meta-learning paradigm wherein a few-shot learning model is learnt, which simultaneously overcomes domain shift between the train and test tasks via adversarial domain adaptation. We demonstrate the efficacy the proposed method through extensive experiments.", "output": [ "Meta Learning for Few Shot learning assumes that training tasks and test tasks are drawn from the same distribution. What do you do if they are not? Meta Learning with task-level Domain Adaptation!", "This paper proposes a model combining unsupervised adversarial domain adaptation with prototypical networks that performs better than few-shot learning baselines on few-shot learning tasks with domain shift.", "The authors proposed meta domain adaptation to address domain shift scenario in meta learning setup, demonstrating performance improvements in several experiments." ] }, { "id": "task668-6b7d57da87b04990a58b5bd610507d2a", "input": "This paper proposes a new actor-critic-style algorithm called Dual Actor-Critic or Dual-AC. It is derived in a principled way from the Lagrangian dual form of the Bellman optimality equation, which can be viewed as a two-player game between the actor and a critic-like function, which is named as dual critic. Compared to its actor-critic relatives, Dual-AC has the desired property that the actor and dual critic are updated cooperatively to optimize the same objective function, providing a more transparent way for learning the critic that is directly related to the objective function of the actor. We then provide a concrete algorithm that can effectively solve the minimax optimization problem, using techniques of multi-step bootstrapping, path regularization, and stochastic dual ascent algorithm. We demonstrate that the proposed algorithm achieves the state-of-the-art performances across several benchmarks.", "output": [ "We propose Dual Actor-Critic algorithm, which is derived in a principled way from the Lagrangian dual form of the Bellman optimality equation. The algorithm achieves the state-of-the-art performances across several benchmarks." ] }, { "id": "task668-b1d90368854b4153b2b8ea271c4dbab3", "input": "Modern deep neural networks (DNNs) require high memory consumption and large computational loads. In order to deploy DNN algorithms efficiently on edge or mobile devices, a series of DNN compression algorithms have been explored, including the line of works on factorization methods. Factorization methods approximate the weight matrix of a DNN layer with multiplication of two or multiple low-rank matrices. However, it is hard to measure the ranks of DNN layers during the training process. Previous works mainly induce low-rank through implicit approximations or via costly singular value decomposition (SVD) process on every training step. The former approach usually induces a high accuracy loss while the latter prevents DNN factorization from efficiently reaching a high compression rate. In this work, we propose SVD training, which first applies SVD to decompose DNN's layers and then performs training on the full-rank decomposed weights. To improve the training quality and convergence, we add orthogonality regularization to the singular vectors, which ensure the valid form of SVD and avoid gradient vanishing/exploding. Low-rank is encouraged by applying sparsity-inducing regularizers on the singular values of each layer. Singular value pruning is applied at the end to reach a low-rank model. We empirically show that SVD training can significantly reduce the rank of DNN layers and achieve higher reduction on computation load under the same accuracy, comparing to not only previous factorization methods but also state-of-the-art filter pruning methods.", "output": [ "Efficiently inducing low-rank deep neural networks via SVD training with sparse singular values and orthogonal singular vectors.", "This paper introduces an approach to network compression by encouraging the weight matrix in each layer to have a low rank and explicitly factorizing the weight matrices into an SVD-like factorization for treatment as new parameters.", "Proposal to parametrize each layer of a deep neural network, before training, with a low-rank matrix decomposition, accordingly replace convolutions with two consecutive convolutions, and then train the decomposed method." ] }, { "id": "task668-b98c8a57b6a841d3ac167dbae5095688", "input": "Magnitude-based pruning is one of the simplest methods for pruning neural networks. Despite its simplicity, magnitude-based pruning and its variants demonstrated remarkable performances for pruning modern architectures. Based on the observation that the magnitude-based pruning indeed minimizes the Frobenius distortion of a linear operator corresponding to a single layer, we develop a simple pruning method, coined lookahead pruning, by extending the single layer optimization to a multi-layer optimization. Our experimental results demonstrate that the proposed method consistently outperforms the magnitude pruning on various networks including VGG and ResNet, particularly in the high-sparsity regime.", "output": [ "We study a multi-layer generalization of the magnitude-based pruning." ] }, { "id": "task668-e57bed73725f4f23bf022ed84297f83c", "input": "Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve performance, but often suffers from task interference which reduces the benefits of transfer. To address this issue we introduce the routing network paradigm, a novel neural network and training algorithm. A routing network is a kind of self-organizing neural network consisting of two components: a router and a set of one or more function blocks. A function block may be any neural network – for example a fully-connected or a convolutional layer. Given an input the router makes a routing decision, choosing a function block to apply and passing the output back to the router recursively, terminating when a fixed recursion depth is reached. In this way the routing network dynamically composes different function blocks for each input. We employ a collaborative multi-agent reinforcement learning (MARL) approach to jointly train the router and function blocks. We evaluate our model against cross-stitch networks and shared-layer baselines on multi-task settings of the MNIST, mini-imagenet, and CIFAR-100 datasets. Our experiments demonstrate a significant improvement in accuracy, with sharper convergence. In addition, routing networks have nearly constant per-task training cost while cross-stitch networks scale linearly with the number of tasks. On CIFAR100 (20 tasks) we obtain cross-stitch performance levels with an 85% average reduction in training time.\n", "output": [ "routing networks: a new kind of neural network which learns to adaptively route its input for multi-task learning", "The paper suggests to use a modular network with a controller which makes decisions, at each time step, regarding the next nodule to apply.", "The paper presents a novel formulation for learning the optimal architecture of a neural network in a multi-task learning framework by using multi-agent reinforcement learning to find a policy, and shows improvement over hard-coded architectures with shared layers." ] }, { "id": "task668-d0493f8ca6664dd4b249d2546e6e2ae4", "input": "Deep ensembles have been empirically shown to be a promising approach for improving accuracy, uncertainty and out-of-distribution robustness of deep learning models. While deep ensembles were theoretically motivated by the bootstrap, non-bootstrap ensembles trained with just random initialization also perform well in practice, which suggests that there could be other explanations for why deep ensembles work well. Bayesian neural networks, which learn distributions over the parameters of the network, are theoretically well-motivated by Bayesian principles, but do not perform as well as deep ensembles in practice, particularly under dataset shift. One possible explanation for this gap between theory and practice is that popular scalable approximate Bayesian methods tend to focus on a single mode, whereas deep ensembles tend to explore diverse modes in function space. We investigate this hypothesis by building on recent work on understanding the loss landscape of neural networks and adding our own exploration to measure the similarity of functions in the space of predictions. Our results show that random initializations explore entirely different modes, while functions along an optimization trajectory or sampled from the subspace thereof cluster within a single mode predictions-wise, while often deviating significantly in the weight space. We demonstrate that while low-loss connectors between modes exist, they are not connected in the space of predictions. Developing the concept of the diversity--accuracy plane, we show that the decorrelation power of random initializations is unmatched by popular subspace sampling methods.", "output": [ "We study deep ensembles through the lens of loss landscape and the space of predictions, demonstrating that the decorrelation power of random initializations is unmatched by subspace sampling that only explores a single mode." ] }, { "id": "task668-c40d4355a1054f89ab9569ca7c533348", "input": "For sequence models with large word-level vocabularies, a majority of network parameters lie in the input and output layers. In this work, we describe a new method, DeFINE, for learning deep word-level representations efficiently. Our architecture uses a hierarchical structure with novel skip-connections which allows for the use of low dimensional input and output layers, reducing total parameters and training time while delivering similar or better performance versus existing methods. DeFINE can be incorporated easily in new or existing sequence models. Compared to state-of-the-art methods including adaptive input representations, this technique results in a 6% to 20% drop in perplexity. On WikiText-103, DeFINE reduces total parameters of Transformer-XL by half with minimal impact on performance. On the Penn Treebank, DeFINE improves AWD-LSTM by 4 points with a 17% reduction in parameters, achieving comparable performance to state-of-the-art methods with fewer parameters. For machine translation, DeFINE improves a Transformer model by 2% while simultaneously reducing total parameters by 26%", "output": [ "DeFINE uses a deep, hierarchical, sparse network with new skip connections to learn better word embeddings efficiently. ", "This paper describes a new method for learning deep word-level representations efficiently by using a hierarchical structure with skip-connections for the use of low dimensional input and output layers." ] }, { "id": "task668-4fc499aabcb14c21b4a0e3293ac36b84", "input": "In learning to rank, one is interested in optimising the global ordering of a list of items according to their utility for users. Popular approaches learn a scoring function that scores items individually (i.e. without the context of other items in the list) by optimising a pointwise, pairwise or listwise loss. The list is then sorted in the descending order of the scores. Possible interactions between items present in the same list are taken into account in the training phase at the loss level. However, during inference, items are scored individually, and possible interactions between them are not considered. In this paper, we propose a context-aware neural network model that learns item scores by applying a self-attention mechanism. The relevance of a given item is thus determined in the context of all other items present in the list, both in training and in inference. Finally, we empirically demonstrate significant performance gains of self-attention based neural architecture over Multi-Layer Perceptron baselines. This effect is consistent across popular pointwise, pairwise and listwise losses on datasets with both implicit and explicit relevance feedback.", "output": [ "Learning to rank using the Transformer architecture." ] }, { "id": "task668-9c68f674db9f430d8d56dd6a603046fe", "input": "Nowadays deep learning is one of the main topics in almost every field. It helped to get amazing results in a great number of tasks. The main problem is that this kind of learning and consequently neural networks, that can be defined deep, are resource intensive. They need specialized hardware to perform a computation in a reasonable time. Unfortunately, it is not sufficient to make deep learning \"usable\" in real life. Many tasks are mandatory to be as much as possible real-time. So it is needed to optimize many components such as code, algorithms, numeric accuracy and hardware, to make them \"efficient and usable\". All these optimizations can help us to produce incredibly accurate and fast learning models.", "output": [ "Embedded architecture for deep learning on optimized devices for face detection and emotion recognition " ] }, { "id": "task668-0475727fb97548d9b431120b2b84772a", "input": "\tDespite a growing literature on explaining neural networks, no consensus has been reached on how to explain a neural network decision or how to evaluate an explanation.\n\t Our contributions in this paper are twofold. First, we investigate schemes to combine explanation methods and reduce model uncertainty to obtain a single aggregated explanation. The aggregation is more robust and aligns better with the neural network than any single explanation method..\n\t Second, we propose a new approach to evaluating explanation methods that circumvents the need for manual evaluation and is not reliant on the alignment of neural networks and humans decision processes.", "output": [ "We show in theory and in practice that combining multiple explanation methods for DNN benefits the explanation." ] }, { "id": "task668-09650dcdd9c94ab8825dce890c2402ad", "input": "Memorization of data in deep neural networks has become a subject of significant research interest. \n In this paper, we link memorization of images in deep convolutional autoencoders to downsampling through strided convolution. To analyze this mechanism in a simpler setting, we train linear convolutional autoencoders and show that linear combinations of training data are stored as eigenvectors in the linear operator corresponding to the network when downsampling is used. On the other hand, networks without downsampling do not memorize training data. We provide further evidence that the same effect happens in nonlinear networks. Moreover, downsampling in nonlinear networks causes the model to not only memorize just linear combinations of images, but individual training images. Since convolutional autoencoder components are building blocks of deep convolutional networks, we envision that our findings will shed light on the important phenomenon of memorization in over-parameterized deep networks. \n", "output": [ "We identify downsampling as a mechansim for memorization in convolutional autoencoders." ] }, { "id": "task668-f32d0e2b42ec450c83df675d681bbf62", "input": "While it is well-documented that climate change accepters and deniers have become increasingly polarized in the United States over time, there has been no large-scale examination of whether these individuals are prone to changing their opinions as a result of natural external occurrences. On the sub-population of Twitter users, we examine whether climate change sentiment changes in response to five separate natural disasters occurring in the U.S. in 2018. We begin by showing that tweets can be classified with over 75% accuracy as either accepting or denying climate change when using our methodology to compensate for limited labelled data; results are robust across several machine learning models and yield geographic-level results in line with prior research. We then apply RNNs to conduct a cohort-level analysis showing that the 2018 hurricanes yielded a statistically significant increase in average tweet sentiment affirming climate change. However, this effect does not hold for the 2018 blizzard and wildfires studied, implying that Twitter users' opinions on climate change are fairly ingrained on this subset of natural disasters.", "output": [ "We train RNNs on famous Twitter users to determine whether the general Twitter population is more likely to believe in climate change after a natural disaster." ] }, { "id": "task668-7b5d7faf571e402799b0b9af25ebc5fe", "input": "With the recently rapid development in deep learning, deep neural networks have been widely adopted in many real-life applications. However, deep neural networks are also known to have very little control over its uncertainty for test examples, which potentially causes very harmful and annoying consequences in practical scenarios. In this paper, we are particularly interested in designing a higher-order uncertainty metric for deep neural networks and investigate its performance on the out-of-distribution detection task proposed by~\\cite{hendrycks2016baseline}. Our method first assumes there exists a underlying higher-order distribution $\\mathcal{P}(z)$ , which generated label-wise distribution $\\mathcal{P}(y)$ over classes on the K-dimension simplex, and then approximate such higher-order distribution via parameterized posterior function $p_{\\theta}(z|x)$ under variational inference framework, finally we use the entropy of learned posterior distribution $p_{\\theta}(z|x)$ as uncertainty measure to detect out-of-distribution examples. However , we identify the overwhelming over-concentration issue in such a framework, which greatly hinders the detection performance. Therefore , we further design a log-smoothing function to alleviate such issue to greatly increase the robustness of the proposed entropy-based uncertainty measure. Through comprehensive experiments on various datasets and architectures, our proposed variational Dirichlet framework with entropy-based uncertainty measure is consistently observed to yield significant improvements over many baseline systems.", "output": [ "A new framework based variational inference for out-of-distribution detection", "Describes a probabilistic approach to quantifying uncertainty in DNN classification tasks that outperforms other SOTA methods in the task of out-of-distribution detection.", "A new framework for out-of-distribution detection, based on variaitonal inference and a prior Dirichlet distribution, that reports state of the art results on several datasets.", "An out-of distribution detection via a new method to approximate the confidence distribution of classification probability using variational inference of Dirichlet distribution." ] }, { "id": "task668-5e53d614e5cd46e3a99edb2d5b32c2a8", "input": "Neural networks (NNs) are able to perform tasks that rely on compositional structure even though they lack obvious mechanisms for representing this structure. To analyze the internal representations that enable such success, we propose ROLE, a technique that detects whether these representations implicitly encode symbolic structure. ROLE learns to approximate the representations of a target encoder E by learning a symbolic constituent structure and an embedding of that structure into E’s representational vector space. The constituents of the approximating symbol structure are defined by structural positions — roles — that can be filled by symbols. We show that when E is constructed to explicitly embed a particular type of structure (e.g., string or tree), ROLE successfully extracts the ground-truth roles defining that structure. We then analyze a seq2seq network trained to perform a more complex compositional task (SCAN), where there is no ground truth role scheme available. For this model, ROLE successfully discovers an interpretable symbolic structure that the model implicitly uses to perform the SCAN task, providing a comprehensive account of the link between the representations and the behavior of a notoriously hard-to-interpret type of model. We verify the causal importance of the discovered symbolic structure by showing that, when we systematically manipulate hidden embeddings based on this symbolic structure, the model’s output is also changed in the way predicted by our analysis. Finally, we use ROLE to explore whether popular sentence embedding models are capturing compositional structure and find evidence that they are not; we conclude by discussing how insights from ROLE can be used to impart new inductive biases that will improve the compositional abilities of such models.", "output": [ "We introduce a new analysis technique that discovers interpretable compositional structure in notoriously hard-to-interpret recurrent neural networks." ] }, { "id": "task668-25dfea85bd5545b08f960f4638cc5274", "input": "A central capability of intelligent systems is the ability to continuously build upon previous experiences to speed up and enhance learning of new tasks. Two distinct research paradigms have studied this question. Meta-learning views this problem as learning a prior over model parameters that is amenable for fast adaptation on a new task, but typically assumes the set of tasks are available together as a batch. In contrast, online (regret based) learning considers a sequential setting in which problems are revealed one after the other, but conventionally train only a single model without any task-specific adaptation. This work introduces an online meta-learning setting, which merges ideas from both the aforementioned paradigms to better capture the spirit and practice of continual lifelong learning. We propose the follow the meta leader (FTML) algorithm which extends the MAML algorithm to this setting. Theoretically, this work provides an O(logT) regret guarantee for the FTML algorithm. Our experimental evaluation on three different large-scale tasks suggest that the proposed algorithm significantly outperforms alternatives based on traditional online learning approaches.", "output": [ "We introduce the online meta learning problem setting to better capture the spirit and practice of continual lifelong learning." ] }, { "id": "task668-35770e31554d4f0a9766512773d5d706", "input": "Recent advances in Generative Adversarial Networks facilitated by improvements to the framework and successful application to various problems has resulted in extensions to multiple domains. IRGAN attempts to leverage the framework for Information-Retrieval (IR), a task that can be described as modeling the correct conditional probability distribution p(d|q) over the documents (d), given the query (q). The work that proposes IRGAN claims that optimizing their minimax loss function will result in a generator which can learn the distribution, but their setup and baseline term steer the model away from an exact adversarial formulation, and this work attempts to point out certain inaccuracies in their formulation. Analyzing their loss curves gives insight into possible mistakes in the loss functions and better performance can be obtained by using the co-training like setup we propose, where two models are trained in a co-operative rather than an adversarial fashion.", "output": [ "Points out problems in loss function used in IRGAN, a recently proposed GAN framework for Information Retrieval. Further, a model motivated by co-training is proposed, which achieves better performance." ] }, { "id": "task668-27279fd66b7d473f832b0e0cd08fd079", "input": "A major challenge in learning image representations is the disentangling of the factors of variation underlying the image formation. This is typically achieved with an autoencoder architecture where a subset of the latent variables is constrained to correspond to specific factors, and the rest of them are considered nuisance variables. This approach has an important drawback: as the dimension of the nuisance variables is increased, image reconstruction is improved, but the decoder has the flexibility to ignore the specified factors, thus losing the ability to condition the output on them. In this work, we propose to overcome this trade-off by progressively growing the dimension of the latent code, while constraining the Jacobian of the output image with respect to the disentangled variables to remain the same. As a result, the obtained models are effective at both disentangling and reconstruction. We demonstrate the applicability of this method in both unsupervised and supervised scenarios for learning disentangled representations. In a facial attribute manipulation task, we obtain high quality image generation while smoothly controlling dozens of attributes with a single model. This is an order of magnitude more disentangled factors than state-of-the-art methods, while obtaining visually similar or superior results, and avoiding adversarial training.", "output": [ "A method for learning image representations that are good for both disentangling factors of variation and obtaining faithful reconstructions." ] }, { "id": "task668-41e210882d9049deafe4d649bfb545be", "input": "Neural network training relies on our ability to find ````````\"good\" minimizers of highly non-convex loss functions. It is well known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and well-chosen training parameters (batch size, learning rate, optimizer) produce minimizers that generalize better. However, the reasons for these differences, and their effect on the underlying loss landscape, is not well understood.\n\n In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple ``\"filter normalization\" method that helps us visualize loss function curvature, and make meaningful side-by-side comparisons between loss functions. Then, using a variety of visualizations, we explore how network architecture effects the loss landscape, and how training parameters affect the shape of minimizers.", "output": [ "We explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods.", "This paper proposes a method to visualize the loss function of a NN and provides insights on the trainability and generalization of NNs.", "Investigates the non-convexity of the loss surface and optimization paths." ] }, { "id": "task668-72159fc857374fbcafcf5859c78a6262", "input": "Words in natural language follow a Zipfian distribution whereby some words are frequent but most are rare. Learning representations for words in the ``long tail'' of this distribution requires enormous amounts of data. \n Representations of rare words trained directly on end tasks are usually poor, requiring us to pre-train embeddings on external data, or treat all rare words as out-of-vocabulary words with a unique representation. We provide a method for predicting embeddings of rare words on the fly from small amounts of auxiliary data with a network trained end-to-end for the downstream task. We show that this improves results against baselines where embeddings are trained on the end task for reading comprehension, recognizing textual entailment and language modeling.\n", "output": [ "We propose a method to deal with rare words by computing their embedding from definitions." ] }, { "id": "task668-28c56f916a844cd4bb7be1e96f4c3503", "input": "Conversational machine comprehension requires a deep understanding of the conversation history. To enable traditional, single-turn models to encode the history comprehensively, we introduce Flow, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, through an alternating parallel processing structure. Compared to shallow approaches that concatenate previous questions/answers as input, Flow integrates the latent semantics of the conversation history more deeply. Our model, FlowQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The effectiveness of Flow also shows in other tasks. By reducing sequential instruction understanding to conversational machine comprehension, FlowQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.", "output": [ "We propose the Flow mechanism and an end-to-end architecture, FlowQA, that achieves SotA on two conversational QA datasets and a sequential instruction understanding task." ] }, { "id": "task668-c8ca0974b58d441dbde2974b50d8a657", "input": "We introduce geomstats, a Python package for Riemannian modelization and optimization over manifolds such as hyperspheres, hyperbolic spaces, SPD matrices or Lie groups of transformations. Our contribution is threefold. First, geomstats allows the flexible modeling of many a machine learning problem through an efficient and extensively unit-tested implementations of these manifolds, as well as the set of useful Riemannian metrics, exponential and logarithm maps that we provide. Moreover, the wide choice of loss functions and our implementation of the corresponding gradients allow fast and easy optimization over manifolds. Finally, geomstats is the only package to provide a unified framework for Riemannian geometry, as the operations implemented in geomstats are available with different computing backends (numpy,tensorflow and keras), as well as with a GPU-enabled mode–-thus considerably facilitating the application of Riemannian geometry in machine learning. In this paper, we present geomstats through a review of the utility and advantages of manifolds in machine learning, using the concrete examples that they span to show the efficiency and practicality of their implementation using our package", "output": [ "We introduce geomstats, an efficient Python package for Riemannian modelization and optimization over manifolds compatible with both numpy and tensorflow .", "The paper introduces the software package geomstats, which provides simple use of Riemannian manifolds and metrics within machine learning models", "Proposes a Python package for optimization and applications on Reimannian manifolds and highlights the differences between Geomstats package and other packages.", "Introduces a geometric toolbox, Geomstats, for machine learning on Reimannian manifolds." ] }, { "id": "task668-2eb80c37cd7946cea22bc3aa4c3fe175", "input": "Training neural networks with verifiable robustness guarantees is challenging. Several existing approaches utilize linear relaxation based neural network output bounds under perturbation, but they can slow down training by a factor of hundreds depending on the underlying network architectures. Meanwhile, interval bound propagation (IBP) based training is efficient and significantly outperforms linear relaxation based methods on many tasks, yet it may suffer from stability issues since the bounds are much looser especially at the beginning of training. In this paper, we propose a new certified adversarial training method, CROWN-IBP, by combining the fast IBP bounds in a forward bounding pass and a tight linear relaxation based bound, CROWN, in a backward bounding pass. CROWN-IBP is computationally efficient and consistently outperforms IBP baselines on training verifiably robust neural networks. We conduct large scale experiments on MNIST and CIFAR datasets, and outperform all previous linear relaxation and bound propagation based certified defenses in L_inf robustness.\n Notably, we achieve 7.02% verified test error on MNIST at epsilon=0.3, and 66.94% on CIFAR-10 with epsilon=8/255.", "output": [ "We propose a new certified adversarial training method, CROWN-IBP, that achieves state-of-the-art robustness for L_inf norm adversarial perturbations." ] }, { "id": "task668-b646e35c8d3c4eabb89ca4335a272517", "input": "We present a hybrid framework that leverages the trade-off between temporal and frequency precision in audio representations to improve the performance of speech enhancement task. We first show that conventional approaches using specific representations such as raw-audio and spectrograms are each effective at targeting different types of noise.\n By integrating both approaches, our model can learn multi-scale and multi-domain features, effectively removing noise existing on different regions on the time-frequency space in a complementary way. Experimental results show that the proposed hybrid model yields better performance and robustness than using each model individually.", "output": [ "A hybrid model utilizing both raw-audio and spectrogram information for speech enhancement tasks." ] }, { "id": "task668-b939327944db4f3b9808f64a5e623c87", "input": "Normalizing constant (also called partition function, Bayesian evidence, or marginal likelihood) is one of the central goals of Bayesian inference, yet most of the existing methods are both expensive and inaccurate. Here we develop a new approach, starting from posterior samples obtained with a standard Markov Chain Monte Carlo (MCMC). We apply a novel Normalizing Flow (NF) approach to obtain an analytic density estimator from these samples, followed by Optimal Bridge Sampling (OBS) to obtain the normalizing constant. We compare our method which we call Gaussianized Bridge Sampling (GBS) to existing methods such as Nested Sampling (NS) and Annealed Importance Sampling (AIS) on several examples, showing our method is both significantly faster and substantially more accurate than these methods, and comes with a reliable error estimation.", "output": [ "We develop a new method for normalization constant (Bayesian evidence) estimation using Optimal Bridge Sampling and a novel Normalizing Flow, which is shown to outperform existing methods in terms of accuracy and computational time." ] }, { "id": "task668-2faef134156242da8a71954d7a43b269", "input": "An adversarial feature learning (AFL) is a powerful framework to learn representations invariant to a nuisance attribute, which uses an adversarial game between a feature extractor and a categorical attribute classifier. It theoretically sounds in term of it maximize conditional entropy between attribute and representation. However, as shown in this paper, the AFL often causes unstable behavior that slows down the convergence. We propose an {\\em attribute perception matching} as an alternative approach, based on the reformulation of conditional entropy maximization as {\\em pair-wise distribution matching}. Although the naive approach for realizing the pair-wise distribution matching requires the significantly large number of parameters, the proposed method requires the same number of parameters with AFL but has a better convergence property. Experiments on both toy and real-world dataset prove that our proposed method converges to better invariant representation significantly faster than AFL. ", "output": [ "This paper proposes a new approach to incorporating desired invariance to representations learning, based on the observations that the current state-of-the-art AFL has practical issues." ] }, { "id": "task668-48f7289b09064ceea4fd1f9d0557c2ad", "input": "Generative networks are known to be difficult to assess. Recent works on generative models, especially on generative adversarial networks, produce nice samples of varied categories of images. But the validation of their quality is highly dependent on the method used. A good generator should generate data which contain meaningful and varied information and that fit the distribution of a dataset. This paper presents a new method to assess a generator. Our approach is based on training a classifier with a mixture of real and generated samples. We train a generative model over a labeled training set, then we use this generative model to sample new data points that we mix with the original training data. This mixture of real and generated data is thus used to train a classifier which is afterwards tested on a given labeled test dataset. We compare this result with the score of the same classifier trained on the real training data mixed with noise. By computing the classifier's accuracy with different ratios of samples from both distributions (real and generated) we are able to estimate if the generator successfully fits and is able to generalize the distribution of the dataset. Our experiments compare the result of different generators from the VAE and GAN framework on MNIST and fashion MNIST dataset.", "output": [ "Evaluating generative networks through their data augmentation capacity on discrimative models." ] }, { "id": "task668-76468b2700784fc6999b8ca21c1ba700", "input": "Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. However, many recent meta-learning approaches are extensively hand-designed, either using architectures specialized to a particular application, or hard-coding algorithmic components that constrain how the meta-learner solves the task. We propose a class of simple and generic meta-learner architectures that use a novel combination of temporal convolutions and soft attention; the former to aggregate information from past experience and the latter to pinpoint specific pieces of information. In the most extensive set of meta-learning experiments to date, we evaluate the resulting Simple Neural AttentIve Learner (or SNAIL) on several heavily-benchmarked tasks. On all tasks, in both supervised and reinforcement learning, SNAIL attains state-of-the-art performance by significant margins.", "output": [ "a simple RNN-based meta-learner that achieves SOTA performance on popular benchmarks" ] }, { "id": "task668-6872d18931fd4f15bd73b6e2849d6aad", "input": "Previous work (Bowman et al., 2015; Yang et al., 2017) has found difficulty developing generative models based on variational autoencoders (VAEs) for text. To address the problem of the decoder ignoring information from the encoder (posterior collapse), these previous models weaken the capacity of the decoder to force the model to use information from latent variables. However, this strategy is not ideal as it degrades the quality of generated text and increases hyper-parameters. In this paper, we propose a new VAE for text utilizing a multimodal prior distribution, a modified encoder, and multi-task learning. We show our model can generate well-conditioned sentences without weakening the capacity of the decoder. Also, the multimodal prior distribution improves the interpretability of acquired representations.", "output": [ "We propose a model of variational autoencoders for text modeling without weakening the decoder, which improves the quality of text generation and interpretability of acquired representations." ] }, { "id": "task668-19357c48218f4045abd53515a862fe75", "input": " We introduce a model that learns to convert simple hand drawings\n into graphics programs written in a subset of \\LaTeX.~ The model\n combines techniques from deep learning and program synthesis. We\n learn a convolutional neural network that proposes plausible drawing\n primitives that explain an image. These drawing primitives are like\n a trace of the set of primitive commands issued by a graphics\n program. We learn a model that uses program synthesis techniques to\n recover a graphics program from that trace. These programs have\n constructs like variable bindings, iterative loops, or simple kinds\n of conditionals. With a graphics program in hand, we can correct\n errors made by the deep network and extrapolate drawings. Taken\n together these results are a step towards agents that induce useful,\n human-readable programs from perceptual input.", "output": [ "Learn to convert a hand drawn sketch into a high-level program" ] }, { "id": "task668-99270f072b92471b965984128c02814e", "input": "Many recently trained neural networks employ large numbers of parameters to achieve good performance. One may intuitively use the number of parameters required as a rough gauge of the difficulty of a problem. But how accurate are such notions? How many parameters are really needed? In this paper we attempt to answer this question by training networks not in their native parameter space, but instead in a smaller, randomly oriented subspace. We slowly increase the dimension of this subspace, note at which dimension solutions first appear, and define this to be the intrinsic dimension of the objective landscape. The approach is simple to implement, computationally tractable, and produces several suggestive conclusions. Many problems have smaller intrinsic dimensions than one might suspect, and the intrinsic dimension for a given dataset varies little across a family of models with vastly different sizes. This latter result has the profound implication that once a parameter space is large enough to solve a problem, extra parameters serve directly to increase the dimensionality of the solution manifold. Intrinsic dimension allows some quantitative comparison of problem difficulty across supervised, reinforcement, and other types of learning where we conclude, for example, that solving the inverted pendulum problem is 100 times easier than classifying digits from MNIST, and playing Atari Pong from pixels is about as hard as classifying CIFAR-10. In addition to providing new cartography of the objective landscapes wandered by parameterized models, the method is a simple technique for constructively obtaining an upper bound on the minimum description length of a solution. A byproduct of this construction is a simple approach for compressing networks, in some cases by more than 100 times.", "output": [ "We train in random subspaces of parameter space to measure how many dimensions are really needed to find a solution." ] }, { "id": "task668-617f5e63d8694656afbf12181297a66d", "input": "In recent years, deep neural networks have demonstrated outstanding performancein many machine learning tasks. However, researchers have discovered that thesestate-of-the-art models are vulnerable to adversarial examples: legitimate examples added by small perturbations which are unnoticeable to human eyes. Adversarial training, which augments the training data with adversarial examples duringthe training process, is a well known defense to improve the robustness of themodel against adversarial attacks. However, this robustness is only effective tothe same attack method used for adversarial training. Madry et al. (2017) suggest that effectiveness of iterative multi-step adversarial attacks and particularlythat projected gradient descent (PGD) may be considered the universal first order adversary and applying the adversarial training with PGD implies resistanceagainst many other first order attacks. However, the computational cost of theadversarial training with PGD and other multi-step adversarial examples is muchhigher than that of the adversarial training with other simpler attack techniques. In this paper, we show how strong adversarial examples can be generated only ata cost similar to that of two runs of the fast gradient sign method (FGSM), allowing defense against adversarial attacks with a robustness level comparable to thatof the adversarial training with multi-step adversarial examples. We empiricallydemonstrate the effectiveness of the proposed two-step defense approach againstdifferent attack methods and its improvements over existing defense strategies.", "output": [ "We proposed a time-efficient defense method against one-step and iterative adversarial attacks.", "Propose a novel, computationaly efficient method named e2SAD which generates sets of two training adversarial samples for each clean training sample.", "The paper introduces a two-step adversarial defense method, to generate two adversarial examples per clean sample and include them in the actual training loop to achieve robustness and claiming it can outperform more expensive iterative methods.", "The paper presents a 2-step approach to generate strong adversarial examples at a far lesser cost as compared to recent iterative multi-step adversarial attacks." ] }, { "id": "task668-52ff0512f8d04488b72f43fc25f083db", "input": "With the rapidly scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for efficient deployment. This work aims to advance the compression beyond the weights to the activations of DNNs. We propose the Integral Pruning (IP) technique which integrates the activation pruning with the weight pruning. Through the learning on the different importance of neuron responses and connections, the generated network, namely IPnet, balances the sparsity between activations and weights and therefore further improves execution efficiency. The feasibility and effectiveness of IPnet are thoroughly evaluated through various network models with different activation functions and on different datasets. With <0.5% disturbance on the testing accuracy, IPnet saves 71.1% ~ 96.35% of computation cost, compared to the original dense models with up to 5.8x and 10x reductions in activation and weight numbers, respectively.", "output": [ "This work advances DNN compression beyond the weights to the activations by integrating the activation pruning with the weight pruning. ", "An integral model compression method that handles both weight and activation pruning, leading to more efficient network computation and effective reduction of the number of multiply-and-accumulate.", "This article presents a novel approach to reduce the computation cost of deep neural networks by integrating activation pruning along with weight pruning and show that common techniques of exclusive weight pruning increases the number of non-zero activations after ReLU." ] }, { "id": "task668-b4ca4314cc9b42e68b2ce4ff1110b0b1", "input": "Automatic melody generation for pop music has been a long-time aspiration for\n both AI researchers and musicians. However, learning to generate euphonious\n melody has turned out to be highly challenging due to a number of factors. Representation\n of multivariate property of notes has been one of the primary challenges.\n It is also difficult to remain in the permissible spectrum of musical variety, outside\n of which would be perceived as a plain random play without auditory pleasantness.\n Observing the conventional structure of pop music poses further challenges.\n In this paper, we propose to represent each note and its properties as a unique\n ‘word,’ thus lessening the prospect of misalignments between the properties, as\n well as reducing the complexity of learning. We also enforce regularization policies\n on the range of notes, thus encouraging the generated melody to stay close\n to what humans would find easy to follow. Furthermore, we generate melody\n conditioned on song part information, thus replicating the overall structure of a\n full song. Experimental results demonstrate that our model can generate auditorily\n pleasant songs that are more indistinguishable from human-written ones than\n previous models.", "output": [ "We propose a novel model to represent notes and their properties, which can enhance the automatic melody generation.", "This paper proposes a generative model of symbolic (MIDI) melody in western popular music which jointly encodes note symbols with timing and duration information to form musical \"words\".", "The paper proposes to facilitate generation of melody by representing notes as \"words\", representing all of the note's properties and thus allowing the generation of musical \"sentences\"." ] }, { "id": "task668-9888e6207a204f23bdd6484b3da097b4", "input": "Pruning units in a deep network can help speed up inference and training as well as reduce the size of the model. We show that bias propagation is a pruning technique which consistently outperforms the common approach of merely removing units, regardless of the architecture and the dataset. We also show how a simple adaptation to an existing scoring function allows us to select the best units to prune. Finally, we show that the units selected by the best performing scoring functions are somewhat consistent over the course of training, implying the dead parts of the network appear during the stages of training.", "output": [ "Mean Replacement is an efficient method to improve the loss after pruning and Taylor approximation based scoring functions works better with absolute values. ", "Proposes a simple improvement to methods for unit pruning using \"mean replacement\"", "This paper presents a mean-replacement pruning strategy and utilizes the absolute-valued Taylor expansion as the scoring function for the pruning" ] }, { "id": "task668-bc4c796b4c194f6ea200d481e1471763", "input": "In colored graphs, node classes are often associated with either their neighbors class or with information not incorporated in the graph associated with each node. We here propose that node classes are also associated with topological features of the nodes. We use this association to improve Graph machine learning in general and specifically, Graph Convolutional Networks (GCN). \n\n First, we show that even in the absence of any external information on nodes, a good accuracy can be obtained on the prediction of the node class using either topological features, or using the neighbors class as an input to a GCN. This accuracy is slightly less than the one that can be obtained using content based GCN.\n\n Secondly, we show that explicitly adding the topology as an input to the GCN does not improve the accuracy when combined with external information on nodes. However, adding an additional adjacency matrix with edges between distant nodes with similar topology to the GCN does significantly improve its accuracy, leading to results better than all state of the art methods in multiple datasets.", "output": [ "Topology-Based Graph Convolutional Network (GCN)" ] }, { "id": "task668-8c41379105744364bade771acb0b4fad", "input": "Residual networks (Resnets) have become a prominent architecture in deep learning. However, a comprehensive understanding of Resnets is still a topic of ongoing research. A recent view argues that Resnets perform iterative refinement of features. We attempt to further expose properties of this aspect. To this end, we study Resnets both analytically and empirically. We formalize the notion of iterative refinement in Resnets by showing that residual architectures naturally encourage features to move along the negative gradient of loss during the feedforward phase. In addition, our empirical analysis suggests that Resnets are able to perform both representation learning and iterative refinement. In general, a Resnet block tends to concentrate representation learning behavior in the first few layers while higher layers perform iterative refinement of features. Finally we observe that sharing residual layers naively leads to representation explosion and hurts generalization performance, and show that simple existing strategies can help alleviating this problem.", "output": [ "Residual connections really perform iterative inference" ] }, { "id": "task668-6885898743494187a15cdffaef4e60d3", "input": "Humans possess an ability to abstractly reason about objects and their interactions, an ability not shared with state-of-the-art deep learning models. Relational networks, introduced by Santoro et al. (2017), add the capacity for relational reasoning to deep neural networks, but are limited in the complexity of the reasoning tasks they can address. We introduce recurrent relational networks which increase the suite of solvable tasks to those that require an order of magnitude more steps of relational reasoning. We use recurrent relational networks to solve Sudoku puzzles and achieve state-of-the-art results by solving 96.6% of the hardest Sudoku puzzles, where relational networks fail to solve any. We also apply our model to the BaBi textual QA dataset solving 19/20 tasks which is competitive with state-of-the-art sparse differentiable neural computers. The recurrent relational network is a general purpose module that can augment any neural network model with the capacity to do many-step relational reasoning.", "output": [ "We introduce Recurrent Relational Networks, a powerful and general neural network module for relational reasoning, and use it to solve 96.6% of the hardest Sudokus and 19/20 BaBi tasks.", "Introduced recurrent relational network (RRNs) that can be added to any neural networks to add relational reasoning capacity.", "Introduction of a deep neural network for structured prediction that achieves state-of-the-art performance on Soduku puzzles and the BaBi task.", "This paper describes a method called relational network to add relational reasoning capacity to deep neural networks." ] }, { "id": "task668-67426984d73342b6885487f849e8b463", "input": "Identifying analogies across domains without supervision is a key task for artificial intelligence. Recent advances in cross domain image mapping have concentrated on translating images across domains. Although the progress made is impressive, the visual fidelity many times does not suffice for identifying the matching sample from the other domain. In this paper, we tackle this very task of finding exact analogies between datasets i.e. for every image from domain A find an analogous image in domain B. We present a matching-by-synthesis approach: AN-GAN, and show that it outperforms current techniques. We further show that the cross-domain mapping task can be broken into two parts: domain alignment and learning the mapping function. The tasks can be iteratively solved, and as the alignment is improved, the unsupervised translation function reaches quality comparable to full supervision.", "output": [ "Finding correspondences between domains by performing matching/mapping iterations" ] }, { "id": "task668-9b52197b1c9646aeb3763fd9ab73ae79", "input": "Self-normalizing discriminative models approximate the normalized probability of a class without having to compute the partition function. This property is useful to computationally-intensive neural network classifiers, as the cost of computing the partition function grows linearly with the number of classes and may become prohibitive. In particular, since neural language models may deal with up to millions of classes, their self-normalization properties received notable attention. Several\n recent studies empirically found that language models, trained using Noise Contrastive Estimation (NCE), exhibit self-normalization, but could not explain why. In this study, we provide a theoretical justification to this property by viewing\n NCE as a low-rank matrix approximation. Our empirical investigation compares NCE to the alternative explicit approach for self-normalizing language models. It also uncovers a surprising negative correlation between self-normalization and\n perplexity, as well as some regularity in the observed errors that may potentially be used for improving self-normalization algorithms in the future.", "output": [ "We prove that NCE is self-normalized and demonstrate it on datasets", "Presents a proof of the self normalization of NCE as a result of being a low-rank matrix approximation of low-rank approximation of the normalized conditional probabilities matrix.", "This paper considers the problem of self-normalizing models and explains the self-normalizing mechanism by interpreting NCE in terms of matrix factorization." ] }, { "id": "task668-c6fcea9a69174666829401c974d3be8e", "input": "Knowledge-grounded dialogue is a task of generating an informative response based on both discourse context and external knowledge. As we focus on better modeling the knowledge selection in the multi-turn knowledge-grounded dialogue, we propose a sequential latent variable model as the first approach to this matter. The model named sequential knowledge transformer (SKT) can keep track of the prior and posterior distribution over knowledge; as a result, it can not only reduce the ambiguity caused from the diversity in knowledge selection of conversation but also better leverage the response information for proper choice of knowledge. Our experimental results show that the proposed model improves the knowledge selection accuracy and subsequently the performance of utterance generation. We achieve the new state-of-the-art performance on Wizard of Wikipedia (Dinan et al., 2019) as one of the most large-scale and challenging benchmarks. We further validate the effectiveness of our model over existing conversation methods in another knowledge-based dialogue Holl-E dataset (Moghe et al., 2018).", "output": [ "Our approach is the first attempt to leverage a sequential latent variable model for knowledge selection in the multi-turn knowledge-grounded dialogue. It achieves the new state-of-the-art performance on Wizard of Wikipedia benchmark.", "A sequential latent variable model for knowledge selection in dialogue generation that extends the posterior attention model to the latent knowledge selection problem and achieves higher performances than previous state-of-the-art models.", "A novel architecture for selecting knowledge-grounded multi-turn dialogue that yields state of the art on relevant benchmarks datasets, and scores higher in human evaluations." ] }, { "id": "task668-4dffee4b481547eeab041a1a0e6c3a2c", "input": "Imitation learning from demonstrations usually relies on learning a policy from trajectories of optimal states and actions. However, in real life expert demonstrations, often the action information is missing and only state trajectories are available. We present a model-based imitation learning method that can learn environment-specific optimal actions only from expert state trajectories. Our proposed method starts with a model-free reinforcement learning algorithm with a heuristic reward signal to sample environment dynamics, which is then used to train the state-transition probability. Subsequently, we learn the optimal actions from expert state trajectories by supervised learning, while back-propagating the error gradients through the modeled environment dynamics. Experimental evaluations show that our proposed method successfully achieves performance similar to (state, action) trajectory-based traditional imitation learning methods even in the absence of action information, with much fewer iterations compared to conventional model-free reinforcement learning methods. We also demonstrate that our method can learn to act from only video demonstrations of expert agent for simple games and can learn to achieve desired performance in less number of iterations.", "output": [ "Learning to imitate an expert in the absence of optimal actions learning a dynamics model while exploring the environment." ] }, { "id": "task668-077da5b2903644d5ab2a23e5e5ebe459", "input": "We introduce \"Search with Amortized Value Estimates\" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS). In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values. The new Q-estimates are then used in combination with real experience to update the prior. This effectively amortizes the value computation performed by MCTS, resulting in a cooperative relationship between model-free learning and model-based search. SAVE can be implemented on top of any Q-learning agent with access to a model, which we demonstrate by incorporating it into agents that perform challenging physical reasoning tasks and Atari. SAVE consistently achieves higher rewards with fewer training steps, and---in contrast to typical model-based search approaches---yields strong performance with very small search budgets. By combining real experience with information computed during search, SAVE demonstrates that it is possible to improve on both the performance of model-free learning and the computational cost of planning.", "output": [ "We propose a model-based method called \"Search with Amortized Value Estimates\" (SAVE) which leverages both real and planned experience by combining Q-learning with Monte-Carlo Tree Search, achieving strong performance with very small search budgets." ] }, { "id": "task668-47b00315409b456bbb6630faf41f0790", "input": "Our work offers a new method for domain translation from semantic label maps\n and Computer Graphic (CG) simulation edge map images to photo-realistic im-\n ages. We train a Generative Adversarial Network (GAN) in a conditional way to\n generate a photo-realistic version of a given CG scene. Existing architectures of\n GANs still lack the photo-realism capabilities needed to train DNNs for computer\n vision tasks, we address this issue by embedding edge maps, and training it in an\n adversarial mode. We also offer an extension to our model that uses our GAN\n architecture to create visually appealing and temporally coherent videos.", "output": [ "Simulation to real images translation and video generation" ] }, { "id": "task668-78c6e89bf2e240c6b24764a38ce70788", "input": "Named entity recognition (NER) and relation extraction (RE) are two important tasks in information extraction and retrieval (IE & IR). Recent work has demonstrated that it is beneficial to learn these tasks jointly, which avoids the propagation of error inherent in pipeline-based systems and improves performance. However, state-of-the-art joint models typically rely on external natural language processing (NLP) tools, such as dependency parsers, limiting their usefulness to domains (e.g. news) where those tools perform well. The few neural, end-to-end models that have been proposed are trained almost completely from scratch. In this paper, we propose a neural, end-to-end model for jointly extracting entities and their relations which does not rely on external NLP tools and which integrates a large, pre-trained language model. Because the bulk of our model's parameters are pre-trained and we eschew recurrence for self-attention, our model is fast to train. On 5 datasets across 3 domains, our model matches or exceeds state-of-the-art performance, sometimes by a large margin.", "output": [ "A novel, high-performing architecture for end-to-end named entity recognition and relation extraction that is fast to train." ] }, { "id": "task668-08a52578b3ba43cb9edaab627141ce6f", "input": "Hierarchical Sparse Coding (HSC) is a powerful model to efficiently represent multi-dimensional, structured data such as images. The simplest solution to solve this computationally hard problem is to decompose it into independent layerwise subproblems. However, neuroscientific evidence would suggest inter-connecting these subproblems as in the Predictive Coding (PC) theory, which adds top-down connections between consecutive layers. In this study, a new model called Sparse Deep Predictive Coding (SDPC) is introduced to assess the impact of this inter-layer feedback connection. In particular, the SDPC is compared with a Hierarchical Lasso (Hi-La) network made out of a sequence of Lasso layers. A 2-layered SDPC and a Hi-La networks are trained on 3 different databases and with different sparsity parameters on each layer. First, we show that the overall prediction error generated by SDPC is lower thanks to the feedback mechanism as it transfers prediction error between layers. Second, we demonstrate that the inference stage of the SDPC is faster to converge than for the Hi-La model. Third, we show that the SDPC also accelerates the learning process. Finally, the qualitative analysis of both models dictionaries, supported by their activation probability, show that the SDPC features are more generic and informative.", "output": [ "This paper experimentally demonstrates the beneficial effect of top-down connections in Hierarchical Sparse Coding algorithm.", "This paper presents a study that compares techniques for Hierarchical Sparse Coding, showing that the top-down term is beneficial in reducing predictive error and can learn faster." ] }, { "id": "task668-d878e42ec1ef40a3b2009448fc1f319e", "input": "Neural activity is highly variable in response to repeated stimuli. We used an open dataset, the Allen Brain Observatory, to quantify the distribution of responses to repeated natural movie presentations. A large fraction of responses are best fit by log-normal distributions or Gaussian mixtures with two components. These distributions are similar to those from units in deep neural networks with dropout. Using a separate set of electrophysiological recordings, we constructed a population coupling model as a control for state-dependent activity fluctuations and found that the model residuals also show non-Gaussian distributions. We then analyzed responses across trials from multiple sections of different movie clips and observed that the noise in cortex aligns better with in-clip versus out-of-clip stimulus variations. We argue that noise is useful for generalization when it moves along representations of different exemplars in-class, similar to the structure of cortical noise.", "output": [ "We study the structure of noise in the brain and find it may help generalization by moving representations along in-class stimulus variations." ] }, { "id": "task668-29394da02456444ba4940c07e607e79a", "input": "The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. \n In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. Our model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators. Extensive experimental results show the superior performance of our approach on spectral image classification, community detection, vertex classification and matrix completion tasks.", "output": [ "A spectral graph convolutional neural network with spectral zoom properties." ] }, { "id": "task668-b1a8e01c0cb642ed87607a57f6b3378d", "input": "We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through safe policies, i.e.,~policies that keep the agent in desirable situations, both during training and at convergence. We formulate these problems as {\\em constrained} Markov decision processes (CMDPs) and present safe policy optimization algorithms that are based on a Lyapunov approach to solve them. Our algorithms can use any standard policy gradient (PG) method, such as deep deterministic policy gradient (DDPG) or proximal policy optimization (PPO), to train a neural network policy, while guaranteeing near-constraint satisfaction for every policy update by projecting either the policy parameter or the selected action onto the set of feasible solutions induced by the state-dependent linearized Lyapunov constraints. Compared to the existing constrained PG algorithms, ours are more data efficient as they are able to utilize both on-policy and off-policy data. Moreover, our action-projection algorithm often leads to less conservative policy updates and allows for natural integration into an end-to-end PG training pipeline. We evaluate our algorithms and compare them with the state-of-the-art baselines on several simulated (MuJoCo) tasks, as well as a real-world robot obstacle-avoidance problem, demonstrating their effectiveness in terms of balancing performance and constraint satisfaction.", "output": [ "A general framework for incorporating long-term safety constraints in policy-based reinforcement learning" ] }, { "id": "task668-10d93068e1604681a756a749d2c84985", "input": "In this paper, we present a new generative model for learning latent embeddings. Compared to the classical generative process, where each observed data point is generated from an individual latent variable, our approach assumes a global latent variable to generate the whole set of observed data points. We then propose a learning objective that is derived as an approximation to a lower bound to the data log likelihood, leading to our algorithm, WiSE-ALE. Compared to the standard ELBO objective, where the variational posterior for each data point is encouraged to match the prior distribution, the WiSE-ALE objective matches the averaged posterior, over all samples, with the prior, allowing the sample-wise posterior distributions to have a wider range of acceptable embedding mean and variance and leading to better reconstruction quality in the auto-encoding process. Through various examples and comparison to other state-of-the-art VAE models, we demonstrate that WiSE-ALE has excellent information embedding properties, whilst still retaining the ability to learn a smooth, compact representation.", "output": [ "We propose a new latent variable model to learn latent embeddings for some high-dimensional data. " ] }, { "id": "task668-9e6af5b514054fb48698763a5e814b8d", "input": "In this article we use rate-distortion theory, a branch of information theory devoted to the problem of lossy compression, to shed light on an important problem in latent variable modeling of data: is there room to improve the model? One way to address this question is to find an upper bound on the probability (equivalently a lower bound on the negative log likelihood) that the model can assign to some data as one varies the prior and/or the likelihood function in a latent variable model. The core of our contribution is to formally show that the problem of optimizing priors in latent variable models is exactly an instance of the variational optimization problem that information theorists solve when computing rate-distortion functions, and then to use this to derive a lower bound on negative log likelihood. Moreover, we will show that if changing the prior can improve the log likelihood, then there is a way to change the likelihood function instead and attain the same log likelihood, and thus rate-distortion theory is of relevance to both optimizing priors as well as optimizing likelihood functions. We will experimentally argue for the usefulness of quantities derived from rate-distortion theory in latent variable modeling by applying them to a problem in image modeling.", "output": [ "Use rate-distortion theory to bound how much a latent variable model can be improved", "Addresses problems of optimization of the prior in the latent variable model and the selection of the likelihood function by proposing criteria based on a lower-bound on the negative log-likelihood.", "Presents a theorem which gives a lower bound on negative log likelihood of rate-distortion for latent-variable modeling", "The authors argue that the rate-distortion theory for lossy compression provides a natural toolkit for studying latent variable models proposes a lower bound." ] }, { "id": "task668-56da6c9ec57a4037a90bbe35ee02a084", "input": "Curriculum learning consists in learning a difficult task by first training on an easy version of it, then on more and more difficult versions and finally on the difficult task. To make this learning efficient, given a curriculum and the current learning state of an agent, we need to find what are the good next tasks to train the agent on.\n Teacher-Student algorithms assume that the good next tasks are the ones on which the agent is making the fastest progress or digress. We first simplify and improve them. However, two problematic situations where the agent is mainly trained on tasks it can't learn yet or it already learnt may occur.\n Therefore, we introduce a new algorithm using min max ordered curriculums that assumes that the good next tasks are the ones that are learnable but not learnt yet. It outperforms Teacher-Student algorithms on small curriculums and significantly outperforms them on sophisticated ones with numerous tasks.", "output": [ "We present a new algorithm for learning by curriculum based on the notion of mastering rate that outperforms previous algorithms." ] }, { "id": "task668-5c5c48b7b5ef42828344585a8ee60d5e", "input": "Neural networks have reached outstanding performance for solving various ill-posed inverse problems in imaging. However, drawbacks of end-to-end learning approaches in comparison to classical variational methods are the requirement of expensive retraining for even slightly different problem statements and the lack of provable error bounds during inference. Recent works tackled the first problem by using networks trained for Gaussian image denoising as generic plug-and-play regularizers in energy minimization algorithms. Even though this obtains state-of-the-art results on many tasks, heavy restrictions on the network architecture have to be made if provable convergence of the underlying fixed point iteration is a requirement. More recent work has proposed to train networks to output descent directions with respect to a given energy function with a provable guarantee of convergence to a minimizer of that energy. However, each problem and energy requires the training of a separate network.\n In this paper we consider the combination of both approaches by projecting the outputs of a plug-and-play denoising network onto the cone of descent directions to a given energy. This way, a single pre-trained network can be used for a wide variety of reconstruction tasks. Our results show improvements compared to classical energy minimization methods while still having provable convergence guarantees.", "output": [ "We use neural networks trained for image denoising as plug-and-play priors in energy minimization algorithms for image reconstruction problems with provable convergence." ] }, { "id": "task668-aceb9fad50784a4fbeceab66ff7d3cd9", "input": "We present EDA: easy data augmentation techniques for boosting performance on text classification tasks. EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion. On five text classification tasks, we show that EDA improves performance for both convolutional and recurrent neural networks. EDA demonstrates particularly strong results for smaller datasets; on average, across five datasets, training with EDA while using only 50% of the available training set achieved the same accuracy as normal training with all available data. We also performed extensive ablation studies and suggest parameters for practical use.", "output": [ "Simple text augmentation techniques can significantly boost performance on text classification tasks, especially for small datasets." ] }, { "id": "task668-89ad1f37a0fc456e937bc46ebbe8cb07", "input": "The robustness of neural networks to adversarial examples has received great attention due to security implications. Despite various attack approaches to crafting visually imperceptible adversarial examples, little has been developed towards a comprehensive measure of robustness. In this paper, we provide theoretical justification for converting robustness analysis into a local Lipschitz constant estimation problem, and propose to use the Extreme Value Theory for efficient evaluation. Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness. The proposed CLEVER score is attack-agnostic and is computationally feasible for large neural networks. Experimental results on various networks, including ResNet, Inception-v3 and MobileNet, show that (i) CLEVER is aligned with the robustness indication measured by the $\\ell_2$ and $\\ell_\\infty$ norms of adversarial examples from powerful attacks, and (ii) defended networks using defensive distillation or bounded ReLU indeed give better CLEVER scores. To the best of our knowledge, CLEVER is the first attack-independent robustness metric that can be applied to any neural network classifiers.\n\n", "output": [ "We propose the first attack-independent robustness metric, a.k.a CLEVER, that can be applied to any neural network classifier." ] }, { "id": "task668-de2c9e2e96b944ef98a7573fe74f80d8", "input": "Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency. Particularly, Dyna architecture, as an elegant model-based architecture integrating learning and planning, provides huge flexibility of using a model. One of the most important components in Dyna is called search-control, which refers to the process of generating state or state-action pairs from which we query the model to acquire simulated experiences. Search-control is critical to improve learning efficiency. In this work, we propose a simple and novel search-control strategy by searching high frequency region on value function. Our main intuition is built on Shannon sampling theorem from signal processing, which indicates that a high frequency signal requires more samples to reconstruct. We empirically show that a high frequency function is more difficult to approximate. This suggests a search-control strategy: we should use states in high frequency region of the value function to query the model to acquire more samples. We develop a simple strategy to locally measure the frequency of a function by gradient norm, and provide theoretical justification for this approach. We then apply our strategy to search-control in Dyna, and conduct experiments to show its property and effectiveness on benchmark domains.", "output": [ "Acquire states from high frequency region for search-control in Dyna.", "The authors propose to do sampling in the high-frequency domain to increase the sample efficiency", "This paper proposes a new way to select states from which do do transitions in dyna algorithm." ] }, { "id": "task668-e9e26c521a964640abd7bf04f5a262e8", "input": "Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics. Although previous works have successfully reduced precision in inference, transferring both training and inference processes to low-bitwidth integers has not been demonstrated simultaneously. In this work, we develop a new method termed as ``\"WAGE\" to discretize both training and inference, where weights (W), activations (A), gradients (G) and errors (E) among layers are shifted and linearly constrained to low-bitwidth integers. To perform pure discrete dataflow for fixed-point devices, we further replace batch normalization by a constant scaling layer and simplify other components that are arduous for integer implementation. Improved accuracies can be obtained on multiple datasets, which indicates that WAGE somehow acts as a type of regularization. Empirically, we demonstrate the potential to deploy training in hardware systems such as integer-based deep learning accelerators and neuromorphic chips with comparable accuracy and higher energy efficiency, which is crucial to future AI applications in variable scenarios with transfer and continual learning demands.", "output": [ "We apply training and inference with only low-bitwidth integers in DNNs", "A method called WAGE which quantizes all operands and operators in a neural network to reduce the number of bits for representation in a network.", "The authors propose discretized weights, activations, gradients, and errors at both training and testing time on neural networks" ] }, { "id": "task668-9478ec65f51e415c8e004ad24fd24f04", "input": "We present an end-to-end trained memory system that quickly adapts to new data and generates samples like them. Inspired by Kanerva's sparse distributed memory, it has a robust distributed reading and writing mechanism. The memory is analytically tractable, which enables optimal on-line compression via a Bayesian update-rule. We formulate it as a hierarchical conditional generative model, where memory provides a rich data-dependent prior distribution. Consequently, the top-down memory and bottom-up perception are combined to produce the code representing an observation. Empirically, we demonstrate that the adaptive memory significantly improves generative models trained on both the Omniglot and CIFAR datasets. Compared with the Differentiable Neural Computer (DNC) and its variants, our memory model has greater capacity and is significantly easier to train.", "output": [ "A generative memory model that combines slow-learning neural networks and a fast-adapting linear Gaussian model as memory." ] }, { "id": "task668-674987ed65414ebc9a94340526c35dac", "input": "When considering simultaneously a finite number of tasks, multi-output learning enables one to account for the similarities of the tasks via appropriate regularizers. We propose a generalization of the classical setting to a continuum of tasks by using vector-valued RKHSs.", "output": [ "We propose an extension of multi-output learning to a continuum of tasks using operator-valued kernels." ] }, { "id": "task668-87bc07c2e6b0448e86afa5d967e41af8", "input": "Neural network quantization is becoming an industry standard to efficiently deploy deep learning models on hardware platforms, such as CPU, GPU, TPU, and FPGAs. However, we observe that the conventional quantization approaches are vulnerable to adversarial attacks. This paper aims to raise people's awareness about the security of the quantized models, and we designed a novel quantization methodology to jointly optimize the efficiency and robustness of deep learning models. We first conduct an empirical study to show that vanilla quantization suffers more from adversarial attacks. We observe that the inferior robustness comes from the error amplification effect, where the quantization operation further enlarges the distance caused by amplified noise. Then we propose a novel Defensive Quantization (DQ) method by controlling the Lipschitz constant of the network during quantization, such that the magnitude of the adversarial noise remains non-expansive during inference. Extensive experiments on CIFAR-10 and SVHN datasets demonstrate that our new quantization method can defend neural networks against adversarial examples, and even achieves superior robustness than their full-precision counterparts, while maintaining the same hardware efficiency as vanilla quantization approaches. As a by-product, DQ can also improve the accuracy of quantized models without adversarial attack.", "output": [ "We designed a novel quantization methodology to jointly optimize the efficiency and robustness of deep learning models.", "Proposes a regularization scheme to protect quantized neural networks from adversarial attacks using a Lipschitz constant filitering of the inner layers' inpout-output." ] }, { "id": "task668-816b037fd4504d2e81e48f14cedde630", "input": "Monte Carlo Tree Search (MCTS) has achieved impressive results on a range of discrete environments, such as Go, Mario and Arcade games, but it has not yet fulfilled its true potential in continuous domains. In this work, we introduceTPO, a tree search based policy optimization method for continuous environments. TPO takes a hybrid approach to policy optimization. Building the MCTS tree in a continuous action space and updating the policy gradient using off-policy MCTS trajectories are non-trivial. To overcome these challenges, we propose limiting tree search branching factor by drawing only few action samples from the policy distribution and define a new loss function based on the trajectories’ mean and standard deviations. Our approach led to some non-intuitive findings. MCTS training generally requires a large number of samples and simulations. However, we observed that bootstrappingtree search with a pre-trained policy allows us to achieve high quality results with a low MCTS branching factor and few number of simulations. Without the proposed policy bootstrapping, continuous MCTS would require a much larger branching factor and simulation count, rendering it computationally and prohibitively expensive. In our experiments, we use PPO as our baseline policy optimization algorithm. TPO significantly improves the policy on nearly all of our benchmarks. For example, in complex environments such as Humanoid, we achieve a 2.5×improvement over the baseline algorithm.", "output": [ "We use MCTS to further optimize a bootstrapped policy for continuous action spaces under a policy iteration setting." ] }, { "id": "task668-515e1953aa29401d96be10dd012047d4", "input": "Word embeddings extract semantic features of words from large datasets of text.\n Most embedding methods rely on a log-bilinear model to predict the occurrence\n of a word in a context of other words. Here we propose word2net, a method that\n replaces their linear parametrization with neural networks. For each term in the\n vocabulary, word2net posits a neural network that takes the context as input and\n outputs a probability of occurrence. Further, word2net can use the hierarchical\n organization of its word networks to incorporate additional meta-data, such as\n syntactic features, into the embedding model. For example, we show how to share\n parameters across word networks to develop an embedding model that includes\n part-of-speech information. We study word2net with two datasets, a collection\n of Wikipedia articles and a corpus of U.S. Senate speeches. Quantitatively, we\n found that word2net outperforms popular embedding methods on predicting held-\n out words and that sharing parameters based on part of speech further boosts\n performance. Qualitatively, word2net learns interpretable semantic representations\n and, compared to vector-based methods, better incorporates syntactic information.", "output": [ "Word2net is a novel method for learning neural network representations of words that can use syntactic information to learn better semantic features.", "This paper extends SGNS with an architectural change from a bag-of-words model to a feedforward model, and contributes a new form of regularization by tying a subset of layers between different associated networks.", "A method to use non-linear combination of context vectors for learning vector representation of words, where the main idea is to replace each word embedding by a neural network." ] }, { "id": "task668-6f953f233c674282b751a8f1137060ba", "input": "Over the past decade, knowledge graphs became popular for capturing structured domain knowledge. \n Relational learning models enable the prediction of missing links inside knowledge graphs. More specifically, latent distance approaches model the relationships among entities via a distance between latent representations.\n Translating embedding models (e.g., TransE) are among the most popular latent distance approaches which use one distance function to learn multiple relation patterns. \n However, they are mostly inefficient in capturing symmetric relations since the representation vector norm for all the symmetric relations becomes equal to zero. They also lose information when learning relations with reflexive patterns since they become symmetric and transitive.\n We propose the Multiple Distance Embedding model (MDE) that addresses these limitations and a framework which enables collaborative combinations of latent distance-based terms (MDE).\n Our solution is based on two principles: 1) using limit-based loss instead of margin ranking loss and 2) by learning independent embedding vectors for each of terms we can collectively train and predict using contradicting distance terms.\n We further demonstrate that MDE allows modeling relations with (anti)symmetry, inversion, and composition patterns. We propose MDE as a neural network model which allows us to map non-linear relations between the embedding vectors and the expected output of the score function.\n Our empirical results show that MDE outperforms the state-of-the-art embedding models on several benchmark datasets.", "output": [ "A novel method of modelling Knowledge Graphs based on Distance Embeddings and Neural Networks" ] }, { "id": "task668-69bf9e9f120d4c2492745dfaae9b7618", "input": "Neural Style Transfer has become a popular technique for\n generating images of distinct artistic styles using convolutional neural networks. This\n recent success in image style transfer has raised the question of\n whether similar methods can be leveraged to alter the “style” of musical\n audio. In this work, we attempt long time-scale high-quality audio transfer\n and texture synthesis in the time-domain that captures harmonic,\n rhythmic, and timbral elements related to musical style, using examples that\n may have different lengths and musical keys. We demonstrate the ability\n to use randomly initialized convolutional neural networks to transfer\n these aspects of musical style from one piece onto another using 3\n different representations of audio: the log-magnitude of the Short Time\n Fourier Transform (STFT), the Mel spectrogram, and the Constant-Q Transform\n spectrogram. We propose using these representations as a way of\n generating and modifying perceptually significant characteristics of\n musical audio content. We demonstrate each representation's\n shortcomings and advantages over others by carefully designing\n neural network structures that complement the nature of musical audio. Finally, we show that the most\n compelling “style” transfer examples make use of an ensemble of these\n representations to help capture the varying desired characteristics of\n audio signals.", "output": [ "We present a long time-scale musical audio style transfer algorithm which synthesizes audio in the time-domain, but uses Time-Frequency representations of audio." ] }, { "id": "task668-a79396fb837c4724a0e28d88129a067e", "input": "Extracting relevant information, causally inferring and predicting the future states with high accuracy is a crucial task for modeling complex systems. The endeavor to address these tasks is made even more challenging when we have to deal with high-dimensional heterogeneous data streams. Such data streams often have higher-order inter-dependencies across spatial and temporal dimensions. We propose to perform a soft-clustering of the data and learn its dynamics to produce a compact dynamical model while still ensuring the original objectives of causal inference and accurate predictions. To efficiently and rigorously process the dynamics of soft-clustering, we advocate for an information theory inspired approach that incorporates stochastic calculus and seeks to determine a trade-off between the predictive accuracy and compactness of the mathematical representation. We cast the model construction as a maximization of the compression of the state variables such that the predictive ability and causal interdependence (relatedness) constraints between the original data streams and the compact model are closely bounded. We provide theoretical guarantees concerning the convergence of the proposed learning algorithm. To further test the proposed framework, we consider a high-dimensional Gaussian case study and describe an iterative scheme for updating the new model parameters. Using numerical experiments, we demonstrate the benefits on compression and prediction accuracy for a class of dynamical systems. Finally, we apply the proposed algorithm to the real-world dataset of multimodal sentiment intensity and show improvements in prediction with reduced dimensions.", "output": [ "Compact perception of dynamical process", "Studies the problem of compactly representing the model of a complex dynamic system while preserving information by using an information bottleneck method.", "This paper studied the Gaussian linear dynamic and proposed an algorithm for computing the Information Bottleneck Hierarchy (IBH)." ] }, { "id": "task668-c59f91d904f14f8790da00f6392279b5", "input": "Typical recent neural network designs are primarily convolutional layers, but the tricks enabling structured efficient linear layers (SELLs) have not yet been adapted to the convolutional setting. We present a method to express the weight tensor in a convolutional layer using diagonal matrices, discrete cosine transforms (DCTs) and permutations that can be optimised using standard stochastic gradient methods. A network composed of such structured efficient convolutional layers (SECL) outperforms existing low-rank networks and demonstrates competitive computational efficiency.", "output": [ "It's possible to substitute the weight matrix in a convolutional layer to train it as a structured efficient layer; performing as well as low-rank decomposition.", "This work applies previous Structured Efficient Linear Layers to conv layers and proposes Structured Efficient Convolutional Layers as substitution of original conv layers." ] }, { "id": "task668-eff8ada446224950b1b5880eeebbb9a1", "input": "Hierarchical Task Networks (HTN) generate plans using a decomposition process guided by extra domain knowledge to guide search towards a planning task. While many HTN planners can make calls to external processes (e.g. to a simulator interface) during the decomposition process, this is a computationally expensive process, so planner implementations often use such calls in an ad-hoc way using very specialized domain knowledge to limit the number of calls. Conversely, the few classical planners that are capable of using external calls (often called semantic attachments) during planning do so in much more limited ways by generating a fixed number of ground operators at problem grounding time. In this paper we develop the notion of semantic attachments for HTN planning using semi co-routines, allowing such procedurally defined predicates to link the planning process to custom unifications outside of the planner. The resulting planner can then use such co-routines as part of its backtracking mechanism to search through parallel dimensions of the state-space (e.g. through numeric variables). We show empirically that our planner outperforms the state-of-the-art numeric planners in a number of domains using minimal extra domain knowledge.", "output": [ "An approach to perform HTN planning using external procedures to evaluate predicates at runtime (semantic attachments)." ] }, { "id": "task668-ccfa2193b80b4a2e8486c465a97e0771", "input": "The scarcity of labeled training data often prohibits the internationalization of NLP models to multiple languages. Cross-lingual understanding has made progress in this area using language universal representations. However, most current approaches focus on the problem as one of aligning language and do not address the natural domain drift across languages and cultures. In this paper, We address the domain gap in the setting of semi-supervised cross-lingual document classification, where labeled data is available in a source language and only unlabeled data is available in the target language. We combine a state-of-the-art unsupervised learning method, masked language modeling pre-training, with a recent method for semi-supervised learning, Unsupervised Data Augmentation (UDA), to simultaneously close the language and the domain gap. We show that addressing the domain gap in cross-lingual tasks is crucial. We improve over strong baselines and achieve a new state-of-the-art for cross-lingual document classification.", "output": [ "Semi-supervised Cross-lingual Document Classification" ] }, { "id": "task668-2e57ca5822744030aeb0380215efc210", "input": "Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space. The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters. Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks. Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space.", "output": [ "Latent Embedding Optimization (LEO) is a novel gradient-based meta-learner with state-of-the-art performance on the challenging 5-way 1-shot and 5-shot miniImageNet and tieredImageNet classification tasks.", "A new meta-learning framework that learns data-dependent latent space, performs fast adaptation in the latent space, is effective for few-shot learning, has task-dependent initialization for adaptation, and works well for multimodal task distribution.", "This paper proposes a latent embedding optimization method for meta-learning, and claims the contribution is to decouple optimization-based meta-learning techniques from high-dimensional space of model parameters." ] }, { "id": "task668-43f903c3b03645879e9266bae4b6f1e7", "input": "We investigate in this paper the architecture of deep convolutional networks. Building on existing state of the art models, we propose a reconfiguration of the model parameters into several parallel branches at the global network level, with each branch being a standalone CNN. We show that this arrangement is an efficient way to significantly reduce the number of parameters while at the same time improving the performance. The use of branches brings an additional form of regularization. In addition to splitting the parameters into parallel branches, we propose a tighter coupling of these branches by averaging their log-probabilities. The tighter coupling favours the learning of better representations, even at the level of the individual branches, as compared to when each branch is trained independently. We refer to this branched architecture as \"coupled ensembles\". The approach is very generic and can be applied with almost any neural network architecture. With coupled ensembles of DenseNet-BC and parameter budget of 25M, we obtain error rates of 2.92%, 15.68% and 1.50% respectively on CIFAR-10, CIFAR-100 and SVHN tasks. For the same parameter budget, DenseNet-BC has an error rate of 3.46%, 17.18%, and 1.8% respectively. With ensembles of coupled ensembles, of DenseNet-BC networks, with 50M total parameters, we obtain error rates of 2.72%, 15.13% and 1.42% respectively on these tasks.", "output": [ "We show that splitting a neural network into parallel branches improves performance and that proper coupling of the branches improves performance even further.", "The work proposes a reconfiguration of the existing state-of-the-art CNN model using a new branching architecture, with better performance.", "This paper shows parameter-saving benefits of coupled ensembling.", "Presents a deep network architecture which processes data using multiple parallel branches and combines the posterior from these branches to compute the final scores." ] }, { "id": "task668-0532135f39504c65812cadd96317ca1a", "input": "Unsupervised domain adaptive object detection aims to learn a robust detector on the domain shift circumstance, where the training (source) domain is label-rich with bounding box annotations, while the testing (target) domain is label-agnostic and the feature distributions between training and testing domains are dissimilar or even totally different. In this paper, we propose a gradient detach based Stacked Complementary Losses (SCL) method that uses detection objective (cross entropy and smooth l1 regression) as the primary objective, and cuts in several auxiliary losses in different network stages to utilize information from the complement data (target images) that can be effective in adapting model parameters to both source and target domains. A gradient detach operation is applied between detection and context sub-networks during training to force networks to learn discriminative representations. We argue that the conventional training with primary objective mainly leverages the information from the source-domain for maximizing likelihood and ignores the complement data in shallow layers of networks, which leads to an insufficient integration within different domains. Thus, our proposed method is a more syncretic adaptation learning process. We conduct comprehensive experiments on seven datasets, the results demonstrate that our method performs favorably better than the state-of-the-art methods by a large margin. For instance, from Cityscapes to FoggyCityscapes, we achieve 37.9% mAP, outperforming the previous art Strong-Weak by 3.6%.", "output": [ "We introduce a new gradient detach based complementary objective training strategy for domain adaptive object detection." ] }, { "id": "task668-c79a371845da4c5693fa948eaef01b01", "input": "In reinforcement learning, it is common to let an agent interact with its environment for a fixed amount of time before resetting the environment and repeating the process in a series of episodes. The task that the agent has to learn can either be to maximize its performance over (i) that fixed amount of time, or (ii) an indefinite period where the time limit is only used during training. In this paper, we investigate theoretically how time limits could effectively be handled in each of the two cases. In the first one, we argue that the terminations due to time limits are in fact part of the environment, and propose to include a notion of the remaining time as part of the agent's input. In the second case, the time limits are not part of the environment and are only used to facilitate learning. We argue that such terminations should not be treated as environmental ones and propose a method, specific to value-based algorithms, that incorporates this insight by continuing to bootstrap at the end of each partial episode. To illustrate the significance of our proposals, we perform several experiments on a range of environments from simple few-state transition graphs to complex control tasks, including novel and standard benchmark domains. Our results show that the proposed methods improve the performance and stability of existing reinforcement learning algorithms.", "output": [ "We consider the problem of learning optimal policies in time-limited and time-unlimited domains using time-limited interactions." ] }, { "id": "task668-3c315ecb9514465e8b25a4cb8a46c160", "input": "Robustness of neural networks has recently been highlighted by the adversarial examples, i.e., inputs added with well-designed perturbations which are imperceptible to humans but can cause the network to give incorrect outputs. In this paper, we design a new CNN architecture that by itself has good robustness. We introduce a simple but powerful technique, Random Mask, to modify existing CNN structures. We show that CNN with Random Mask achieves state-of-the-art performance against black-box adversarial attacks without applying any adversarial training. We next investigate the adversarial examples which “fool” a CNN with Random Mask. Surprisingly, we find that these adversarial examples often “fool” humans as well. This raises fundamental questions on how to define adversarial examples and robustness properly.", "output": [ "We propose a technique that modifies CNN structures to enhance robustness while keeping high test accuracy, and raise doubt on whether current definition of adversarial examples is appropriate by generating adversarial examples able to fool humans.", "This paper proposes a simple technique for improving the robustness of neural networks against black-box attacks.", "The authors propose a simple method for increasing the robustness of convolutional neural networks against adversarial examples, with surprisingly good results." ] }, { "id": "task668-f9d17af19e624c4bb04997cc1ca0e47a", "input": "This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques.", "output": [ "We propose a differentiable architecture search algorithm for both convolutional and recurrent networks, achieving competitive performance with the state of the art using orders of magnitude less computation resources." ] }, { "id": "task668-3f1b6821b4d749129ee1e0794ef35d42", "input": "The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both 'what' and 'how' to imitate. We pursue an alternative paradigm wherein an agent first explores the world without any expert supervision and then distills its experience into a goal-conditioned skill policy with a novel forward consistency loss. In our framework, the role of the expert is only to communicate the goals (i.e., what to imitate) during inference. The learned policy is then employed to mimic the expert (i.e., how to imitate) after seeing just a sequence of images demonstrating the desired task. Our method is 'zero-shot' in the sense that the agent never has access to expert actions during training or for the task demonstration at inference. We evaluate our zero-shot imitator in two real-world settings: complex rope manipulation with a Baxter robot and navigation in previously unseen office environments with a TurtleBot. Through further experiments in VizDoom simulation, we provide evidence that better mechanisms for exploration lead to learning a more capable policy which in turn improves end task performance. Videos, models, and more details are available at https://pathak22.github.io/zeroshot-imitation/.", "output": [ "Agents can learn to imitate solely visual demonstrations (without actions) at test time after learning from their own experience without any form of supervision at training time.", "This paper proposes and approach for zero-shot visual learning by learning parametric skill functions.", "A paper about imitation of a task presented just during inference, where learning is performed in a self-supervised manner and during training the agent explores related but different tasks.", "Proposes a method for sidestepping the issue of expensive expert demonstration by using the random exploration of an agent to learn generalizable skills which can be applied without specific pretraining" ] }, { "id": "task668-0a83176a5bed45ed8fea7f2509484095", "input": "We present NeuroSAT, a message passing neural network that learns to solve SAT problems after only being trained as a classifier to predict satisfiability. Although it is not competitive with state-of-the-art SAT solvers, NeuroSAT can solve problems that are substantially larger and more difficult than it ever saw during training by simply running for more iterations. Moreover, NeuroSAT generalizes to novel distributions; after training only on random SAT problems, at test time it can solve SAT problems encoding graph coloring, clique detection, dominating set, and vertex cover problems, all on a range of distributions over small random graphs.", "output": [ "We train a graph network to predict boolean satisfiability and show that it learns to search for solutions, and that the solutions it finds can be decoded from its activations.", "The paper describes a general neural network architecture for predicting satisfiability", "This paper presents the NeuroSAT architecture which uses a deep message passing neural net for predicting the satisfiability of CNF instances" ] }, { "id": "task668-bcc5add0fe8343368398dc5f952d32e2", "input": "This paper explores the use of self-ensembling for visual domain adaptation problems. Our technique is derived from the mean teacher variant (Tarvainen et. al 2017) of temporal ensembling (Laine et al. 2017), a technique that achieved state of the art results in the area of semi-supervised learning. We introduce a number of modifications to their approach for challenging domain adaptation scenarios and evaluate its effectiveness. Our approach achieves state of the art results in a variety of benchmarks, including our winning entry in the VISDA-2017 visual domain adaptation challenge. In small image benchmarks, our algorithm not only outperforms prior art, but can also achieve accuracy that is close to that of a classifier trained in a supervised fashion.", "output": [ "Self-ensembling based algorithm for visual domain adaptation, state of the art results, won VisDA-2017 image classification domain adaptation challenge." ] }, { "id": "task668-c43fc8231ac843ffb2ecccc446480321", "input": "We present a deep learning-based method for super-resolving coarse (low-resolution) labels assigned to groups of image pixels into pixel-level (high-resolution) labels, given the joint distribution between those low- and high-resolution labels. This method involves a novel loss function that minimizes the distance between a distribution determined by a set of model outputs and the corresponding distribution given by low-resolution labels over the same set of outputs. This setup does not require that the high-resolution classes match the low-resolution classes and can be used in high-resolution semantic segmentation tasks where high-resolution labeled data is not available. Furthermore, our proposed method is able to utilize both data with low-resolution labels and any available high-resolution labels, which we show improves performance compared to a network trained only with the same amount of high-resolution data.\n We test our proposed algorithm in a challenging land cover mapping task to super-resolve labels at a 30m resolution to a separate set of labels at a 1m resolution. We compare our algorithm with models that are trained on high-resolution data and show that 1) we can achieve similar performance using only low-resolution data; and 2) we can achieve better performance when we incorporate a small amount of high-resolution data in our training. We also test our approach on a medical imaging problem, resolving low-resolution probability maps into high-resolution segmentation of lymphocytes with accuracy equal to that of fully supervised models.", "output": [ "Super-resolving coarse labels into pixel-level labels, applied to aerial imagery and medical scans.", "A method to super-resolve coarse low-res segmentation labels if the joint distribution of low-res and high-res labels are known." ] }, { "id": "task668-f2465838c7514ab6b7bbd7c5682eeb69", "input": "Creating a knowledge base that is accurate, up-to-date and complete remains a significant challenge despite substantial efforts in automated knowledge base construction. In this paper, we present Alexandria -- a system for unsupervised, high-precision knowledge base construction. Alexandria uses a probabilistic program to define a process of converting knowledge base facts into unstructured text. Using probabilistic inference, we can invert this program and so retrieve facts, schemas and entities from web text. The use of a probabilistic program allows uncertainty in the text to be propagated through to the retrieved facts, which increases accuracy and helps merge facts from multiple sources. Because Alexandria does not require labelled training data, knowledge bases can be constructed with the minimum of manual input. We demonstrate this by constructing a high precision (typically 97\\%+) knowledge base for people from a single seed fact.", "output": [ "This paper presents a system for unsupervised, high-precision knowledge base construction using a probabilistic program to define a process of converting knowledge base facts into unstructured text.", "Overview about existing knowledge base that is constructed with a probabilistic model, with the knowledge base construction approach evaluated against other knowledge base approaches YAGO2, NELL, Knowledge Vault, and DeepDive.", "This paper uses a probabilistic program describing the process by which facts describing entities can be realised in text and large number of web pages, to learn to perform fact extraction about people using a single seed fact." ] }, { "id": "task668-9ad40ee774a948b4852bbce085710893", "input": "This paper explores many immediate connections between adaptive control and machine learning, both through common update laws as well as common concepts. Adaptive control as a field has focused on mathematical rigor and guaranteed convergence. The rapid advances in machine learning on the other hand have brought about a plethora of new techniques and problems for learning. This paper elucidates many of the numerous common connections between both fields such that results from both may be leveraged together to solve new problems. In particular, a specific problem related to higher order learning is solved through insights obtained from these intersections.", "output": [ "History of parallel developments in update laws and concepts between adaptive control and optimization in machine learning." ] }, { "id": "task668-b4fd84801f394bc483c2d0a6b148ef57", "input": "Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states. We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if predictive coding representations are useful to predict brain activity in the visual cortex. We use representational similarity analysis (RSA) to compare PredNet representations to functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data from the Algonauts Project (Cichy et al., 2019). In contrast to previous findings in the literature (Khaligh-Razavi & Kriegeskorte, 2014), we report empirical data suggesting that unsupervised models trained to predict frames of videos without further fine-tuning may outperform supervised image classification baselines in terms of correlation to spatial (fMRI) and temporal (MEG) data.", "output": [ "We show empirical evidence that predictive coding models yield representations more correlated to brain data than supervised image recognition models." ] }, { "id": "task668-2904f402d95e484e92e2b98e546d5f27", "input": "Current model-based reinforcement learning approaches use the model simply as a learned black-box simulator to augment the data for policy optimization or value function learning. In this paper, we show how to make more effective use of the model by exploiting its differentiability. We construct a policy optimization algorithm that uses the pathwise derivative of the learned model and policy across future timesteps. Instabilities of learning across many timesteps are prevented by using a terminal value function, learning the policy in an actor-critic fashion. Furthermore, we present a derivation on the monotonic improvement of our objective in terms of the gradient error in the model and value function. We show that our approach (i) is consistently more sample efficient than existing state-of-the-art model-based algorithms, (ii) matches the asymptotic performance of model-free algorithms, and (iii) scales to long horizons, a regime where typically past model-based approaches have struggled.", "output": [ "Policy gradient through backpropagation through time using learned models and Q-functions. SOTA results in reinforcement learning benchmark environments." ] }, { "id": "task668-1e71315635594ab992253a99c4f7acd2", "input": "We investigate task clustering for deep learning-based multi-task and few-shot learning in the settings with large numbers of diverse tasks. Our method measures task similarities using cross-task transfer performance matrix. Although this matrix provides us critical information regarding similarities between tasks, the uncertain task-pairs, i.e., the ones with extremely asymmetric transfer scores, may collectively mislead clustering algorithms to output an inaccurate task-partition. Moreover, when the number of tasks is large, generating the full transfer performance matrix can be very time consuming. To overcome these limitations, we propose a novel task clustering algorithm to estimate the similarity matrix based on the theory of matrix completion. The proposed algorithm can work on partially-observed similarity matrices based on only sampled task-pairs with reliable scores, ensuring its efficiency and robustness. Our theoretical analysis shows that under mild assumptions, the reconstructed matrix perfectly matches the underlying “true” similarity matrix with an overwhelming probability. The final task partition is computed by applying an efficient spectral clustering algorithm to the recovered matrix. Our results show that the new task clustering method can discover task clusters that benefit both multi-task learning and few-shot learning setups for sentiment classification and dialog intent classification tasks.", "output": [ "We propose a matrix-completion based task clustering algorithm for deep multi-task and few-shot learning in the settings with large numbers of diverse tasks." ] }, { "id": "task668-1e32c88abbeb439580818415f1c65380", "input": "To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights. One common technique for quantizing neural networks is the straight-through gradient method, which enables back-propagation through the quantization mapping. Despite its empirical success, little is understood about why the straight-through gradient method works.\n Building upon a novel observation that the straight-through gradient method is in fact identical to the well-known Nesterov’s dual-averaging algorithm on a quantization constrained optimization problem, we propose a more principled alternative approach, called ProxQuant , that formulates quantized network training as a regularized learning problem instead and optimizes it via the prox-gradient method. ProxQuant does back-propagation on the underlying full-precision vector and applies an efficient prox-operator in between stochastic gradient steps to encourage quantizedness. For quantizing ResNets and LSTMs, ProxQuant outperforms state-of-the-art results on binary quantization and is on par with state-of-the-art on multi-bit quantization. For binary quantization, our analysis shows both theoretically and experimentally that ProxQuant is more stable than the straight-through gradient method (i.e. BinaryConnect), challenging the indispensability of the straight-through gradient method and providing a powerful alternative.", "output": [ "A principled framework for model quantization using the proximal gradient method." ] }, { "id": "task668-4f9b8f8c6b064530b06ee838393c582f", "input": "Adversarial examples have been shown to be an effective way of assessing the robustness of neural sequence-to-sequence (seq2seq) models, by applying perturbations to the input of a model leading to large degradation in performance. However, these perturbations are only indicative of a weakness in the model if they do not change the semantics of the input in a way that would change the expected output. Using the example of machine translation (MT), we propose a new evaluation framework for adversarial attacks on seq2seq models taking meaning preservation into account and demonstrate that existing methods may not preserve meaning in general. Based on these findings, we propose new constraints for attacks on word-based MT systems and show, via human and automatic evaluation, that they produce more semantically similar adversarial inputs. Furthermore, we show that performing adversarial training with meaning-preserving attacks is beneficial to the model in terms of adversarial robustness without hurting test performance.", "output": [ "How you should evaluate adversarial attacks on seq2seq", "The authors investigate ways of generating adversarial examples, showing that adversarial training with the attack most consistent with the introduced meaning-preservation criteria results in improved robustness to this type of attack without degradation in the non-adversarial setting.", "The paper is about meaning-preserving adversarial perturbations in the context of Seq2Seq models" ] }, { "id": "task668-695de4360ba84bc08fe9b76cd5ef7c2a", "input": "The digitization of data has resulted in making datasets available to millions of users in the form of relational databases and spreadsheet tables. However, a majority of these users come from diverse backgrounds and lack the programming expertise to query and analyze such tables. We present a system that allows for querying data tables using natural language questions, where the system translates the question into an executable SQL query. We use a deep sequence to sequence model in wich the decoder uses a simple type system of SQL expressions to structure the output prediction. Based on the type, the decoder either copies an output token from the input question using an attention-based copying mechanism or generates it from a fixed vocabulary. We also introduce a value-based loss function that transforms a distribution over locations to copy from into a distribution over the set of input tokens to improve training of our model. We evaluate our model on the recently released WikiSQL dataset and show that our model trained using only supervised learning significantly outperforms the current state-of-the-art Seq2SQL model that uses reinforcement learning.", "output": [ "We present a type-based pointer network model together with a value-based loss method to effectively train a neural model to translate natural language to SQL.", "The paper claims to develop a novel method to map natural language queries to SQL by using a grammar to guide decoding and using a new loss function for pointer / copy mechanism" ] }, { "id": "task668-780afb4e349047ceb1b90cc671886016", "input": "The recent rise in popularity of few-shot learning algorithms has enabled models to quickly adapt to new tasks based on only a few training samples. Previous few-shot learning works have mainly focused on classification and reinforcement learning. \n In this paper, we propose a few-shot meta-learning system that focuses exclusively on regression tasks. Our model is based on the idea that the degree of freedom of the unknown function can be significantly reduced if it is represented as a linear combination of a set of appropriate basis functions. This enables a few labelled samples to approximate the function. We design a Feature Extractor network to encode basis functions for a task distribution, and a Weights Generator to generate the weight vector for a novel task. We show that our model outperforms the current state of the art meta-learning methods in various regression tasks.", "output": [ "We propose a few-shot learning model that is tailored specifically for regression tasks", "This paper proposes a novel shot-learning method for small sample regression problems.", "A method that learns a regression model with a few samples and outperforms other methods." ] }, { "id": "task668-d81dbb174cfb42db80080974be01e16b", "input": "As deep reinforcement learning is being applied to more and more tasks, there is a growing need to better understand and probe the learned agents. Visualizing and understanding the decision making process can be very valuable to comprehend and identify problems in the learned behavior. However, this topic has been relatively under-explored in the reinforcement learning community. In this work we present a method for synthesizing states of interest for a trained agent. Such states could be situations (e.g. crashing or damaging a car) in which specific actions are necessary. Further, critical states in which a very high or a very low reward can be achieved (e.g. risky states) are often interesting to understand the situational awareness of the system. To this end, we learn a generative model over the state space of the environment and use its latent space to optimize a target function for the state of interest. In our experiments we show that this method can generate insightful visualizations for a variety of environments and reinforcement learning methods. We explore these issues in the standard Atari benchmark games as well as in an autonomous driving simulator. Based on the efficiency with which we have been able to identify significant decision scenarios with this technique, we believe this general approach could serve as an important tool for AI safety applications.", "output": [ "We present a method to synthesize states of interest for reinforcement learning agents in order to analyze their behavior. ", "This paper proposes a generative model of visual observations in RL that is capable of generating observations of interests.", "An approach for visualizing states of interest that involves a variational autoencoder that learns to reconstruct state space and an optimization step that finds conditioning parameters to generate synthetic images." ] }, { "id": "task668-6687499f9e90461dadb5d18475b7560e", "input": "Nonlinearity is crucial to the performance of a deep (neural) network (DN).\n To date there has been little progress understanding the menagerie of available nonlinearities, but recently progress has been made on understanding the r\\^{o}le played by piecewise affine and convex nonlinearities like the ReLU and absolute value activation functions and max-pooling.\n In particular, DN layers constructed from these operations can be interpreted as {\\em max-affine spline operators} (MASOs) that have an elegant link to vector quantization (VQ) and $K$-means.\n While this is good theoretical progress, the entire MASO approach is predicated on the requirement that the nonlinearities be piecewise affine and convex, which precludes important activation functions like the sigmoid, hyperbolic tangent, and softmax.\n {\\em This paper extends the MASO framework to these and an infinitely large class of new nonlinearities by linking deterministic MASOs with probabilistic Gaussian Mixture Models (GMMs). }\n We show that, under a GMM, piecewise affine, convex nonlinearities like ReLU, absolute value, and max-pooling can be interpreted as solutions to certain natural ``hard'' VQ inference problems, while sigmoid, hyperbolic tangent, and softmax can be interpreted as solutions to corresponding ``soft'' VQ inference problems.\n We further extend the framework by hybridizing the hard and soft VQ optimizations to create a $\\beta$-VQ inference that interpolates between hard, soft, and linear VQ inference.\n A prime example of a $\\beta$-VQ DN nonlinearity is the {\\em swish} nonlinearity, which offers state-of-the-art performance in a range of computer vision tasks but was developed ad hoc by experimentation.\n Finally, we validate with experiments an important assertion of our theory, namely that DN performance can be significantly improved by enforcing orthogonality in its linear filters.\n", "output": [ "Reformulate deep networks nonlinearities from a vector quantization scope and bridge most known nonlinearities together." ] }, { "id": "task668-e474e3a6fbcf43b990b55bf3f55fd8c0", "input": "In cities with tall buildings, emergency responders need an accurate floor level location to find 911 callers quickly. We introduce a system to estimate a victim's floor level via their mobile device's sensor data in a two-step process. First, we train a neural network to determine when a smartphone enters or exits a building via GPS signal changes. Second, we use a barometer equipped smartphone to measure the change in barometric pressure from the entrance of the building to the victim's indoor location. Unlike impractical previous approaches, our system is the first that does not require the use of beacons, prior knowledge of the building infrastructure, or knowledge of user behavior. We demonstrate real-world feasibility through 63 experiments across five different tall buildings throughout New York City where our system predicted the correct floor level with 100% accuracy.\n", "output": [ "We used an LSTM to detect when a smartphone walks into a building. Then we predict the device's floor level using data from sensors aboard the smartphone.", "The paper introduces a system to estimate a floor-level via their mobile device's sensor data using an LSTM and changes in barometric pressure", "Proposal for a two-step method to determine which floor a mobile phone is on inside a tall building." ] }, { "id": "task668-e8a5992da00c42d39c30d99b8c9e1277", "input": "We propose a generative adversarial training approach for the problem of clarification question generation. Our approach generates clarification questions with the goal of eliciting new information that would make the given context more complete. We develop a Generative Adversarial Network (GAN) where the generator is a sequence-to-sequence model and the discriminator is a utility function that models the value of updating the context with the answer to the clarification question. We evaluate on two datasets, using both automatic metrics and human judgments of usefulness, specificity and relevance, showing that our approach outperforms both a retrieval-based model and ablations that exclude the utility model and the adversarial training.\n", "output": [ "We propose an adversarial training approach to the problem of clarification question generation which uses the answer to the question to model the reward. " ] }, { "id": "task668-448921103a0841a5afb41618cb1a245d", "input": "We present an information-theoretic framework for understanding trade-offs in unsupervised learning of deep latent-variables models using variational inference. This framework emphasizes the need to consider latent-variable models along two dimensions: the ability to reconstruct inputs (distortion) and the communication cost (rate). We derive the optimal frontier of generative models in the two-dimensional rate-distortion plane, and show how the standard evidence lower bound objective is insufficient to select between points along this frontier. However, by performing targeted optimization to learn generative models with different rates, we are able to learn many models that can achieve similar generative performance but make vastly different trade-offs in terms of the usage of the latent variable. Through experiments on MNIST and Omniglot with a variety of architectures, we show how our framework sheds light on many recent proposed extensions to the variational autoencoder family.", "output": [ "We provide an information theoretic and experimental analysis of state-of-the-art variational autoencoders." ] }, { "id": "task668-d659924c631d4c2895e93bf9fcfe171c", "input": "The unconditional generation of high fidelity images is a longstanding benchmark\n for testing the performance of image decoders. Autoregressive image models\n have been able to generate small images unconditionally, but the extension of\n these methods to large images where fidelity can be more readily assessed has\n remained an open problem. Among the major challenges are the capacity to encode\n the vast previous context and the sheer difficulty of learning a distribution that\n preserves both global semantic coherence and exactness of detail. To address the\n former challenge, we propose the Subscale Pixel Network (SPN), a conditional\n decoder architecture that generates an image as a sequence of image slices of equal\n size. The SPN compactly captures image-wide spatial dependencies and requires a\n fraction of the memory and the computation. To address the latter challenge, we\n propose to use multidimensional upscaling to grow an image in both size and depth\n via intermediate stages corresponding to distinct SPNs. We evaluate SPNs on the\n unconditional generation of CelebAHQ of size 256 and of ImageNet from size 32\n to 128. We achieve state-of-the-art likelihood results in multiple settings, set up\n new benchmark results in previously unexplored settings and are able to generate\n very high fidelity large scale samples on the basis of both datasets.", "output": [ "We show that autoregressive models can generate high fidelity images. ", "An architecture utilizing decoder, size-upscaling decoder, and depth-upscaling decoder components to tackle the problem of learning long-range dependencies in images in order to obtain high fidelity images.", "This paper addresses the problem of generation to high fidelity images, successfully showing convincing Imagenet samples with 128x128 resolution for a likelihood density model." ] }, { "id": "task668-8c44228084da43058838845cceab6b87", "input": "We introduce Explainable Adversarial Learning, ExL, an approach for training neural networks that are intrinsically robust to adversarial attacks. We find that the implicit generative modeling of random noise with the same loss function used during posterior maximization, improves a model's understanding of the data manifold furthering adversarial robustness. We prove our approach's efficacy and provide a simplistic visualization tool for understanding adversarial data, using Principal Component Analysis. Our analysis reveals that adversarial robustness, in general, manifests in models with higher variance along the high-ranked principal components. We show that models learnt with our approach perform remarkably well against a wide-range of attacks. Furthermore, combining ExL with state-of-the-art adversarial training extends the robustness of a model, even beyond what it is adversarially trained for, in both white-box and black-box attack scenarios.", "output": [ "Noise modeling at the input during discriminative training improves adversarial robustness. Propose PCA based evaluation metric for adversarial robustness", "This paper proposes, ExL, an adversarial training method using multiplicate noise that is shown to be helpful in defending against blackbox attacks on three datasets.", "This paper includes multiplicative noise N in training data to achieve adversarial robustness, when training on both model parameters theta and on the noise itself." ] }, { "id": "task668-c5e75bc5fcd74e108e7a24cd4e51664a", "input": "Understanding object motion is one of the core problems in computer vision. It requires segmenting and tracking objects over time. Significant progress has been made in instance segmentation, but such models cannot track objects, and more crucially, they are unable to reason in both 3D space and time.\n We propose a new spatio-temporal embedding loss on videos that generates temporally consistent video instance segmentation. Our model includes a temporal network that learns to model temporal context and motion, which is essential to produce smooth embeddings over time. Further, our model also estimates monocular depth, with a self-supervised loss, as the relative distance to an object effectively constrains where it can be next, ensuring a time-consistent embedding. Finally, we show that our model can accurately track and segment instances, even with occlusions and missed detections, advancing the state-of-the-art on the KITTI Multi-Object and Tracking Dataset.", "output": [ "We introduce a new spatio-temporal embedding loss on videos that generates temporally consistent video instance segmentation, even with occlusions and missed detections, using appearance, geometry, and temporal context." ] }, { "id": "task668-37d78dc34fc24f1c9ce5786ed055b583", "input": "Long Short-Term Memory (LSTM) is one of the most widely used recurrent structures in sequence modeling. Its goal is to use gates to control the information flow (e.g., whether to skip some information/transformation or not) in the recurrent computations, although its practical implementation based on soft gates only partially achieves this goal and is easy to overfit. In this paper, we propose a new way for LSTM training, which pushes the values of the gates towards 0 or 1. By doing so, we can (1) better control the information flow: the gates are mostly open or closed, instead of in a middle state; and (2) avoid overfitting to certain extent: the gates operate at their flat regions, which is shown to correspond to better generalization ability. However, learning towards discrete values of the gates is generally difficult. To tackle this challenge, we leverage the recently developed Gumbel-Softmax trick from the field of variational methods, and make the model trainable with standard backpropagation. Experimental results on language modeling and machine translation show that (1) the values of the gates generated by our method are more reasonable and intuitively interpretable, and (2) our proposed method generalizes better and achieves better accuracy on test sets in all tasks. Moreover, the learnt models are not sensitive to low-precision approximation and low-rank approximation of the gate parameters due to the flat loss surface.", "output": [ "We propose a new algorithm for LSTM training by learning towards binary-valued gates which we shown has many nice properties.", "Propose a new \"gate\" function for LSTM to enable the values of the gates towards 0 or 1. ", "The paper aims to push LSTM gates to be binary by employing the recent Gumbel-Softmax trick to obtain end-to-end trainable categorical distribution." ] }, { "id": "task668-ac1863dd82874f7ab03f6325d7c8b0e3", "input": "Intuitively, image classification should profit from using spatial information. Recent work, however, suggests that this might be overrated in standard CNNs. In this paper, we are pushing the envelope and aim to further investigate the reliance on and necessity of spatial information. We propose and analyze three methods, namely Shuffle Conv, GAP+FC and 1x1 Conv, that destroy spatial information during both training and testing phases. We extensively evaluate these methods on several object recognition datasets (CIFAR100, Small-ImageNet, ImageNet) with a wide range of CNN architectures (VGG16, ResNet50, ResNet152, MobileNet, SqueezeNet). Interestingly, we consistently observe that spatial information can be completely deleted from a significant number of layers with no or only small performance drops.", "output": [ "Spatial information at last layers is not necessary for a good classification accuracy." ] }, { "id": "task668-e9407dcfa3d649d6bc50924738e7f37a", "input": "Rectified linear units, or ReLUs, have become a preferred activation function for artificial neural networks. In this paper we consider the problem of learning a generative model in the presence of nonlinearity (modeled by the ReLU functions). Given a set of signal vectors $\\mathbf{y}^i \\in \\mathbb{R}^d, i =1, 2, \\dots , n$, we aim to learn the network parameters, i.e., the $d\\times k$ matrix $A$, under the model $\\mathbf{y}^i = \\mathrm{ReLU}(A\\mathbf{c}^i +\\mathbf{b})$, where $\\mathbf{b}\\in \\mathbb{R}^d$ is a random bias vector, and {$\\mathbf{c}^i \\in \\mathbb{R}^k$ are arbitrary unknown latent vectors}. We show that it is possible to recover the column space of $A$ within an error of $O(d)$ (in Frobenius norm) under certain conditions on the distribution of $\\mathbf{b}$.", "output": [ "We show that it is possible to recover the parameters of a 1-layer ReLU generative model from looking at samples generated by it" ] }, { "id": "task668-65f65fbcfbc74e92bbb11618c5db138f", "input": "Many real-world time series, such as in activity recognition, finance, or climate science, have changepoints where the system's structure or parameters change. Detecting changes is important as they may indicate critical events. However, existing methods for changepoint detection face challenges when (1) the patterns of change cannot be modeled using simple and predefined metrics, and (2) changes can occur gradually, at multiple time-scales. To address this, we show how changepoint detection can be treated as a supervised learning problem, and propose a new deep neural network architecture that can efficiently identify both abrupt and gradual changes at multiple scales. Our proposed method, pyramid recurrent neural network (PRNN), is designed to be scale-invariant, by incorporating wavelets and pyramid analysis techniques from multi-scale signal processing. Through experiments on synthetic and real-world datasets, we show that PRNN can detect abrupt and gradual changes with higher accuracy than the state of the art and can extrapolate to detect changepoints at novel timescales that have not been seen in training.", "output": [ "We introduce a scale-invariant neural network architecture for changepoint detection in multivariate time series.", "The paper leverages the concept of wavelet transform within a deep architecture to solve change point detection.", "This paper proposes a pyramid based neural net and applies it to 1D signals with underlying processes occurring at different time scales where the task is change point detection" ] }, { "id": "task668-dea6e065716c4031836a5099bf388877", "input": "Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate. We propose a mixed objective that combines cross entropy loss with self-critical policy learning, using rewards derived from word overlap to solve the misalignment between evaluation metric and optimization objective. In addition to the mixed objective, we introduce a deep residual coattention encoder that is inspired by recent work in deep self-attention and residual networks. Our proposals improve model performance across question types and input lengths, especially for long questions that requires the ability to capture long-term dependencies. On the Stanford Question Answering Dataset, our model achieves state of the art results with 75.1% exact match accuracy and 83.1% F1, while the ensemble obtains 78.9% exact match accuracy and 86.0% F1.", "output": [ "We introduce the DCN+ with deep residual coattention and mixed-objective RL, which achieves state of the art performance on the Stanford Question Answering Dataset." ] }, { "id": "task668-3212d02b3cd74499a141b37821bfcbfb", "input": "This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system. Specifically, we study applying image transformations such as bit-depth reduction, JPEG compression, total variance minimization, and image quilting before feeding the image to a convolutional network classifier. Our experiments on ImageNet show that total variance minimization and image quilting are very effective defenses in practice, in particular, when the network is trained on transformed images. The strength of those defenses lies in their non-differentiable nature and their inherent randomness, which makes it difficult for an adversary to circumvent the defenses. Our best defense eliminates 60% of strong gray-box and 90% of strong black-box attacks by a variety of major attack methods.", "output": [ "We apply a model-agnostic defense strategy against adversarial examples and achieve 60% white-box accuracy and 90% black-box accuracy against major attack algorithms." ] }, { "id": "task668-8c9f801f968542138c78b29c615aa63f", "input": "It has been widely recognized that adversarial examples can be easily crafted to fool deep networks, which mainly root from the locally non-linear behavior nearby input examples. Applying mixup in training provides an effective mechanism to improve generalization performance and model robustness against adversarial perturbations, which introduces the globally linear behavior in-between training examples. However, in previous work, the mixup-trained models only passively defend adversarial attacks in inference by directly classifying the inputs, where the induced global linearity is not well exploited. Namely, since the locality of the adversarial perturbations, it would be more efficient to actively break the locality via the globality of the model predictions. Inspired by simple geometric intuition, we develop an inference principle, named mixup inference (MI), for mixup-trained models. MI mixups the input with other random clean samples, which can shrink and transfer the equivalent perturbation if the input is adversarial. Our experiments on CIFAR-10 and CIFAR-100 demonstrate that MI can further improve the adversarial robustness for the models trained by mixup and its variants.", "output": [ "We exploit the global linearity of the mixup-trained models in inference to break the locality of the adversarial perturbations." ] }, { "id": "task668-44ae3fe223024c478235da0f4558d234", "input": "We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the \"learning to search\" (L2S) approach to structured prediction. RNNs have been widely successful in structured prediction applications such as machine translation or parsing, and are commonly trained using maximum likelihood estimation (MLE). Unfortunately, this training loss is not always an appropriate surrogate for the test error: by only maximizing the ground truth probability, it fails to exploit the wealth of information offered by structured losses. Further, it introduces discrepancies between training and predicting (such as exposure bias) that may hurt test performance. Instead, SEARNN leverages test-alike search space exploration to introduce global-local losses that are closer to the test error. We first demonstrate improved performance over MLE on two different tasks: OCR and spelling correction. Then, we propose a subsampling strategy to enable SEARNN to scale to large vocabulary sizes. This allows us to validate the benefits of our approach on a machine translation task.", "output": [ "We introduce SeaRNN, a novel algorithm for RNN training, inspired by the learning to search approach to structured prediction, in order to avoid the limitations of MLE training." ] }, { "id": "task668-4148b1a00bf14cfb8aa06c5bbaff0b5c", "input": "Stability is a key aspect of data analysis. In many applications, the natural notion of stability is geometric, as illustrated for example in computer vision. Scattering transforms construct deep convolutional representations which are certified stable to input deformations. This stability to deformations can be interpreted as stability with respect to changes in the metric structure of the domain. \n\n In this work, we show that scattering transforms can be generalized to non-Euclidean domains using diffusion wavelets, while preserving a notion of stability with respect to metric changes in the domain, measured with diffusion maps. The resulting representation is stable to metric perturbations of the domain while being able to capture ''high-frequency'' information, akin to the Euclidean Scattering.", "output": [ "Stability of scattering transform representations of graph data to deformations of the underlying graph support." ] }, { "id": "task668-946b7865695e4bb99a5be1b85f0f0b5b", "input": "In this paper, we introduce Symplectic ODE-Net (SymODEN), a deep learning framework which can infer the dynamics of a physical system from observed state trajectories. To achieve better generalization with fewer training samples, SymODEN incorporates appropriate inductive bias by designing the associated computation graph in a physics-informed manner. In particular, we enforce Hamiltonian dynamics with control to learn the underlying dynamics in a transparent way which can then be leveraged to draw insight about relevant physical aspects of the system, such as mass and potential energy. In addition, we propose a parametrization which can enforce this Hamiltonian formalism even when the generalized coordinate data is embedded in a high-dimensional space or we can only access velocity data instead of generalized momentum. This framework, by offering interpretable, physically-consistent models for physical systems, opens up new possibilities for synthesizing model-based control strategies.", "output": [ "This work enforces Hamiltonian dynamics with control to learn system models from embedded position and velocity data, and exploits this physically-consistent dynamics to synthesize model-based control via energy shaping." ] }, { "id": "task668-f0b691a6e73442188e651b45cd36c19e", "input": "Since deep neural networks are over-parameterized, they can memorize noisy examples. We address such memorizing issue in the presence of annotation noise. From the fact that deep neural networks cannot generalize neighborhoods of the features acquired via memorization, we hypothesize that noisy examples do not consistently incur small losses on the network under a certain perturbation. Based on this, we propose a novel training method called Learning with Ensemble Consensus (LEC) that prevents overfitting noisy examples by eliminating them using the consensus of an ensemble of perturbed networks. One of the proposed LECs, LTEC outperforms the current state-of-the-art methods on noisy MNIST, CIFAR-10, and CIFAR-100 in an efficient manner.", "output": [ "This work presents a method of generating and using ensembles effectively to identify noisy examples in the presence of annotation noise. " ] }, { "id": "task668-f737720e75a145f3b456f911fab5bac3", "input": "Random Matrix Theory (RMT) is applied to analyze the weight matrices of Deep Neural Networks (DNNs), including both production quality, pre-trained models such as AlexNet and Inception, and smaller models trained from scratch, such as LeNet5 and a miniature-AlexNet. Empirical and theoretical results clearly indicate that the empirical spectral density (ESD) of DNN layer matrices displays signatures of traditionally-regularized statistical models, even in the absence of exogenously specifying traditional forms of regularization, such as Dropout or Weight Norm constraints. Building on recent results in RMT, most notably its extension to Universality classes of Heavy-Tailed matrices, we develop a theory to identify 5+1 Phases of Training, corresponding to increasing amounts of Implicit Self-Regularization. For smaller and/or older DNNs, this Implicit Self-Regularization is like traditional Tikhonov regularization, in that there is a \"size scale\" separating signal from noise. For state-of-the-art DNNs, however, we identify a novel form of Heavy-Tailed Self-Regularization, similar to the self-organization seen in the statistical physics of disordered systems. This implicit Self-Regularization can depend strongly on the many knobs of the training process. By exploiting the generalization gap phenomena, we demonstrate that we can cause a small model to exhibit all 5+1 phases of training simply by changing the batch size.", "output": [ "See the abstract. (For the revision, the paper is identical, except for a 59 page Supplementary Material, which can serve as a stand-along technical report version of the paper.)" ] }, { "id": "task668-fd5a45907c7648adba5239700d647fc6", "input": "We argue that the widely used Omniglot and miniImageNet benchmarks are too simple because their class semantics do not vary across episodes, which defeats their intended purpose of evaluating few-shot classification methods. The class semantics of Omniglot is invariably “characters” and the class semantics of miniImageNet, “object category”. Because the class semantics are so similar, we propose a new method called Centroid Networks which can achieve surprisingly high accuracies on Omniglot and miniImageNet without using any labels at metaevaluation time. Our results suggest that those benchmarks are not adapted for supervised few-shot classification since the supervision itself is not necessary during meta-evaluation. The Meta-Dataset, a collection of 10 datasets, was recently proposed as a harder few-shot classification benchmark. Using our method, we derive a new metric, the Class Semantics Consistency Criterion, and use it to quantify the difficulty of Meta-Dataset. Finally, under some restrictive assumptions, we show that Centroid Networks is faster and more accurate than a state-of-the-art learning-to-cluster method (Hsu et al., 2018).", "output": [ "Omniglot and miniImageNet are too simple for few-shot learning because we can solve them without using labels during meta-evaluation, as demonstrated with a method called centroid networks" ] }, { "id": "task668-550fcd73803a4cc79e4a5cee93a64b03", "input": "Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data. Applications of spectral GCNs have been successful, but limited to a few problems where the graph is fixed, such as shape correspondence and node classification. In this work, we address this limitation by revisiting a particular family of spectral graph networks, Chebyshev GCNs, showing its efficacy in solving graph classification tasks with a variable graph structure and size. Current GCNs also restrict graphs to have at most one edge between any pair of nodes. To this end, we propose a novel multigraph network that learns from multi-relational graphs. We explicitly model different types of edges: annotated edges, learned edges with abstract meaning, and hierarchical edges. We also experiment with different ways to fuse the representations extracted from different edge types. This restriction is sometimes implied from a dataset, however, we relax this restriction for all kinds of datasets. We achieve state-of-the-art results on a variety of chemical, social, and vision graph classification benchmarks.", "output": [ "A novel approach to graph classification based on spectral graph convolutional networks and its extension to multigraphs with learnable relations and hierarchical structure. We show state-of-the art results on chemical, social and image datasets." ] }, { "id": "task668-8fce871aa28f4cfd90553088b1ae76e8", "input": "We present Compositional Attention Networks, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning. While many types of neural networks are effective at learning and generalizing from massive quantities of data, this model moves away from monolithic black-box architectures towards a design that provides a strong prior for iterative reasoning, enabling it to support explainable and structured learning, as well as generalization from a modest amount of data. The model builds on the great success of existing recurrent cells such as LSTMs: It sequences a single recurrent Memory, Attention, and Control (MAC) cell, and by careful design imposes structural constraints on the operation of each cell and the interactions between them, incorporating explicit control and soft attention mechanisms into their interfaces. We demonstrate the model's strength and robustness on the challenging CLEVR dataset for visual reasoning, achieving a new state-of-the-art 98.9% accuracy, halving the error rate of the previous best model. More importantly, we show that the new model is more computationally efficient, data-efficient, and requires an order of magnitude less time and/or data to achieve good results.", "output": [ "We present a novel architecture, based on dynamic memory, attention and composition for the task of machine reasoning." ] }, { "id": "task668-a9187e1109474109b405d17a92032244", "input": "The complex world around us is inherently multimodal and sequential (continuous). Information is scattered across different modalities and requires multiple continuous sensors to be captured. As machine learning leaps towards better generalization to real world, multimodal sequential learning becomes a fundamental research area. Arguably, modeling arbitrarily distributed spatio-temporal dynamics within and across modalities is the biggest challenge in this research area. In this paper, we present a new transformer model, called the Factorized Multimodal Transformer (FMT) for multimodal sequential learning. FMT inherently models the intramodal and intermodal (involving two or more modalities) dynamics within its multimodal input in a factorized manner. The proposed factorization allows for increasing the number of self-attentions to better model the multimodal phenomena at hand; without encountering difficulties during training (e.g. overfitting) even on relatively low-resource setups. All the attention mechanisms within FMT have a full time-domain receptive field which allows them to asynchronously capture long-range multimodal dynamics. In our experiments we focus on datasets that contain the three commonly studied modalities of language, vision and acoustic. We perform a wide range of experiments, spanning across 3 well-studied datasets and 21 distinct labels. FMT shows superior performance over previously proposed models, setting new state of the art in the studied datasets.", "output": [ "A multimodal transformer for multimodal sequential learning, with strong empirical results on multimodal language metrics such as multimodal sentiment analysis, emotion recognition and personality traits recognition. " ] }, { "id": "task668-3d0465004ca64a40adfaa65e274d8311", "input": "Deep learning models require extensive architecture design exploration and hyperparameter optimization to perform well on a given task. The exploration of the model design space is often made by a human expert, and optimized using a combination of grid search and search heuristics over a large space of possible choices. Neural Architecture Search (NAS) is a Reinforcement Learning approach that has been proposed to automate architecture design. NAS has been successfully applied to generate Neural Networks that rival the best human-designed architectures. However, NAS requires sampling, constructing, and training hundreds to thousands of models to achieve well-performing architectures. This procedure needs to be executed from scratch for each new task. The application of NAS to a wide set of tasks currently lacks a way to transfer generalizable knowledge across tasks.\n In this paper, we present the Multitask Neural Model Search (MNMS) controller. Our goal is to learn a generalizable framework that can condition model construction on successful model searches for previously seen tasks, thus significantly speeding up the search for new tasks. We demonstrate that MNMS can conduct an automated architecture search for multiple tasks simultaneously while still learning well-performing, specialized models for each task. We then show that pre-trained MNMS controllers can transfer learning to new tasks. By leveraging knowledge from previous searches, we find that pre-trained MNMS models start from a better location in the search space and reduce search time on unseen tasks, while still discovering models that outperform published human-designed models.", "output": [ "We present Multitask Neural Model Search, a Meta-learner that can design models for multiple tasks simultaneously and transfer learning to unseen tasks.", "This paper extends Neural Architecture Search to the multi-task learning problem where a task conditioned model search controller is learned to handle multiple tasks simultaneously.", "In this paper, authors summarize their work on building a framework, called Multitask Neural Model Search controller, for automated neural network construction across multiple tasks simultaneously." ] }, { "id": "task668-b92e50d70f1c4be292342973a3f3f81b", "input": "We consider the problem of unsupervised learning of a low dimensional, interpretable, latent state of a video containing a moving object. The problem of distilling dynamics from pixels has been extensively considered through the lens of graphical/state space models that exploit Markov structure for cheap computation and structured graphical model priors for enforcing interpretability on latent representations. We take a step towards extending these approaches by discarding the Markov structure; instead, repurposing the recently proposed Gaussian Process Prior Variational Autoencoder for learning sophisticated latent trajectories. We describe the model and perform experiments on a synthetic dataset and see that the model reliably reconstructs smooth dynamics exhibiting U-turns and loops. We also observe that this model may be trained without any beta-annealing or freeze-thaw of training parameters. Training is performed purely end-to-end on the unmodified evidence lower bound objective. This is in contrast to previous works, albeit for slightly different use cases, where application specific training tricks are often required.", "output": [ "We learn sohpisticated trajectories of an object purely from pixels with a toy video dataset by using a VAE structure with a Gaussian process prior." ] }, { "id": "task668-cc006881cea1426594916e5328f6a7a5", "input": "Identifying the hypernym relations that hold between words is a fundamental task in NLP. Word embedding methods have recently shown some capability to encode hypernymy. However, such methods tend not to explicitly encode the hypernym hierarchy that exists between words. In this paper, we propose a method to learn a hierarchical word embedding in a specific order to capture the hypernymy. To learn the word embeddings, the proposed method considers not only the hypernym relations that exists between words on a taxonomy, but also their contextual information in a large text corpus. The experimental results on a supervised hypernymy detection and a newly-proposed hierarchical path completion tasks show the ability of the proposed method to encode the hierarchy. Moreover, the proposed method outperforms previously proposed methods for learning word and hypernym-specific word embeddings on multiple benchmarks.", "output": [ "We presented a method to jointly learn a Hierarchical Word Embedding (HWE) using a corpus and a taxonomy for identifying the hypernymy relations between words.", "The paper presents a method to jointly learn word embeddings using co-occurrence statistics as well as incorporating hierarchal information from semantic networks.", "This paper proposed a joint learning method of hypernym from both raw text and supervised taxonomy data. ", "This paper proposes adding a measure of \"distributional inclusion\" difference to the GloVE objective for the purpose of representing hypernym relations." ] }, { "id": "task668-64421f9431ad4060889c66107c5dd925", "input": "Graph neural networks have recently achieved great successes in predicting quantum mechanical properties of molecules. These models represent a molecule as a graph using only the distance between atoms (nodes) and not the spatial direction from one atom to another. However, directional information plays a central role in empirical potentials for molecules, e.g. in angular potentials. To alleviate this limitation we propose directional message passing, in which we embed the messages passed between atoms instead of the atoms themselves. Each message is associated with a direction in coordinate space. These directional message embeddings are rotationally equivariant since the associated directions rotate with the molecule. We propose a message passing scheme analogous to belief propagation, which uses the directional information by transforming messages based on the angle between them. Additionally, we use spherical Bessel functions to construct a theoretically well-founded, orthogonal radial basis that achieves better performance than the currently prevalent Gaussian radial basis functions while using more than 4x fewer parameters. We leverage these innovations to construct the directional message passing neural network (DimeNet). DimeNet outperforms previous GNNs on average by 77% on MD17 and by 41% on QM9.", "output": [ "Directional message passing incorporates spatial directional information to improve graph neural networks." ] }, { "id": "task668-96d383fb97e340d680799005da87db89", "input": "Knowledge extraction techniques are used to convert neural networks into symbolic descriptions with the objective of producing more comprehensible learning models. The central challenge is to find an explanation which is more comprehensible than the original model while still representing that model faithfully. The distributed nature of deep networks has led many to believe that the hidden features of a neural network cannot be explained by logical descriptions simple enough to be understood by humans, and that decompositional knowledge extraction should be abandoned in favour of other methods. In this paper we examine this question systematically by proposing a knowledge extraction method using \\textit{M-of-N} rules which allows us to map the complexity/accuracy landscape of rules describing hidden features in a Convolutional Neural Network (CNN). Experiments reported in this paper show that the shape of this landscape reveals an optimal trade off between comprehensibility and accuracy, showing that each latent variable has an optimal \\textit{M-of-N} rule to describe its behaviour. We find that the rules with optimal tradeoff in the first and final layer have a high degree of explainability whereas the rules with the optimal tradeoff in the second and third layer are less explainable. The results shed light on the feasibility of rule extraction from deep networks, and point to the value of decompositional knowledge extraction as a method of explainability.", "output": [ "Systematically examines how well we can explain the hidden features of a deep network in terms of logical rules." ] }, { "id": "task668-5633d4de8478464cb158646b3d38b4f5", "input": "Reading comprehension is a challenging task, especially when executed across longer or across multiple evidence documents, where the answer is likely to reoccur. Existing neural architectures typically do not scale to the entire evidence, and hence, resort to selecting a single passage in the document (either via truncation or other means), and carefully searching for the answer within that passage. However, in some cases, this strategy can be suboptimal, since by focusing on a specific passage, it becomes difficult to leverage multiple mentions of the same answer throughout the document. In this work, we take a different approach by constructing lightweight models that are combined in a cascade to find the answer. Each submodel consists only of feed-forward networks equipped with an attention mechanism, making it trivially parallelizable. We show that our approach can scale to approximately an order of magnitude larger evidence documents and can aggregate information from multiple mentions of each answer candidate across the document. Empirically, our approach achieves state-of-the-art performance on both the Wikipedia and web domains of the TriviaQA dataset, outperforming more complex, recurrent architectures.", "output": [ "We propose neural cascades, a simple and trivially parallelizable approach to reading comprehension, consisting only of feed-forward nets and attention that achieves state-of-the-art performance on the TriviaQA dataset." ] }, { "id": "task668-3084fbf05b1945d7b2c7e4d5a63dc1bc", "input": "Recent image style transferring methods achieved arbitrary stylization with input content and style images. To transfer the style of an arbitrary image to a content image, these methods used a feed-forward network with a lowest-scaled feature transformer or a cascade of the networks with a feature transformer of a corresponding scale. However, their approaches did not consider either multi-scaled style in their single-scale feature transformer or dependency between the transformed feature statistics across the cascade networks. This shortcoming resulted in generating partially and inexactly transferred style in the generated images.\n To overcome this limitation of partial style transfer, we propose a total style transferring method which transfers multi-scaled feature statistics through a single feed-forward process. First, our method transforms multi-scaled feature maps of a content image into those of a target style image by considering both inter-channel correlations in each single scaled feature map and inter-scale correlations between multi-scaled feature maps. Second, each transformed feature map is inserted into the decoder layer of the corresponding scale using skip-connection. Finally, the skip-connected multi-scaled feature maps are decoded into a stylized image through our trained decoder network.", "output": [ "A paper suggesting a method to transform the style of images using deep neural networks." ] }, { "id": "task668-5c22b40082a048d8a19613d933aa0964", "input": "We propose a learned image-guided rendering technique that combines the benefits of image-based rendering and GAN-based image synthesis. The goal of our method is to generate photo-realistic re-renderings of reconstructed objects for virtual and augmented reality applications (e.g., virtual showrooms, virtual tours and sightseeing, the digital inspection of historical artifacts). A core component of our work is the handling of view-dependent effects. Specifically, we directly train an object-specific deep neural network to synthesize the view-dependent appearance of an object.\n As input data we are using an RGB video of the object. This video is used to reconstruct a proxy geometry of the object via multi-view stereo. Based on this 3D proxy, the appearance of a captured view can be warped into a new target view as in classical image-based rendering. This warping assumes diffuse surfaces, in case of view-dependent effects, such as specular highlights, it leads to artifacts. To this end, we propose EffectsNet, a deep neural network that predicts view-dependent effects. Based on these estimations, we are able to convert observed images to diffuse images. These diffuse images can be projected into other views. In the target view, our pipeline reinserts the new view-dependent effects. To composite multiple reprojected images to a final output, we learn a composition network that outputs photo-realistic results. Using this image-guided approach, the network does not have to allocate capacity on ``remembering'' object appearance, instead it learns how to combine the appearance of captured images. We demonstrate the effectiveness of our approach both qualitatively and quantitatively on synthetic as well as on real data.", "output": [ "We propose a learned image-guided rendering technique that combines the benefits of image-based rendering and GAN-based image synthesis while considering view-dependent effects.", "This submission proposes a method to handle view-dependent effects in neural rendering, which improves the robustness of existing neural rendering methods." ] }, { "id": "task668-7b810fff83ac4541814c67b127b75724", "input": "We propose a method, called Label Embedding Network, which can learn label representation (label embedding) during the training process of deep networks. With the proposed method, the label embedding is adaptively and automatically learned through back propagation. The original one-hot represented loss function is converted into a new loss function with soft distributions, such that the originally unrelated labels have continuous interactions with each other during the training process. As a result, the trained model can achieve substantially higher accuracy and with faster convergence speed. Experimental results based on competitive tasks demonstrate the effectiveness of the proposed method, and the learned label embedding is reasonable and interpretable. The proposed method achieves comparable or even better results than the state-of-the-art systems.", "output": [ "Learning Label Representation for Deep Networks" ] }, { "id": "task668-b49e90cc64884a3592b9b30812b4eb63", "input": "Network quantization is a model compression and acceleration technique that has become essential to neural network deployment. Most quantization methods per- form fine-tuning on a pretrained network, but this sometimes results in a large loss in accuracy compared to the original network. We introduce a new technique to train quantization-friendly networks, which can be directly converted to an accurate quantized network without the need for additional fine-tuning. Our technique allows quantizing the weights and activations of all network layers down to 4 bits, achieving high efficiency and facilitating deployment in practical settings. Com- pared to other fully quantized networks operating at 4 bits, we show substantial improvements in accuracy, for example 66.68% top-1 accuracy on ImageNet using ResNet-18, compared to the previous state-of-the-art accuracy of 61.52% Louizos et al. (2019) and a full precision reference accuracy of 69.76%. We performed a thorough set of experiments to test the efficacy of our method and also conducted ablation studies on different aspects of the method and techniques to improve training stability and accuracy. Our codebase and trained models are available on GitHub.", "output": [ "We train accurate fully quantized networks using a loss function maximizing full precision model accuracy and minimizing the difference between the full precision and quantized networks." ] }, { "id": "task668-466eea835f0c48478a1551426e4c3d41", "input": "Pattern databases are the foundation of some of the strongest admissible heuristics for optimal classical planning. Experiments showed that the most informative way of combining information from multiple pattern databases is to use saturated cost partitioning. Previous work selected patterns and computed saturated cost partitionings over the resulting pattern database heuristics in two separate steps. We introduce a new method that uses saturated cost partitioning to select patterns and show that it outperforms all existing pattern selection algorithms.", "output": [ "Using saturated cost partitioning to select patterns is preferable to all existing pattern selection algorithms." ] }, { "id": "task668-f45734648d484e87ba0c17839582006d", "input": "We develop a novel and efficient algorithm for optimizing neural networks inspired by a recently proposed geodesic optimization algorithm. Our algorithm, which we call Stochastic Geodesic Optimization (SGeO), utilizes an adaptive coefficient on top of Polyak's Heavy Ball method effectively controlling the amount of weight put on the previous update to the parameters based on the change of direction in the optimization path. Experimental results on strongly convex functions with Lipschitz gradients and deep Autoencoder benchmarks show that SGeO reaches lower errors than established first-order methods and competes well with lower or similar errors to a recent second-order method called K-FAC (Kronecker-Factored Approximate Curvature). We also incorporate Nesterov style lookahead gradient into our algorithm (SGeO-N) and observe notable improvements.", "output": [ "We utilize an adaptive coefficient on top of regular momentum inspired by geodesic optimization which significantly speeds up training in both convex and non-convex functions." ] }, { "id": "task668-e55a494049a44711a383b0bf31efbbda", "input": "Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences. In this work, we take this research direction to the extreme and investigate whether it is possible to learn to translate even without any parallel data. We propose a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space. By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data. We demonstrate our model on two widely used datasets and two language pairs, reporting BLEU scores of 32.8 and 15.1 on the Multi30k and WMT English-French datasets, without using even a single parallel sentence at training time.", "output": [ "We propose a new unsupervised machine translation model that can learn without using parallel corpora; experimental results show impressive performance on multiple corpora and pairs of languages." ] }, { "id": "task668-53c252e485e84b3a822e003220a89446", "input": "We propose an effective multitask learning setup for reducing distant supervision noise by leveraging sentence-level supervision. We show how sentence-level supervision can be used to improve the encoding of individual sentences, and to learn which input sentences are more likely to express the relationship between a pair of entities. We also introduce a novel neural architecture for collecting signals from multiple input sentences, which combines the benefits of attention and maxpooling. The proposed method increases AUC by 10% (from 0.261 to 0.284), and outperforms recently published results on the FB-NYT dataset.", "output": [ "A new form of attention that works well for the distant supervision setting, and a multitask learning approach to add sentence-level annotations. " ] }, { "id": "task668-0d1607a7d5eb4bea91540452df1b7ca7", "input": "The travelling salesman problem (TSP) is a well-known combinatorial optimization problem with a variety of real-life applications. We tackle TSP by incorporating machine learning methodology and leveraging the variable neighborhood search strategy. More precisely, the search process is considered as a Markov decision process (MDP), where a 2-opt local search is used to search within a small neighborhood, while a Monte Carlo tree search (MCTS) method (which iterates through simulation, selection and back-propagation steps), is used to sample a number of targeted actions within an enlarged neighborhood. This new paradigm clearly distinguishes itself from the existing machine learning (ML) based paradigms for solving the TSP, which either uses an end-to-end ML model, or simply applies traditional techniques after ML for post optimization. Experiments based on two public data sets show that, our approach clearly dominates all the existing learning based TSP algorithms in terms of performance, demonstrating its high potential on the TSP. More importantly, as a general framework without complicated hand-crafted rules, it can be readily extended to many other combinatorial optimization problems.", "output": [ "This paper combines Monte Carlo tree search with 2-opt local search in a variable neighborhood mode to solve the TSP effectively." ] }, { "id": "task668-725120e56d404fecaa47a0d3a94c3ef6", "input": "Artificial neural networks are built on the basic operation of linear combination and non-linear activation function. Theoretically this structure can approximate any continuous function with three layer architecture. But in practice learning the parameters of such network can be hard. Also the choice of activation function can greatly impact the performance of the network. In this paper we are proposing to replace the basic linear combination operation with non-linear operations that do away with the need of additional non-linear activation function. To this end we are proposing the use of elementary morphological operations (dilation and erosion) as the basic operation in neurons. We show that these networks (Denoted as Morph-Net) with morphological operations can approximate any smooth function requiring less number of parameters than what is necessary for normal neural networks. The results show that our network perform favorably when compared with similar structured network. We have carried out our experiments on MNIST, Fashion-MNIST, CIFAR10 and CIFAR100.", "output": [ "Using mophological operation (dilation and erosion) we have defined a class of network which can approximate any continious function. ", "This paper proposes to replace standard RELU/tanh units with a combination of dilation and erosion operations, observing that the new operator creates more hyper-planes and has more expressive power.", "The authors introduce Morph-Net, a single layer neural network where the mapping is performed using morphological dilation and erosion." ] }, { "id": "task668-27f6ab3ec93e446082bb8d00b2f888ad", "input": "Many problems with large-scale labeled training data have been impressively solved by deep learning. However, Unseen Class Categorization (UCC) with minimal information provided about target classes is the most commonly encountered setting in industry, which remains a challenging research problem in machine learning. Previous approaches to UCC either fail to generate a powerful discriminative feature extractor or fail to learn a flexible classifier that can be easily adapted to unseen classes. In this paper, we propose to address these issues through network reparameterization, \\textit{i.e.}, reparametrizing the learnable weights of a network as a function of other variables, by which we decouple the feature extraction part and the classification part of a deep classification model to suit the special setting of UCC, securing both strong discriminability and excellent adaptability. Extensive experiments for UCC on several widely-used benchmark datasets in the settings of zero-shot and few-shot learning demonstrate that, our method with network reparameterization achieves state-of-the-art performance.", "output": [ "A unified frame for both few-shot learning and zero-shot learning based on network reparameterization" ] }, { "id": "task668-9cd371c292734133943c850566619c96", "input": "Traditional set prediction models can struggle with simple datasets due to an issue we call the responsibility problem. We introduce a pooling method for sets of feature vectors based on sorting features across elements of the set. This can be used to construct a permutation-equivariant auto-encoder that avoids this responsibility problem. On a toy dataset of polygons and a set version of MNIST, we show that such an auto-encoder produces considerably better reconstructions and representations. Replacing the pooling function in existing set encoders with FSPool improves accuracy and convergence speed on a variety of datasets.", "output": [ "Sort in encoder and undo sorting in decoder to avoid responsibility problem in set auto-encoders" ] }, { "id": "task668-540e47db287c4d4bbe614c90f827627f", "input": "REINFORCE can be used to train models in structured prediction settings to directly optimize the test-time objective. However, the common case of sampling one prediction per datapoint (input) is data-inefficient. We show that by drawing multiple samples (predictions) per datapoint, we can learn with significantly less data, as we freely obtain a REINFORCE baseline to reduce variance. Additionally we derive a REINFORCE estimator with baseline, based on sampling without replacement. Combined with a recent technique to sample sequences without replacement using Stochastic Beam Search, this improves the training procedure for a sequence model that predicts the solution to the Travelling Salesman Problem.", "output": [ "We show that by drawing multiple samples (predictions) per input (datapoint), we can learn with less data as we freely obtain a REINFORCE baseline." ] }, { "id": "task668-dcdbe77134354a238933b838ae614637", "input": "While counter machines have received little attention in theoretical computer science since the 1960s, they have recently achieved a newfound relevance to the field of natural language processing (NLP). Recent work has suggested that some strong-performing recurrent neural networks utilize their memory as counters. Thus, one potential way to understand the sucess of these networks is to revisit the theory of counter computation. Therefore, we choose to study the abilities of real-time counter machines as formal grammars. We first show that several variants of the counter machine converge to express the same class of formal languages. We also prove that counter languages are closed under complement, union, intersection, and many other common set operations. Next, we show that counter machines cannot evaluate boolean expressions, even though they can weakly validate their syntax. This has implications for the interpretability and evaluation of neural network systems: successfully matching syntactic patterns does not guarantee that a counter-like model accurately represents underlying semantic structures. Finally, we consider the question of whether counter languages are semilinear. This work makes general contributions to the theory of formal languages that are of particular interest for the interpretability of recurrent neural networks.", "output": [ "We study the class of formal languages acceptable by real-time counter automata, a model of computation related to some types of recurrent neural networks." ] }, { "id": "task668-bb3330a4bdd24d02812f97229da1e55c", "input": "In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has featured meta-learning, in which a parameterized model for a learning algorithm is defined and trained on episodes representing different classification problems, each with a small labeled training set and its corresponding test set. In this work, we advance this few-shot classification paradigm towards a scenario where unlabeled examples are also available within each episode. We consider two situations: one where all unlabeled examples are assumed to belong to the same set of classes as the labeled examples of the episode, as well as the more challenging situation where examples from other distractor classes are also provided. To address this paradigm, we propose novel extensions of Prototypical Networks (Snell et al., 2017) that are augmented with the ability to use unlabeled examples when producing prototypes. These models are trained in an end-to-end way on episodes, to learn to leverage the unlabeled examples successfully. We evaluate these methods on versions of the Omniglot and miniImageNet benchmarks, adapted to this new framework augmented with unlabeled examples. We also propose a new split of ImageNet, consisting of a large set of classes, with a hierarchical structure. Our experiments confirm that our Prototypical Networks can learn to improve their predictions due to unlabeled examples, much like a semi-supervised algorithm would.", "output": [ "We propose novel extensions of Prototypical Networks that are augmented with the ability to use unlabeled examples when producing prototypes.", "This paper is an extension of a prototypical network that considers employing the unlabeled examples available to help train each episode", "Studies the problem of semi-supervised few-shot classification by extending the prototypical networks into the setting of semi-supervised learning with example from distractor classes", "Extends the Prototypical Network to the semi-supervised setting by updating prototypes using assigned pseudo-labels, dealing with distractors, and weighing samples using distance to the original prototypes." ] }, { "id": "task668-41e300758cbc4daab491b28a06d5cc32", "input": "Adversarial training is one of the main defenses against adversarial attacks. In this paper, we provide the first rigorous study on diagnosing elements of large-scale adversarial training on ImageNet, which reveals two intriguing properties. \n\n First, we study the role of normalization. Batch normalization (BN) is a crucial element for achieving state-of-the-art performance on many vision tasks, but we show it may prevent networks from obtaining strong robustness in adversarial training. One unexpected observation is that, for models trained with BN, simply removing clean images from training data largely boosts adversarial robustness, i.e., 18.3%. We relate this phenomenon to the hypothesis that clean images and adversarial images are drawn from two different domains. This two-domain hypothesis may explain the issue of BN when training with a mixture of clean and adversarial images, as estimating normalization statistics of this mixture distribution is challenging. Guided by this two-domain hypothesis, we show disentangling the mixture distribution for normalization, i.e., applying separate BNs to clean and adversarial images for statistics estimation, achieves much stronger robustness. Additionally, we find that enforcing BNs to behave consistently at training and testing can further enhance robustness.\n\n Second, we study the role of network capacity. We find our so-called \"deep\" networks are still shallow for the task of adversarial learning. Unlike traditional classification tasks where accuracy is only marginally improved by adding more layers to \"deep\" networks (e.g., ResNet-152), adversarial training exhibits a much stronger demand on deeper networks to achieve higher adversarial robustness. This robustness improvement can be observed substantially and consistently even by pushing the network capacity to an unprecedented scale, i.e., ResNet-638. \n", "output": [ "The first rigor diagnose of large-scale adversarial training on ImageNet" ] }, { "id": "task668-424030823e364eee938846c6f2fd9c8c", "input": "Deep reinforcement learning (RL) agents often fail to generalize to unseen environments (yet semantically similar to trained agents), particularly when they are trained on high-dimensional state spaces, such as images. In this paper, we propose a simple technique to improve a generalization ability of deep RL agents by introducing a randomized (convolutional) neural network that randomly perturbs input observations. It enables trained agents to adapt to new domains by learning robust features invariant across varied and randomized environments. Furthermore, we consider an inference method based on the Monte Carlo approximation to reduce the variance induced by this randomization. We demonstrate the superiority of our method across 2D CoinRun, 3D DeepMind Lab exploration and 3D robotics control tasks: it significantly outperforms various regularization and data augmentation methods for the same purpose.", "output": [ "We propose a simple randomization technique for improving generalization in deep reinforcement learning across tasks with various unseen visual patterns." ] }, { "id": "task668-694304762811489fb749090c96a66f56", "input": "Determinantal Point Processes (DPPs) provide an elegant and versatile way to sample sets of items that balance the point-wise quality with the set-wise diversity of selected items. For this reason, they have gained prominence in many machine learning applications that rely on subset selection. However, sampling from a DPP over a ground set of size N is a costly operation, requiring in general an O(N^3) preprocessing cost and an O(Nk^3) sampling cost for subsets of size k. We approach this problem by introducing DppNets: generative deep models that produce DPP-like samples for arbitrary ground sets. We develop an inhibitive attention mechanism based on transformer networks that captures a notion of dissimilarity between feature vectors. We show theoretically that such an approximation is sensible as it maintains the guarantees of inhibition or dissimilarity that makes DPP so powerful and unique. Empirically, we demonstrate that samples from our model receive high likelihood under the more expensive DPP alternative.", "output": [ "We approximate Determinantal Point Processes with neural nets; we justify our model theoretically and empirically." ] }, { "id": "task668-3f9ff8c685274b5dac3363bf6b1e7f9f", "input": "Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning. To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions. TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values. We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network. Both approaches are trained end-to-end, such that the learned model is optimised for its actual use in the tree. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al., 2017) on multiple Atari games. Furthermore, we present ablation studies that demonstrate the effect of different auxiliary losses on learning transition models.", "output": [ "We present TreeQN and ATreeC, new architectures for deep reinforcement learning in discrete-action domains that integrate differentiable on-line tree planning into the action-value function or policy." ] }, { "id": "task668-691e7e35988c41fa809530b79fba9d3b", "input": "Combining multiple function approximators in machine learning models typically leads to better performance and robustness compared with a single function. In reinforcement learning, ensemble algorithms such as an averaging method and a majority voting method are not always optimal, because each function can learn fundamentally different optimal trajectories from exploration. In this paper, we propose a Temporal Difference Weighted (TDW) algorithm, an ensemble method that adjusts weights of each contribution based on accumulated temporal difference errors. The advantage of this algorithm is that it improves ensemble performance by reducing weights of Q-functions unfamiliar with current trajectories. We provide experimental results for Gridworld tasks and Atari tasks that show significant performance improvements compared with baseline algorithms.", "output": [ "Ensemble method for reinforcement learning that weights Q-functions based on accumulated TD errors." ] }, { "id": "task668-4a25113337434532bbf61f99a20b0730", "input": "Recent literature has demonstrated promising results on the training of Generative Adversarial Networks by employing a set of discriminators, as opposed to the traditional game involving one generator against a single adversary. Those methods perform single-objective optimization on some simple consolidation of the losses, e.g. an average. In this work, we revisit the multiple-discriminator approach by framing the simultaneous minimization of losses provided by different models as a multi-objective optimization problem. Specifically, we evaluate the performance of multiple gradient descent and the hypervolume maximization algorithm on a number of different datasets. Moreover, we argue that the previously proposed methods and hypervolume maximization can all be seen as variations of multiple gradient descent in which the update direction computation can be done efficiently. Our results indicate that hypervolume maximization presents a better compromise between sample quality and diversity, and computational cost than previous methods.", "output": [ "We introduce hypervolume maximization for training GANs with multiple discriminators, showing performance improvements in terms of sample quality and diversity. " ] }, { "id": "task668-f7ea6329abd641b9a7f3b16707db4e8d", "input": "In this paper, we propose a novel kind of kernel, random forest kernel, to enhance the empirical performance of MMD GAN. Different from common forests with deterministic routings, a probabilistic routing variant is used in our innovated random-forest kernel, which is possible to merge with the CNN frameworks. Our proposed random-forest kernel has the following advantages: From the perspective of random forest, the output of GAN discriminator can be viewed as feature inputs to the forest, where each tree gets access to merely a fraction of the features, and thus the entire forest benefits from ensemble learning. In the aspect of kernel method, random-forest kernel is proved to be characteristic, and therefore suitable for the MMD structure. Besides, being an asymmetric kernel, our random-forest kernel is much more flexible, in terms of capturing the differences between distributions. Sharing the advantages of CNN, kernel method, and ensemble learning, our random-forest kernel based MMD GAN obtains desirable empirical performances on CIFAR-10, CelebA and LSUN bedroom data sets. Furthermore, for the sake of completeness, we also put forward comprehensive theoretical analysis to support our experimental results.", "output": [ "Equip MMD GANs with a new random-forest kernel." ] }, { "id": "task668-28bed74f4aa94168938c9867ed616929", "input": "Batch normalization (BN) is often used in an attempt to stabilize and accelerate training in deep neural networks. In many cases it indeed decreases the number of parameter updates required to achieve low training error. However, it also reduces robustness to small adversarial input perturbations and common corruptions by double-digit percentages, as we show on five standard datasets. Furthermore, we find that substituting weight decay for BN is sufficient to nullify a relationship between adversarial vulnerability and the input dimension. A recent mean-field analysis found that BN induces gradient explosion when used on multiple layers, but this cannot fully explain the vulnerability we observe, given that it occurs already for a single BN layer. We argue that the actual cause is the tilting of the decision boundary with respect to the nearest-centroid classifier along input dimensions of low variance. As a result, the constant introduced for numerical stability in the BN step acts as an important hyperparameter that can be tuned to recover some robustness at the cost of standard test accuracy. We explain this mechanism explicitly on a linear ``toy model and show in experiments that it still holds for nonlinear ``real-world models.", "output": [ "Batch normalization reduces robustness at test-time to common corruptions and adversarial examples." ] }, { "id": "task668-a211bab5fff440a2bb7fcf0367728bc3", "input": "There is growing interest in geometrically-inspired embeddings for learning hierarchies, partial orders, and lattice structures, with natural applications to transitive relational data such as entailment graphs. Recent work has extended these ideas beyond deterministic hierarchies to probabilistically calibrated models, which enable learning from uncertain supervision and inferring soft-inclusions among concepts, while maintaining the geometric inductive bias of hierarchical embedding models. We build on the Box Lattice model of Vilnis et al. (2018), which showed promising results in modeling soft-inclusions through an overlapping hierarchy of sets, parameterized as high-dimensional hyperrectangles (boxes). However, the hard edges of the boxes present difficulties for standard gradient based optimization; that work employed a special surrogate function for the disjoint case, but we find this method to be fragile. In this work, we present a novel hierarchical embedding model, inspired by a relaxation of box embeddings into parameterized density functions using Gaussian convolutions over the boxes. Our approach provides an alternative surrogate to the original lattice measure that improves the robustness of optimization in the disjoint case, while also preserving the desirable properties with respect to the original lattice. We demonstrate increased or matching performance on WordNet hypernymy prediction, Flickr caption entailment, and a MovieLens-based market basket dataset. We show especially marked improvements in the case of sparse data, where many conditional probabilities should be low, and thus boxes should be nearly disjoint.", "output": [ "Improve hierarchical embedding models using kernel smoothing" ] }, { "id": "task668-ddb860e2de164a8fb2aaa215a933cb4a", "input": "Many real applications show a great deal of interest in learning multiple tasks from different data sources/modalities with unbalanced samples and dimensions. Unfortunately, existing cutting-edge deep multi-task learning (MTL) approaches cannot be directly applied to these settings, due to either heterogeneous input dimensions or the heterogeneity in the optimal network architectures of different tasks. It is thus demanding to develop knowledge-sharing mechanism to handle the intrinsic discrepancies among network architectures across tasks. To this end, we propose a flexible knowledge-sharing framework for jointly learning multiple tasks from distinct data sources/modalities. The proposed framework allows each task to own its task (data)-specific network design, via utilizing a compact tensor representation, while the sharing is achieved through the partially shared latent cores. By providing more elaborate sharing control with latent cores, our framework is effective in transferring task-invariant knowledge, yet also being efficient in learning task-specific features. Experiments on both single and multiple data sources/modalities settings display the promising results of the proposed method, especially favourable in insufficient data scenarios.", "output": [ "a distributed latent-space based knowledge-sharing framework for deep multi-task learning" ] }, { "id": "task668-fdad58d48545404a9e317890577676c5", "input": "We present a simple proof for the benefit of depth in multi-layer feedforward network with rectifed activation (``\"depth separation\"). Specifically we present a sequence of classification problems f_i such that (a) for any fixed depth rectified network we can find an index m such that problems with index > m require exponential network width to fully represent the function f_m; and (b) for any problem f_m in the family, we present a concrete neural network with linear depth and bounded width that fully represents it.\n\n While there are several previous work showing similar results, our proof uses substantially simpler tools and techniques, and should be accessible to undergraduate students in computer science and people with similar backgrounds.", "output": [ "ReLU MLP depth seperation proof with gemoteric arguments", "A proof that deeper networks need less units than shallower ones for a family of problems. " ] }, { "id": "task668-73a984a9ec6449fba4b1264b61cbd877", "input": "This paper presents noise type/position classification of various impact noises generated in a building which is a serious conflict issue in apartment complexes. For this study, a collection of floor impact noise dataset is recorded with a single microphone. Noise types/positions are selected based on a report by the Floor Management Center under Korea Environmental Corporation. Using a convolutional neural networks based classifier, the impact noise signals converted to log-scaled Mel-spectrograms are classified into noise types or positions. Also, our model is evaluated on a standard environmental sound dataset ESC-50 to show extensibility on environmental sound classification.\n", "output": [ "This paper presents noise type/position classification of various impact noises generated in a building which is a serious conflict issue in apartment complexes", "This work describes the use of convolutional neural networks in a novel application area of building noise type and noise position classification. " ] }, { "id": "task668-f77032d675f041fba3261703979e2353", "input": "In this paper, we consider the problem of training neural networks (NN). To promote a NN with specific structures, we explicitly take into consideration the nonsmooth regularization (such as L1-norm) and constraints (such as interval constraint). This is formulated as a constrained nonsmooth nonconvex optimization problem, and we propose a convergent proximal-type stochastic gradient descent (Prox-SGD) algorithm. We show that under properly selected learning rates, momentum eventually resembles the unknown real gradient and thus is crucial in analyzing the convergence. We establish that with probability 1, every limit point of the sequence generated by the proposed Prox-SGD is a stationary point. Then the Prox-SGD is tailored to train a sparse neural network and a binary neural network, and the theoretical analysis is also supported by extensive numerical tests.", "output": [ "We propose a convergent proximal-type stochastic gradient descent algorithm for constrained nonsmooth nonconvex optimization problems", "This paper proposes Prox-SGD, a theoretical framework for stochastic optimization algorithms shown to converge asymptotically to stationarity for smooth non-convvex loss + convex constraint/regularizer.", "The paper proposes a new gradient-based stochastic optimization algorithm with gradient averaging by adapting theory for proximal algorithms to the non-convex setting." ] }, { "id": "task668-d75eeee9cf264ac98a129b0915d83e84", "input": "We study how, in generative adversarial networks, variance in the discriminator's output affects the generator's ability to learn the data distribution. In particular, we contrast the results from various well-known techniques for training GANs when the discriminator is near-optimal and updated multiple times per update to the generator. As an alternative, we propose an additional method to train GANs by explicitly modeling the discriminator's output as a bi-modal Gaussian distribution over the real/fake indicator variables. In order to do this, we train the Gaussian classifier to match the target bi-modal distribution implicitly through meta-adversarial training. We observe that our new method, when trained together with a strong discriminator, provides meaningful, non-vanishing gradients.", "output": [ "We introduce meta-adversarial learning, a new technique to regularize GANs, and propose a training method by explicitly controlling the discriminator's output distribution.", "The paper proposes variance regularizing adversarial learning for training GANs to ensure that the gradient for the generator does not vanish" ] }, { "id": "task668-c1557f8f4f2f4e1b82e7f704db8a36db", "input": "Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo with simple mechanisms for proposing new states, which hinders exploration efficiency and often requires long runs to deliver accurate posterior estimates. In this paper we present an alternative approach: a variational framework for Bayesian phylogenetic analysis. We approximate the true posterior using an expressive graphical model for tree distributions, called a subsplit Bayesian network, together with appropriate branch length distributions. We train the variational approximation via stochastic gradient ascent and adopt multi-sample based gradient estimators for different latent variables separately to handle the composite latent space of phylogenetic models. We show that our structured variational approximations are flexible enough to provide comparable posterior estimation to MCMC, while requiring less computation due to a more efficient tree exploration mechanism enabled by variational inference. Moreover, the variational approximations can be readily used for further statistical analysis such as marginal likelihood estimation for model comparison via importance sampling. Experiments on both synthetic data and real data Bayesian phylogenetic inference problems demonstrate the effectiveness and efficiency of our methods.", "output": [ "The first variational Bayes formulation of phylogenetic inference, a challenging inference problem over structures with intertwined discrete and continuous components", "Explores an approximate inference solution to the problem of Bayesian inference of phylogenetic trees by leveraging recently proposed subsplit Bayesian networks and modern gradient estimators for VI.", "Proposes a variational approach to Bayesian posterior inference in phylogenetic trees." ] }, { "id": "task668-f55b22af3ce94fcf955d43a4fcb98c5c", "input": "Graph convolutional networks (GCNs) have been widely used for classifying graph nodes in the semi-supervised setting.\n Previous works have shown that GCNs are vulnerable to the perturbation on adjacency and feature matrices of existing nodes. However, it is unrealistic to change the connections of existing nodes in many applications, such as existing users in social networks. In this paper, we investigate methods attacking GCNs by adding fake nodes. A greedy algorithm is proposed to generate adjacency and feature matrices of fake nodes, aiming to minimize the classification accuracy on the existing ones. In additional, we introduce a discriminator to classify fake nodes from real nodes, and propose a Greedy-GAN algorithm to simultaneously update the discriminator and the attacker, to make fake nodes indistinguishable to the real ones. Our non-targeted attack decreases the accuracy of GCN down to 0.10, and our targeted attack reaches a success rate of 0.99 for attacking the whole datasets, and 0.94 on average for attacking a single node.", "output": [ "non-targeted and targeted attack on GCN by adding fake nodes", "The authors propose a new adversarial technique to add \"fake\" nodes to fool a GCN-based classifier" ] }, { "id": "task668-b6fcb2ad11554bd98884a26844d77fe1", "input": "We study the statistical properties of the endpoint of stochastic gradient descent (SGD). We approximate SGD as a stochastic differential equation (SDE) and consider its Boltzmann Gibbs equilibrium distribution under the assumption of isotropic variance in loss gradients.. Through this analysis, we find that three factors – learning rate, batch size and the variance of the loss gradients – control the trade-off between the depth and width of the minima found by SGD, with wider minima favoured by a higher ratio of learning rate to batch size. In the equilibrium distribution only the ratio of learning rate to batch size appears, implying that it’s invariant under a simultaneous rescaling of each by the same amount. \nWe experimentally show how learning rate and batch size affect SGD from two perspectives: the endpoint of SGD and the dynamics that lead up to it. For the endpoint, the experiments suggest the endpoint of SGD is similar under simultaneous rescaling of batch size and learning rate, and also that a higher ratio leads to flatter minima, both findings are consistent with our theoretical analysis. We note experimentally that the dynamics also seem to be similar under the same rescaling of learning rate and batch size, which we explore showing that one can exchange batch size and learning rate in a cyclical learning rate schedule. Next, we illustrate how noise affects memorization, showing that high noise levels lead to better generalization. Finally, we find experimentally that the similarity under simultaneous rescaling of learning rate and batch size breaks down if the learning rate gets too large or the batch size gets too small.", "output": [ "Three factors (batch size, learning rate, gradient noise) change in predictable way the properties (e.g. sharpness) of minima found by SGD." ] }, { "id": "task668-7faf12db654b4b549522764702e69355", "input": "Neural networks are vulnerable to small adversarial perturbations. Existing literature largely focused on understanding and mitigating the vulnerability of learned models. In this paper, we demonstrate an intriguing phenomenon about the most popular robust training method in the literature, adversarial training: Adversarial robustness, unlike clean accuracy, is sensitive to the input data distribution. Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarial trained model that is both trained and evaluated on the new distribution. Our discovery of such sensitivity on data distribution is based on a study which disentangles the behaviors of clean accuracy and robust accuracy of the Bayes classifier. Empirical investigations further confirm our finding. We construct semantically-identical variants for MNIST and CIFAR10 respectively, and show that standardly trained models achieve comparable clean accuracies on them, but adversarially trained models achieve significantly different robustness accuracies. This counter-intuitive phenomenon indicates that input data distribution alone can affect the adversarial robustness of trained neural networks, not necessarily the tasks themselves. Lastly, we discuss the practical implications on evaluating adversarial robustness, and make initial attempts to understand this complex phenomenon.", "output": [ "Robustness performance of PGD trained models are sensitive to semantics-preserving transformation of image datasets, which implies the trickiness of evaluation of robust learning algorithms in practice.", "Paper clarifies the difference between clean and robust accuracy and shows that changing the marginal distribution of the input data P(x) while preserving its semantic P(y|x) affects the robustness of the model.", "This paper investigates the origin of the lack of robustness of classifiers to perturbations of adversarial inputs under l-inf bounded perturbations." ] }, { "id": "task668-8be154f96c7f4d07ad1183e6ecd78962", "input": "Recent work has introduced attacks that extract the architecture information of deep neural networks (DNN), as this knowledge enhances an adversary’s capability to conduct attacks on black-box networks. This paper presents the first in-depth security analysis of DNN fingerprinting attacks that exploit cache side-channels. First, we define the threat model for these attacks: our adversary does not need the ability to query the victim model; instead, she runs a co-located process on the host machine victim ’s deep learning (DL) system is running and passively monitors the accesses of the target functions in the shared framework. Second, we introduce DeepRecon, an attack that reconstructs the architecture of the victim network by using the internal information extracted via Flush+Reload, a cache side-channel technique. Once the attacker observes function invocations that map directly to architecture attributes of the victim network, the attacker can reconstruct the victim’s entire network architecture. In our evaluation, we demonstrate that an attacker can accurately reconstruct two complex networks (VGG19 and ResNet50) having only observed one forward propagation. Based on the extracted architecture attributes, we also demonstrate that an attacker can build a meta-model that accurately fingerprints the architecture and family of the pre-trained model in a transfer learning setting. From this meta-model, we evaluate the importance of the observed attributes in the fingerprinting process. Third, we propose and evaluate new framework-level defense techniques that obfuscate our attacker’s observations. Our empirical security analysis represents a step toward understanding the DNNs’ vulnerability to cache side-channel attacks.", "output": [ "We conduct the first in-depth security analysis of DNN fingerprinting attacks that exploit cache side-channels, which represents a step toward understanding the DNN’s vulnerability to side-channel attacks.", "This paper considers the problem of fingerprinting neural network architectures using cache side channels, and discusses security-through-obscurity defenses.", "This paper performs cache side-channel attacks to extract attributes of a victim model and infer its architecture, as well as show they can achieve a nearly perfect classification accuracy." ] }, { "id": "task668-4ab0acedaf01454dbbe68e78c80852be", "input": "Character-level language modeling is an essential but challenging task in Natural Language Processing. \n Prior works have focused on identifying long-term dependencies between characters and have built deeper and wider networks for better performance. However, their models require substantial computational resources, which hinders the usability of character-level language models in applications with limited resources. In this paper, we propose a lightweight model, called Group-Transformer, that reduces the resource requirements for a Transformer, a promising method for modeling sequence with long-term dependencies. Specifically, the proposed method partitions linear operations to reduce the number of parameters and computational cost. As a result, Group-Transformer only uses 18.2\\% of parameters compared to the best performing LSTM-based model, while providing better performance on two benchmark tasks, enwik8 and text8. When compared to Transformers with a comparable number of parameters and time complexity, the proposed model shows better performance. The implementation code will be available.", "output": [ "This paper proposes a novel lightweight Transformer for character-level language modeling, utilizing group-wise operations." ] }, { "id": "task668-72785a926e1247ee924d2cc2a813aae7", "input": "Imitation learning algorithms provide a simple and straightforward approach for training control policies via standard supervised learning methods. By maximizing the likelihood of good actions provided by an expert demonstrator, supervised imitation learning can produce effective policies without the algorithmic complexities and optimization challenges of reinforcement learning, at the cost of requiring an expert demonstrator -- typically a person -- to provide the demonstrations. In this paper, we ask: can we use imitation learning to train effective policies without any expert demonstrations? The key observation that makes this possible is that, in the multi-task setting, trajectories that are generated by a suboptimal policy can still serve as optimal examples for other tasks. In particular, in the setting where the tasks correspond to different goals, every trajectory is a successful demonstration for the state that it actually reaches. Informed by this observation, we propose a very simple algorithm for learning behaviors without any demonstrations, user-provided reward functions, or complex reinforcement learning methods. Our method simply maximizes the likelihood of actions the agent actually took in its own previous rollouts, conditioned on the goal being the state that it actually reached. Although related variants of this approach have been proposed previously in imitation learning settings with example demonstrations, we present the first instance of this approach as a method for learning goal-reaching policies entirely from scratch. We present a theoretical result linking self-supervised imitation learning and reinforcement learning, and empirical results showing that it performs competitively with more complex reinforcement learning methods on a range of challenging goal reaching problems.", "output": [ "Learning how to reach goals from scratch by using imitation learning with data relabeling" ] }, { "id": "task668-4051229c92f14e659287ecbdff490db5", "input": " Over the last few years, the phenomenon of adversarial examples --- maliciously constructed inputs that fool trained machine learning models --- has captured the attention of the research community, especially when the adversary is restricted to making small modifications of a correctly handled input. At the same time, less surprisingly, image classifiers lack human-level performance on randomly corrupted images, such as images with additive Gaussian noise. In this work, we show that these are two manifestations of the same underlying phenomenon. We establish this connection in several ways. First, we find that adversarial examples exist at the same distance scales we would expect from a linear model with the same performance on corrupted images. Next, we show that Gaussian data augmentation during training improves robustness to small adversarial perturbations and that adversarial training improves robustness to several types of image corruptions. Finally, we present a model-independent upper bound on the distance from a corrupted image to its nearest error given test performance and show that in practice we already come close to achieving the bound, so that improving robustness further for the corrupted image distribution requires significantly reducing test error. All of this suggests that improving adversarial robustness should go hand in hand with improving performance in the presence of more general and realistic image corruptions. This yields a computationally tractable evaluation metric for defenses to consider: test error in noisy image distributions.", "output": [ "Small adversarial perturbations should be expected given observed error rates of models outside the natural data distribution.", "This paper proposes an alternative view for adversarial examples in high dimension spaces by considering the \"error rate\" in a Gaussian distribution centered at each test point." ] }, { "id": "task668-020d3f97ffb34298aeedd51581c5b948", "input": "Recent advancements in deep learning techniques such as Convolutional Neural Networks(CNN) and Generative Adversarial Networks(GAN) have achieved breakthroughs in the problem of semantic image inpainting, the task of reconstructing missing pixels in given images. While much more effective than conventional approaches, deep learning models require large datasets and great computational resources for training, and inpainting quality varies considerably when training data vary in size and diversity. To address these problems, we present in this paper a inpainting strategy of \\textit{Comparative Sample Augmentation}, which enhances the quality of training set by filtering out irrelevant images and constructing additional images using information about the surrounding regions of the images to be inpainted. Experiments on multiple datasets demonstrate that our method extends the applicability of deep inpainting models to training sets with varying sizes, while maintaining inpainting quality as measured by qualitative and quantitative metrics for a large class of deep models, with little need for model-specific consideration.", "output": [ "We introduced a strategy which enables inpainting models on datasets of various sizes", "Help image inpainting using GANs by using a comparative augmenting filter and adding random noise to each pixel." ] }, { "id": "task668-4c07ffc93c06443a837a29d11f2a92e2", "input": "Recent efforts to combine Representation Learning with Formal Methods, commonly known as the Neuro-Symbolic Methods, have given rise to a new trend of applying rich neural architectures to solve classical combinatorial optimization problems. In this paper, we propose a neural framework that can learn to solve the Circuit Satisfiability problem. Our framework is built upon two fundamental contributions: a rich embedding architecture that encodes the problem structure and an end-to-end differentiable training procedure that mimics Reinforcement Learning and trains the model directly toward solving the SAT problem. The experimental results show the superior out-of-sample generalization performance of our framework compared to the recently developed NeuroSAT method.", "output": [ "We propose a neural framework that can learn to solve the Circuit Satisfiability problem from (unlabeled) circuit instances." ] }, { "id": "task668-acc3b923f6eb4aae93cae3366da8b8fa", "input": "Combining information from different sensory modalities to execute goal directed actions is a key aspect of human intelligence. Specifically, human agents are very easily able to translate the task communicated in one sensory domain (say vision) into a representation that enables them to complete this task when they can only sense their environment using a separate sensory modality (say touch). In order to build agents with similar capabilities, in this work we consider the problem of a retrieving a target object from a drawer. The agent is provided with an image of a previously unseen object and it explores objects in the drawer using only tactile sensing to retrieve the object that was shown in the image without receiving any visual feedback. Success at this task requires close integration of visual and tactile sensing. We present a method for performing this task in a simulated environment using an anthropomorphic hand. We hope that future research in the direction of combining sensory signals for acting will find the object retrieval from a drawer to be a useful benchmark problem", "output": [ "In this work, we study the problem of learning representations to identify novel objects by exploring objects using tactile sensing. Key point here is that the query is provided in image domain." ] }, { "id": "task668-0d918dfd1fbb4a81ac67f7f428c9a263", "input": "How can we teach artificial agents to use human language flexibly to solve problems in a real-world environment? We have one example in nature of agents being able to solve this problem: human babies eventually learn to use human language to solve problems, and they are taught with an adult human-in-the-loop. Unfortunately, current machine learning methods (e.g. from deep reinforcement learning) are too data inefficient to learn a language in this way (3). An outstanding goal is finding an algorithm with a suitable ‘language learning prior’ that allows it to learn human language, while minimizing the number of required human interactions.\n\n In this paper, we propose to learn such a prior in simulation, leveraging the increasing amount of available compute for machine learning experiments (1). We call our approach Learning to Learn to Communicate (L2C). Specifically, in L2C we train a meta-learning agent in simulation to interact with populations of pre-trained agents, each with their own distinct communication protocol. Once the meta-learning agent is able to quickly adapt to each population of agents, it can be deployed in new populations unseen during training, including populations of humans. To show the promise of the L2C framework, we conduct some preliminary experiments in a Lewis signaling game (4), where we show that agents\n trained with L2C are able to learn a simple form of human language (represented by a hand-coded compositional language) in fewer iterations than randomly initialized agents.", "output": [ "We propose to use meta-learning for more efficient language learning, via a kind of 'domain randomization'. " ] }, { "id": "task668-958571f6dd71488dae214d76befd9ffa", "input": "Multi-hop text-based question-answering is a current challenge in machine comprehension. \n This task requires to sequentially integrate facts from multiple passages to answer complex natural language questions.\n In this paper, we propose a novel architecture, called the Latent Question Reformulation Network (LQR-net), a multi-hop and parallel attentive network designed for question-answering tasks that require reasoning capabilities.\n LQR-net is composed of an association of \\textbf{reading modules} and \\textbf{reformulation modules}.\n The purpose of the reading module is to produce a question-aware representation of the document.\n From this document representation, the reformulation module extracts essential elements to calculate an updated representation of the question.\n This updated question is then passed to the following hop.\n We evaluate our architecture on the \\hotpotqa question-answering dataset designed to assess multi-hop reasoning capabilities.\n Our model achieves competitive results on the public leaderboard and outperforms the best current \\textit{published} models in terms of Exact Match (EM) and $F_1$ score.\n Finally, we show that an analysis of the sequential reformulations can provide interpretable reasoning paths.", "output": [ "In this paper, we propose the Latent Question Reformulation Network (LQR-net), a multi-hop and parallel attentive network designed for question-answering tasks that require reasoning capabilities." ] }, { "id": "task668-261ea8acb39a41d8bbb88017b3390676", "input": "Inverse reinforcement learning (IRL) is used to infer the reward function from the actions of an expert running a Markov Decision Process (MDP). A novel approach using variational inference for learning the reward function is proposed in this research. Using this technique, the intractable posterior distribution of the continuous latent variable (the reward function in this case) is analytically approximated to appear to be as close to the prior belief while trying to reconstruct the future state conditioned on the current state and action. The reward function is derived using a well-known deep generative model known as Conditional Variational Auto-encoder (CVAE) with Wasserstein loss function, thus referred to as Conditional Wasserstein Auto-encoder-IRL (CWAE-IRL), which can be analyzed as a combination of the backward and forward inference. This can then form an efficient alternative to the previous approaches to IRL while having no knowledge of the system dynamics of the agent. Experimental results on standard benchmarks such as objectworld and pendulum show that the proposed algorithm can effectively learn the latent reward function in complex, high-dimensional environments.", "output": [ "Using a supervised latent variable modeling framework to determine reward in inverse reinforcement learning task" ] }, { "id": "task668-47215ab695054565a131b279df543dcc", "input": "A significant challenge for the practical application of reinforcement learning toreal world problems is the need to specify an oracle reward function that correctly defines a task. Inverse reinforcement learning (IRL) seeks to avoid this challenge by instead inferring a reward function from expert behavior. While appealing, it can be impractically expensive to collect datasets of demonstrations that cover the variation common in the real world (e.g. opening any type of door). Thus in practice, IRL must commonly be performed with only a limited set of demonstrations where it can be exceedingly difficult to unambiguously recover a reward function. In this work, we exploit the insight that demonstrations from other tasks can be used to constrain the set of possible reward functions by learning a \"prior\" that is specifically optimized for the ability to infer expressive reward functions from limited numbers of demonstrations. We demonstrate that our method can efficiently recover rewards from images for novel tasks and provide intuition as to how our approach is analogous to learning a prior.", "output": [ "The applicability of inverse reinforcement learning is often hampered by the expense of collecting expert demonstrations; this paper seeks to broaden its applicability by incorporating prior task information through meta-learning." ] }, { "id": "task668-93a22a06c7b04fc99cff2430359f073a", "input": "The interpretability of neural networks has become crucial for their applications in real world with respect to the reliability and trustworthiness. Existing explanation generation methods usually provide important features by scoring their individual contributions to the model prediction and ignore the interactions between features, which eventually provide a bag-of-words representation as explanation. In natural language processing, this type of explanations is challenging for human user to understand the meaning of an explanation and draw the connection between explanation and model prediction, especially for long texts. In this work, we focus on detecting the interactions between features, and propose a novel approach to build a hierarchy of explanations based on feature interactions. The proposed method is evaluated with three neural classifiers, LSTM, CNN, and BERT, on two benchmark text classification datasets. The generated explanations are assessed by both automatic evaluation measurements and human evaluators. Experiments show the effectiveness of the proposed method in providing explanations that are both faithful to models, and understandable to humans.", "output": [ "A novel approach to construct hierarchical explanations for text classification by detecting feature interactions.", "A novel method for providing explanations for predicitions made by text classifiers that outperforms baselines on word level importance scores, and a new metric, cohesion loss, to evaluate span-level importance.", "An interpretation method based on feature interactions and feature importance score as compared to independent feature contributions." ] }, { "id": "task668-66a93257e70142afb43f2e0e9de21685", "input": "Deep learning has demonstrated abilities to learn complex structures, but they can be restricted by available data. Recently, Consensus Networks (CNs) were proposed to alleviate data sparsity by utilizing features from multiple modalities, but they too have been limited by the size of labeled data. In this paper, we extend CN to Transductive Consensus Networks (TCNs), suitable for semi-supervised learning. In TCNs, different modalities of input are compressed into latent representations, which we encourage to become indistinguishable during iterative adversarial training. To understand TCNs two mechanisms, consensus and classification, we put forward its three variants in ablation studies on these mechanisms. To further investigate TCN models, we treat the latent representations as probability distributions and measure their similarities as the negative relative Jensen-Shannon divergences. We show that a consensus state beneficial for classification desires a stable but imperfect similarity between the representations. Overall, TCNs outperform or align with the best benchmark algorithms given 20 to 200 labeled samples on the Bank Marketing and the DementiaBank datasets.", "output": [ "TCN for multimodal semi-supervised learning + ablation study of its mechanisms + interpretations of latent representations" ] }, { "id": "task668-b8bf3a8f27a248628e6f4008c702204b", "input": "We propose a new model for making generalizable and diverse retrosynthetic reaction predictions. Given a target compound, the task is to predict the likely chemical reactants to produce the target. This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules. Building on top of the popular Transformer architecture, we propose two novel pre-training methods that construct relevant auxiliary tasks (plausible reactions) for our problem. Furthermore, we incorporate a discrete latent variable model into the architecture to encourage the model to produce a diverse set of alternative predictions. On the 50k subset of reaction examples from the United States patent literature (USPTO-50k) benchmark dataset, our model greatly improves performance over the baseline, while also generating predictions that are more diverse.", "output": [ "We propose a new model for making generalizable and diverse retrosynthetic reaction predictions." ] }, { "id": "task668-713e16e9ebb74602a04bdae972b9accc", "input": "We present DL2, a system for training and querying neural networks with logical constraints. The key idea is to translate these constraints into a differentiable loss with desirable mathematical properties and to then either train with this loss in an iterative manner or to use the loss for querying the network for inputs subject to the constraints. We empirically demonstrate that DL2 is effective in both training and querying scenarios, across a range of constraints and data sets.", "output": [ "A differentiable loss for logic constraints for training and querying neural networks.", "A framework for turning queries over parameters and input, ouput pairs to neural networks into differentiable loss functions and an associated declarative language for specifying these queries", "This paper tackles the problem of combining logical approaches with neural networks by translating a logical formula into a non-negative loss function for a neural network." ] }, { "id": "task668-b3e0d1cc40364eceb45af1e2aa601e5b", "input": "In this work, we attempt to answer a critical question: whether there exists some input sequence that will cause a well-trained discrete-space neural network sequence-to-sequence (seq2seq) model to generate egregious outputs (aggressive, malicious, attacking, etc.). And if such inputs exist, how to find them efficiently. We adopt an empirical methodology, in which we first create lists of egregious output sequences, and then design a discrete optimization algorithm to find input sequences that will cause the model to generate them. Moreover, the optimization algorithm is enhanced for large vocabulary search and constrained to search for input sequences that are likely to be input by real-world users. In our experiments, we apply this approach to dialogue response generation models trained on three real-world dialogue data-sets: Ubuntu, Switchboard and OpenSubtitles, testing whether the model can generate malicious responses. We demonstrate that given the trigger inputs our algorithm finds, a significant number of malicious sentences are assigned large probability by the model, which reveals an undesirable consequence of standard seq2seq training.", "output": [ "This paper aims to provide an empirical answer to the question of whether well-trained dialogue response model can output malicious responses." ] }, { "id": "task668-ed5fc1fcb5e34306a2ae5c0102506559", "input": "Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency.\n They have been shown to be successful in image classification models, both in obtaining better models than previously possible for a given parameter count (the Xception architecture) and considerably reducing the number of parameters required to perform at a given level (the MobileNets family of architectures). Recently, convolutional sequence-to-sequence networks have been applied to machine translation tasks with good results. In this work, we study how depthwise separable convolutions can be applied to neural machine translation. We introduce a new architecture inspired by Xception and ByteNet, called SliceNet, which enables a significant reduction of the parameter count and amount of computation needed to obtain results like ByteNet, and, with a similar parameter count, achieves better results.\n In addition to showing that depthwise separable convolutions perform well for machine translation, we investigate the architectural changes that they enable: we observe that thanks to depthwise separability, we can increase the length of convolution windows, removing the need for filter dilation. We also introduce a new super-separable convolution operation that further reduces the number of parameters and computational cost of the models.", "output": [ "Depthwise separable convolutions improve neural machine translation: the more separable the better.", "This paper proposes to use depthwise separable convolution layers in a fully convolutional neural machine translation model, and introduces a new super-separable convolution layer which further reduces computational cost." ] }, { "id": "task668-fe12ef3c7ba640138f1726324d405ffb", "input": "\tIn this paper, we propose the Asynchronous Accelerated Nonuniform Randomized Block Coordinate Descent algorithm (A2BCD). We prove A2BCD converges linearly to a solution of the convex minimization problem at the same rate as NU_ACDM, so long as the maximum delay is not too large. This is the first asynchronous Nesterov-accelerated algorithm that attains any provable speedup. Moreover, we then prove that these algorithms both have optimal complexity. Asynchronous algorithms complete much faster iterations, and A2BCD has optimal complexity. Hence we observe in experiments that A2BCD is the top-performing coordinate descent algorithm, converging up to 4-5x faster than NU_ACDM on some data sets in terms of wall-clock time. To motivate our theory and proof techniques, we also derive and analyze a continuous-time analog of our algorithm and prove it converges at the same rate.", "output": [ "We prove the first-ever convergence proof of an asynchronous accelerated algorithm that attains a speedup." ] }, { "id": "task668-017827b91d254c58a822ee862d6e579d", "input": "The resemblance between the methods used in studying quantum-many body physics and in machine learning has drawn considerable attention. In particular, tensor networks (TNs) and deep learning architectures bear striking similarities to the extent that TNs can be used for machine learning. Previous results used one-dimensional TNs in image recognition, showing limited scalability and a request of high bond dimension. In this work, we train two-dimensional hierarchical TNs to solve image recognition problems, using a training algorithm derived from the multipartite entanglement renormalization ansatz (MERA). This approach overcomes scalability issues and implies novel mathematical connections among quantum many-body physics, quantum information theory, and machine learning. While keeping the TN unitary in the training phase, TN states can be defined, which optimally encodes each class of the images into a quantum many-body state. We study the quantum features of the TN states, including quantum entanglement and fidelity. We suggest these quantities could be novel properties that characterize the image classes, as well as the machine learning tasks. Our work could be further applied to identifying possible quantum properties of certain artificial intelligence methods.", "output": [ "This approach overcomes scalability issues and implies novel mathematical connections among quantum many-body physics, quantum information theory, and machine learning." ] }, { "id": "task668-8e0ec507096e4be1ad4c1d388e70f003", "input": "Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis. We introduce Interactive Inference Network (IIN), a novel class of neural network architectures that is able to achieve high-level understanding of the sentence pair by hierarchically extracting semantic features from interaction space. We show that an interaction tensor (attention weight) contains semantic information to solve natural language inference, and a denser interaction tensor contains richer semantic information. One instance of such architecture, Densely Interactive Inference Network (DIIN), demonstrates the state-of-the-art performance on large scale NLI copora and large-scale NLI alike corpus. It's noteworthy that DIIN achieve a greater than 20% error reduction on the challenging Multi-Genre NLI (MultiNLI) dataset with respect to the strongest published system.", "output": [ "show multi-channel attention weight contains semantic feature to solve natural language inference task." ] }, { "id": "task668-3af39903a53249f896c1d5e128db3a97", "input": "When machine learning models are used for high-stakes decisions, they should predict accurately, fairly, and responsibly. To fulfill these three requirements, a model must be able to output a reject option (i.e. say \"``I Don't Know\") when it is not qualified to make a prediction. In this work, we propose learning to defer, a method by which a model can defer judgment to a downstream decision-maker such as a human user. We show that learning to defer generalizes the rejection learning framework in two ways: by considering the effect of other agents in the decision-making process, and by allowing for optimization of complex objectives. We propose a learning algorithm which accounts for potential biases held by decision-makerslater in a pipeline. Experiments on real-world datasets demonstrate that learning\n to defer can make a model not only more accurate but also less biased. Even when\n operated by highly biased users, we show that\n deferring models can still greatly improve the fairness of the entire pipeline.", "output": [ "Incorporating the ability to say I-don't-know can improve the fairness of a classifier without sacrificing too much accuracy, and this improvement magnifies when the classifier has insight into downstream decision-making." ] }, { "id": "task668-292001538a2f454fa914b4e03c726734", "input": "Collaborative personalization, such as through learned user representations (embeddings), can improve the prediction accuracy of neural-network-based models significantly. We propose Federated User Representation Learning (FURL), a simple, scalable, privacy-preserving and resource-efficient way to utilize existing neural personalization techniques in the Federated Learning (FL) setting. FURL divides model parameters into federated and private parameters. Private parameters, such as private user embeddings, are trained locally, but unlike federated parameters, they are not transferred to or averaged on the server. We show theoretically that this parameter split does not affect training for most model personalization approaches. Storing user embeddings locally not only preserves user privacy, but also improves memory locality of personalization compared to on-server training. We evaluate FURL on two datasets, demonstrating a significant improvement in model quality with 8% and 51% performance increases, and approximately the same level of performance as centralized training with only 0% and 4% reductions. Furthermore, we show that user embeddings learned in FL and the centralized setting have a very similar structure, indicating that FURL can learn collaboratively through the shared parameters while preserving user privacy.", "output": [ "We propose Federated User Representation Learning (FURL), a simple, scalable, privacy-preserving and bandwidth-efficient way to utilize existing neural personalization techniques in the Federated Learning (FL) setting." ] }, { "id": "task668-fb7174a9070942fea7cd4868287494e2", "input": "Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modelling benchmarks. However, these have been evaluated using differing codebases and limited computational resources, which represent uncontrolled sources of experimental variation. We reevaluate several popular architectures and regularisation methods with large-scale automatic black-box hyperparameter tuning and arrive at the somewhat surprising conclusion that standard LSTM architectures, when properly regularised, outperform more recent models. We establish a new state of the art on the Penn Treebank and Wikitext-2 corpora, as well as strong baselines on the Hutter Prize dataset.\n", "output": [ "Show that LSTMs are as good or better than recent innovations for LM and that model evaluation is often unreliable.", "This paper describes a comprehensive validation of LSTM-based word and character language models, leading to a significant result in language modeling and a milestone in deep learning." ] }, { "id": "task668-0a5357059aa4409f8ca158e0b1c2979f", "input": "Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters. To understand consistency regularization, we conceptually explore how loss geometry interacts with training procedures. The consistency loss dramatically improves generalization performance over supervised-only training; however, we show that SGD struggles to converge on the consistency loss and continues to make large steps that lead to changes in predictions on the test data. Motivated by these observations, we propose to train consistency-based methods with Stochastic Weight Averaging (SWA), a recent approach which averages weights along the trajectory of SGD with a modified learning rate schedule. We also propose fast-SWA, which further accelerates convergence by averaging multiple points within each cycle of a cyclical learning rate schedule. With weight averaging, we achieve the best known semi-supervised results on CIFAR-10 and CIFAR-100, over many different quantities of labeled training data. For example, we achieve 5.0% error on CIFAR-10 with only 4000 labels, compared to the previous best result in the literature of 6.3%.", "output": [ "Consistency-based models for semi-supervised learning do not converge to a single point but continue to explore a diverse set of plausible solutions on the perimeter of a flat region. Weight averaging helps improve generalization performance.", "The paper proposes to apply Stochastic Weight Averaging to the semi-supervised learning context, arguing that the semi-supervised MT/Pi models are especially amenable to SWA and propose fast SWA to speed up training." ] }, { "id": "task668-a5238b9390074384b68351d831a77cee", "input": "Producing agents that can generalize to a wide range of environments is a significant challenge in reinforcement learning. One method for overcoming this issue is domain randomization, whereby at the start of each training episode some parameters of the environment are randomized so that the agent is exposed to many possible variations. However, domain randomization is highly inefficient and may lead to policies with high variance across domains. In this work, we formalize the domain randomization problem, and show that minimizing the policy's Lipschitz constant with respect to the randomization parameters leads to low variance in the learned policies. We propose a method where the agent only needs to be trained on one variation of the environment, and its learned state representations are regularized during training to minimize this constant. We conduct experiments that demonstrate that our technique leads to more efficient and robust learning than standard domain randomization, while achieving equal generalization scores.", "output": [ "We produce reinforcement learning agents that generalize well to a wide range of environments using a novel regularization technique.", "The paper introduces the high variance policies challenge in domain randomization for reinforcement learning and mainly focuses on the problem of visual randomization, where the different randomized domains differ only in state space and the underlying rewards and dynamics are the same.", "To improve the generalization ability of deep RL agents across the tasks with different visual patterns, this paper proposed a simple regularization technique for domain randomization." ] }, { "id": "task668-7e398cd61f98429f85ef07423096f993", "input": "The maximum mean discrepancy (MMD) between two probability measures P\n and Q is a metric that is zero if and only if all moments of the two measures\n are equal, making it an appealing statistic for two-sample tests. Given i.i.d. samples\n from P and Q, Gretton et al. (2012) show that we can construct an unbiased\n estimator for the square of the MMD between the two distributions. If P is a\n distribution of interest and Q is the distribution implied by a generative neural\n network with stochastic inputs, we can use this estimator to train our neural network.\n However, in practice we do not always have i.i.d. samples from our target\n of interest. Data sets often exhibit biases—for example, under-representation of\n certain demographics—and if we ignore this fact our machine learning algorithms\n will propagate these biases. Alternatively, it may be useful to assume our data has\n been gathered via a biased sample selection mechanism in order to manipulate\n properties of the estimating distribution Q.\n In this paper, we construct an estimator for the MMD between P and Q when we\n only have access to P via some biased sample selection mechanism, and suggest\n methods for estimating this sample selection mechanism when it is not already\n known. We show that this estimator can be used to train generative neural networks\n on a biased data sample, to give a simulator that reverses the effect of that\n bias.", "output": [ "We propose an estimator for the maximum mean discrepancy, appropriate when a target distribution is only accessible via a biased sample selection procedure, and show that it can be used in a generative network to correct for this bias.", "Proposes an importance-weighted estimator of the MMD to estimate the MMD between distributions based on samples biased according to a known or estimated unknown scheme.", "The authors address the problem of sample selection bias in MMD-GANs and propose an estimate of the MMD between two distributions using weighted maximum mean discrepancy.", "This paper presents a modification of the objective used to train generative networks with an MMD adversary " ] }, { "id": "task668-e3c79400d7324c449fdb99e0d7568b9c", "input": "Although challenging, strategy profile evaluation in large connected learner networks is crucial for enabling the next wave of machine learning applications. Recently, $\\alpha$-Rank, an evolutionary algorithm, has been proposed as a solution for ranking joint policy profiles in multi-agent systems. $\\alpha$-Rank claimed scalability through a polynomial time implementation with respect to the total number of pure strategy profiles. In this paper, we formally prove that such a claim is not grounded. In fact, we show that $\\alpha$-Rank exhibits an exponential complexity in number of agents, hindering its application beyond a small finite number of joint profiles. Realizing such a limitation, we contribute by proposing a scalable evaluation protocol that we title $\\alpha^{\\alpha}$-Rank. Our method combines evolutionary dynamics with stochastic optimization and double oracles for \\emph{truly} scalable ranking with linear (in number of agents) time and memory complexities. Our contributions allow us, for the first time, to conduct large-scale evaluation experiments of multi-agent systems, where we show successful results on large joint strategy profiles with sizes in the order of $\\mathcal{O}(2^{25})$ (i.e., $\\approx \\text{$33$ million strategies}$) -- a setting not evaluable using current techniques.", "output": [ "We provide a scalable solution to multi-agent evaluation with linear rate complexity in both time and memory in terms of number of agents" ] }, { "id": "task668-56977192870948daa129cb026f6cd0ae", "input": "Neural networks with low-precision weights and activations offer compelling\n efficiency advantages over their full-precision equivalents. The two most\n frequently discussed benefits of quantization are reduced memory consumption,\n and a faster forward pass when implemented with efficient bitwise\n operations. We propose a third benefit of very low-precision neural networks:\n improved robustness against some adversarial attacks, and in the worst case,\n performance that is on par with full-precision models. We focus on the very\n low-precision case where weights and activations are both quantized to $\\pm$1,\n and note that stochastically quantizing weights in just one layer can sharply\n reduce the impact of iterative attacks. We observe that non-scaled binary neural\n networks exhibit a similar effect to the original \\emph{defensive distillation}\n procedure that led to \\emph{gradient masking}, and a false notion of security.\n We address this by conducting both black-box and white-box experiments with\n binary models that do not artificially mask gradients.", "output": [ "We conduct adversarial attacks against binarized neural networks and show that we reduce the impact of the strongest attacks, while maintaining comparable accuracy in a black-box setting" ] }, { "id": "task668-2a2f3589446d4ee28f0d7de03771bbed", "input": "Artificial neural networks have opened up a world of possibilities in data science and artificial intelligence, but neural networks are cumbersome tools that grow with the complexity of the learning problem. We make contributions to this issue by considering a modified version of the fully connected layer we call a block diagonal inner product layer. These modified layers have weight matrices that are block diagonal, turning a single fully connected layer into a set of densely connected neuron groups. This idea is a natural extension of group, or depthwise separable, convolutional layers applied to the fully connected layers. Block diagonal inner product layers can be achieved by either initializing a purely block diagonal weight matrix or by iteratively pruning off diagonal block entries. This method condenses network storage and speeds up the run time without significant adverse effect on the testing accuracy, thus offering a new approach to improve network computation efficiency.", "output": [ "We look at neural networks with block diagonal inner product layers for efficiency.", "This paper proposes making the inner layers in a neural network be block diagonal, and discusses that block diagonal matrices are more efficient than pruning and block diagonal layers lead to more efficient networks.", "Replacing fully connected layers with block-diagonal fully connected layers" ] }, { "id": "task668-72535290437342b1bec3b66302f1f340", "input": "\nNew types of compute hardware in development and entering the market hold the promise of revolutionizing deep learning in a manner as profound as GPUs. However, existing software frameworks and training algorithms for deep learning have yet to evolve to fully leverage the capability of the new wave of silicon. In particular, models that exploit structured input via complex and instance-dependent control flow are difficult to accelerate using existing algorithms and hardware that typically rely on minibatching. We present an asynchronous model-parallel (AMP) training algorithm that is specifically motivated by training on networks of interconnected devices. Through an implementation on multi-core CPUs, we show that AMP training converges to the same accuracy as conventional synchronous training algorithms in a similar number of epochs, but utilizes the available hardware more efficiently, even for small minibatch sizes, resulting in shorter overall training times. Our framework opens the door for scaling up a new class of deep learning models that cannot be efficiently trained today.", "output": [ "Using asynchronous gradient updates to accelerate dynamic neural network training" ] }, { "id": "task668-3844d8da9e0440c79184ff3b8fdb446b", "input": "The high computational and parameter complexity of neural networks makes their training very slow and difficult to deploy on energy and storage-constrained comput- ing systems. Many network complexity reduction techniques have been proposed including fixed-point implementation. However, a systematic approach for design- ing full fixed-point training and inference of deep neural networks remains elusive. We describe a precision assignment methodology for neural network training in which all network parameters, i.e., activations and weights in the feedforward path, gradients and weight accumulators in the feedback path, are assigned close to minimal precision. The precision assignment is derived analytically and enables tracking the convergence behavior of the full precision training, known to converge a priori. Thus, our work leads to a systematic methodology of determining suit- able precision for fixed-point training. The near optimality (minimality) of the resulting precision assignment is validated empirically for four networks on the CIFAR-10, CIFAR-100, and SVHN datasets. The complexity reduction arising from our approach is compared with other fixed-point neural network designs.", "output": [ "We analyze and determine the precision requirements for training neural networks when all tensors, including back-propagated signals and weight accumulators, are quantized to fixed-point format." ] }, { "id": "task668-f236756ee85e419d9cd98d27cf4caa35", "input": "Building deep neural networks to control autonomous agents which have to interact in real-time with the physical world, such as robots or automotive vehicles, requires a seamless integration of time into a network’s architecture. The central question of this work is, how the temporal nature of reality should be reflected in the execution of a deep neural network and its components. Most artificial deep neural networks are partitioned into a directed graph of connected modules or layers and the layers themselves consist of elemental building blocks, such as single units. For most deep neural networks, all units of a layer are processed synchronously and in parallel, but layers themselves are processed in a sequential manner. In contrast, all elements of a biological neural network are processed in parallel. In this paper, we define a class of networks between these two extreme cases. These networks are executed in a streaming or synchronous layerwise-parallel manner, unlocking the layers of such networks for parallel processing. Compared to the standard layerwise-sequential deep networks, these new layerwise-parallel networks show a fundamentally different temporal behavior and flow of information, especially for networks with skip or recurrent connections. We argue that layerwise-parallel deep networks are better suited for future challenges of deep neural network design, such as large functional modularized and/or recurrent architectures as well as networks allocating different network capacities dependent on current stimulus and/or task complexity. We layout basic properties and discuss major challenges for layerwise-parallel networks. Additionally, we provide a toolbox to design, train, evaluate, and online-interact with layerwise-parallel networks.", "output": [ "We define a concept of layerwise model-parallel deep neural networks, for which layers operate in parallel, and provide a toolbox to design, train, evaluate, and on-line interact with these networks.", "A GPU-accelerated toolbox for parallel neuron updating, written in Theano, that supports different update orders in recurrent networks and networks with connections that skip layers. ", "A new toolbox for deep neural networks learning and evaluation, and proposal for a paradigm switch from layerwise-sequential networks to layer-wise parallel networks." ] }, { "id": "task668-f81e4ac38d984f6891b24742598308ee", "input": "Discretizing floating-point vectors is a fundamental step of modern indexing methods. State-of-the-art techniques learn parameters of the quantizers on training data for optimal performance, thus adapting quantizers to the data. In this work, we propose to reverse this paradigm and adapt the data to the quantizer: we train a neural net whose last layers form a fixed parameter-free quantizer, such as pre-defined points of a sphere. As a proxy objective, we design and train a neural network that favors uniformity in the spherical latent space, while preserving the neighborhood structure after the mapping. For this purpose, we propose a new regularizer derived from the Kozachenko-Leonenko differential entropy estimator and combine it with a locality-aware triplet loss. \n Experiments show that our end-to-end approach outperforms most learned quantization methods, and is competitive with the state of the art on widely adopted benchmarks. Further more, we show that training without the quantization step results in almost no difference in accuracy, but yields a generic catalyser that can be applied with any subsequent quantization technique.\n", "output": [ "We learn a neural network that uniformizes the input distribution, which leads to competitive indexing performance in high-dimensional space" ] }, { "id": "task668-ad5c487c2923437693671dc3c64cbe49", "input": "While deep learning has led to remarkable results on a number of challenging problems, researchers have discovered a vulnerability of neural networks in adversarial settings, where small but carefully chosen perturbations to the input can make the models produce extremely inaccurate outputs. This makes these models particularly unsuitable for safety-critical application domains (e.g. self-driving cars) where robustness is extremely important. Recent work has shown that augmenting training with adversarially generated data provides some degree of robustness against test-time attacks. In this paper we investigate how this approach scales as we increase the computational budget given to the defender. We show that increasing the number of parameters in adversarially-trained models increases their robustness, and in particular that ensembling smaller models while adversarially training the entire ensemble as a single model is a more efficient way of spending said budget than simply using a larger single model. Crucially, we show that it is the adversarial training of the ensemble, rather than the ensembling of adversarially trained models, which provides robustness.", "output": [ "Adversarial training of ensembles provides robustness to adversarial examples beyond that observed in adversarially trained models and independently-trained ensembles thereof.", " Proposes to train an ensemble of models jointly, where at each time step, a set of examples that are adversarial for the ensemble itself is incorporated in the learning." ] }, { "id": "task668-3e5c44d3a8bc4652974ddcbaed043216", "input": "Auto-encoders are commonly used for unsupervised representation learning and for pre-training deeper neural networks.\n When its activation function is linear and the encoding dimension (width of hidden layer) is smaller than the input dimension, it is well known that auto-encoder is optimized to learn the principal components of the data distribution (Oja1982).\n However, when the activation is nonlinear and when the width is larger than the input dimension (overcomplete), auto-encoder behaves differently from PCA, and in fact is known to perform well empirically for sparse coding problems. \n\n We provide a theoretical explanation for this empirically observed phenomenon, when rectified-linear unit (ReLu) is adopted as the activation function and the hidden-layer width is set to be large.\n In this case, we show that, with significant probability, initializing the weight matrix of an auto-encoder by sampling from a spherical Gaussian distribution followed by stochastic gradient descent (SGD) training converges towards the ground-truth representation for a class of sparse dictionary learning models.\n In addition, we can show that, conditioning on convergence, the expected convergence rate is O(1/t), where t is the number of updates.\n Our analysis quantifies how increasing hidden layer width helps the training performance when random initialization is used, and how the norm of network weights influence the speed of SGD convergence.", "output": [ "theoretical analysis of nonlinear wide autoencoder" ] }, { "id": "task668-a34a70f8b5b743f1bb61781f9d723230", "input": "A state-of-the-art generative model, a ”factorized action variational autoencoder (FAVAE),” is presented for learning disentangled and interpretable representations from sequential data via the information bottleneck without supervision. The purpose of disentangled representation learning is to obtain interpretable and transferable representations from data. We focused on the disentangled representation of sequential data because there is a wide range of potential applications if disentanglement representation is extended to sequential data such as video, speech, and stock price data. Sequential data is characterized by dynamic factors and static factors: dynamic factors are time-dependent, and static factors are independent of time. Previous works succeed in disentangling static factors and dynamic factors by explicitly modeling the priors of latent variables to distinguish between static and dynamic factors. However, this model can not disentangle representations between dynamic factors, such as disentangling ”picking” and ”throwing” in robotic tasks. In this paper, we propose new model that can disentangle multiple dynamic factors. Since our method does not require modeling priors, it is capable of disentangling ”between” dynamic factors. In experiments, we show that FAVAE can extract the disentangled dynamic factors.", "output": [ "We propose new model that can disentangle multiple dynamic factors in sequential data" ] }, { "id": "task668-4d8b96065ea44089b49d34bf6a5de372", "input": "Learning disentangled representation from any unlabelled data is a non-trivial problem. In this paper we propose Information Maximising Autoencoder (InfoAE) where the encoder learns powerful disentangled representation through maximizing the mutual information between the representation and given information in an unsupervised fashion. We have evaluated our model on MNIST dataset and achieved approximately 98.9 % test accuracy while using complete unsupervised training.", "output": [ "Learn disentangle representation in an unsupervised manner.", "The authors present a framework in which an auto encoder (E, D) is regularized such that its latent representation to share mutual information with a generated latent space representation." ] }, { "id": "task668-0100ab63b09041febdf1d9e637df384f", "input": "Recent progress on physics-based character animation has shown impressive breakthroughs on human motion synthesis, through imitating motion capture data via deep reinforcement learning. However, results have mostly been demonstrated on imitating a single distinct motion pattern, and do not generalize to interactive tasks that require flexible motion patterns due to varying human-object spatial configurations. To bridge this gap, we focus on one class of interactive tasks---sitting onto a chair. We propose a hierarchical reinforcement learning framework which relies on a collection of subtask controllers trained to imitate simple, reusable mocap motions, and a meta controller trained to execute the subtasks properly to complete the main task. We experimentally demonstrate the strength of our approach over different single level and hierarchical baselines. We also show that our approach can be applied to motion prediction given an image input. A video highlight can be found at https://youtu.be/XWU3wzz1ip8/.\n", "output": [ "Synthesizing human motions on interactive tasks using mocap data and hierarchical RL." ] }, { "id": "task668-f5d299a69190459b898e622fcc68572a", "input": "We address the problem of marginal inference for an exponential family defined over the set of permutation matrices. This problem is known to quickly become intractable as the size of the permutation increases, since its involves the computation of the permanent of a matrix, a #P-hard problem. We introduce Sinkhorn variational marginal inference as a scalable alternative, a method whose validity is ultimately justified by the so-called Sinkhorn approximation of the permanent. We demonstrate the efectiveness of our method in the problem of probabilistic identification of neurons in the worm C.elegans", "output": [ "New methodology for variational marginal inference of permutations based on Sinkhorn algorithm, applied to probabilistic identification of neurons" ] }, { "id": "task668-71442444ad8d4780b585ae2182bd4499", "input": "In this work, we propose the polynomial convolutional neural network (PolyCNN), as a new design of a weight-learning efficient variant of the traditional CNN. The biggest advantage of the PolyCNN is that at each convolutional layer, only one convolutional filter is needed for learning the weights, which we call the seed filter, and all the other convolutional filters are the polynomial transformations of the seed filter, which is termed as an early fan-out. Alternatively, we can also perform late fan-out on the seed filter response to create the number of response maps needed to be input into the next layer. Both early and late fan-out allow the PolyCNN to learn only one convolutional filter at each layer, which can dramatically reduce the model complexity by saving 10x to 50x parameters during learning. While being efficient during both training and testing, the PolyCNN does not suffer performance due to the non-linear polynomial expansion which translates to richer representational power within the convolutional layers. By allowing direct control over model complexity, PolyCNN provides a flexible trade-off between performance and efficiency. We have verified the on-par performance between the proposed PolyCNN and the standard CNN on several visual datasets, such as MNIST, CIFAR-10, SVHN, and ImageNet.", "output": [ "PolyCNN only needs to learn one seed convolutional filter at each layer. This is an efficient variant of traditional CNN, with on-par performance.", "Attempts at reducing the number of CNN model parameters by using the polynomial transformation of filters to create blow-up the filter responses.", "The authors propose a weight sharing architecture for reducing the number of convolutional neural network parameters with seed filters" ] }, { "id": "task668-367dfa8a82094ded884a31d55313b944", "input": "Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called dataset distillation: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one. The idea is to synthesize a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data. For example, we show that it is possible to compress 60,000 MNIST training images into just 10 synthetic distilled images (one per class) and achieve close to the original performance, given a fixed network initialization. We evaluate our method in various initialization settings. Experiments on multiple datasets, MNIST, CIFAR10, PASCAL-VOC, and CUB-200, demonstrate the ad-vantage of our approach compared to alternative methods. Finally, we include a real-world application of dataset distillation to the continual learning setting: we show that storing distilled images as episodic memory of previous tasks can alleviate forgetting more effectively than real images.", "output": [ "We propose to distill a large dataset into a small set of synthetic data that can train networks close to original performance. " ] }, { "id": "task668-8b3f1efa8c4a4687933e2297aa4887b2", "input": "In this paper, we present a technique for generating artificial datasets that retain statistical properties of the real data while providing differential privacy guarantees with respect to this data. We include a Gaussian noise layer in the discriminator of a generative adversarial network to make the output and the gradients differentially private with respect to the training data, and then use the generator component to synthesise privacy-preserving artificial dataset. Our experiments show that under a reasonably small privacy budget we are able to generate data of high quality and successfully train machine learning models on this artificial data.", "output": [ "Train GANs with differential privacy to generate artificial privacy-preserving datasets." ] }, { "id": "task668-637e8c2590db4e1796986025920e8bf3", "input": "In this paper we study generative modeling via autoencoders while using the elegant geometric properties of the optimal transport (OT) problem and the Wasserstein distances. We introduce Sliced-Wasserstein Autoencoders (SWAE), which are generative models that enable one to shape the distribution of the latent space into any samplable probability distribution without the need for training an adversarial network or defining a closed-form for the distribution. In short, we regularize the autoencoder loss with the sliced-Wasserstein distance between the distribution of the encoded training samples and a predefined samplable distribution. We show that the proposed formulation has an efficient numerical solution that provides similar capabilities to Wasserstein Autoencoders (WAE) and Variational Autoencoders (VAE), while benefiting from an embarrassingly simple implementation. \n", "output": [ "\"Generative modeling with no need for adversarial training\"" ] }, { "id": "task668-7c2864d6b5ba4d2086a47c8428f752b0", "input": "Planning in high-dimensional space remains a challenging problem, even with recent advances in algorithms and computational power. We are inspired by efference copy and sensory reafference theory from neuroscience. Our aim is to allow agents to form mental models of their environments for planning. The cerebellum is emulated with a two-stream, fully connected, predictor network. The network receives as inputs the efference as well as the features of the current state. Building on insights gained from knowledge distillation methods, we choose as our features the outputs of a pre-trained network, yielding a compressed representation of the current state. The representation is chosen such that it allows for fast search using classical graph search algorithms. We display the effectiveness of our approach on a viewpoint-matching task using a modified best-first search algorithm.", "output": [ "We present a neuroscience-inspired method based on neural networks for latent space search" ] }, { "id": "task668-f0a1a7bfe48f42839a39931c8c7d462e", "input": "We propose a novel generative model architecture designed to learn representations for images that factor out a single attribute from the rest of the representation. A single object may have many attributes which when altered do not change the identity of the object itself. Consider the human face; the identity of a particular person is independent of whether or not they happen to be wearing glasses. The attribute of wearing glasses can be changed without changing the identity of the person. However, the ability to manipulate and alter image attributes without altering the object identity is not a trivial task. Here, we are interested in learning a representation of the image that separates the identity of an object (such as a human face) from an attribute (such as 'wearing glasses'). We demonstrate the success of our factorization approach by using the learned representation to synthesize the same face with and without a chosen attribute. We refer to this specific synthesis process as image attribute manipulation. We further demonstrate that our model achieves competitive scores, with state of the art, on a facial attribute classification task.", "output": [ "Learn representations for images that factor out a single attribute.", "This paper builds on Conditional VAE GANs to allow attribute manipulation in the synthesis process.", "This paper proposes a generative model to learn the representation which can separate the identity of an object from an attribute, and extends the autoencoder adversarial by adding an auxiliary network." ] }, { "id": "task668-069698425571414a857bd17ba8030aea", "input": "Despite considerable advances in neural language modeling, it remains an open question what the best decoding strategy is for text generation from a language model (e.g. to generate a story). The counter-intuitive empirical observation is that even though the use of likelihood as training objective leads to high quality models for a broad range of language understanding tasks, maximization-based decoding methods such as beam search lead to degeneration — output text that is bland, incoherent, or gets stuck in repetitive loops.\n\n To address this we propose Nucleus Sampling, a simple but effective method to draw considerably higher quality text out of neural language models. Our approach avoids text degeneration by truncating the unreliable tail of the probability distribution, sampling from the dynamic nucleus of tokens containing the vast majority of the probability mass.\n\n To properly examine current maximization-based and stochastic decoding methods, we compare generations from each of these methods to the distribution of human text along several axes such as likelihood, diversity, and repetition. Our results show that (1) maximization is an inappropriate decoding objective for open-ended text generation, (2) the probability distributions of the best current language models have an unreliable tail which needs to be truncated during generation and (3) Nucleus Sampling is the best decoding strategy for generating long-form text that is both high-quality — as measured by human evaluation — and as diverse as human-written text.", "output": [ "Current language generation systems either aim for high likelihood and devolve into generic repetition or miscalibrate their stochasticity—we provide evidence of both and propose a solution: Nucleus Sampling." ] }, { "id": "task668-d3b15c95fe254fd6bff92214cf195121", "input": "A successful application of convolutional architectures is to increase the resolution of single low-resolution images -- a image restoration task called super-resolution (SR). Naturally, SR is of value to resource constrained devices like mobile phones, electronic photograph frames and televisions to enhance image quality. However, SR demands perhaps the most extreme amounts of memory and compute operations of any mainstream vision task known today, preventing SR from being deployed to devices that require them. In this paper, we perform a early systematic study of system resource efficiency for SR, within the context of a variety of architectural and low-precision approaches originally developed for discriminative neural networks. We present a rich set of insights, representative SR architectures, and efficiency trade-offs; for example, the prioritization of ways to compress models to reach a specific memory and computation target and techniques to compact SR models so that they are suitable for DSPs and FPGAs. As a result of doing so, we manage to achieve better and comparable performance with previous models in the existing literature, highlighting the practicality of using existing efficiency techniques in SR tasks. Collectively, we believe these results provides the foundation for further research into the little explored area of resource efficiency for SR.", "output": [ "We build an understanding of resource-efficient techniques on Super-Resolution", "The paper proposes a detailed empirical evaluation of the trade-offs achieved by various convolutional neural networks on the super resolution problem.", "This paper proposed to improve the system resource efficiency for super resolution networks." ] }, { "id": "task668-53bdf609f4b3430485e2bdfa187445c7", "input": "Positive-unlabeled (PU) learning addresses the problem of learning a binary classifier from positive (P) and unlabeled (U) data. It is often applied to situations where negative (N) data are difficult to be fully labeled. However, collecting a non-representative N set that contains only a small portion of all possible N data can be much easier in many practical situations. This paper studies a novel classification framework which incorporates such biased N (bN) data in PU learning. The fact that the training N data are biased also makes our work very different from those of standard semi-supervised learning. We provide an empirical risk minimization-based method to address this PUbN classification problem. Our approach can be regarded as a variant of traditional example-reweighting algorithms, with the weight of each example computed through a preliminary step that draws inspiration from PU learning. We also derive an estimation error bound for the proposed method. Experimental results demonstrate the effectiveness of our algorithm in not only PUbN learning scenarios but also ordinary PU leaning scenarios on several benchmark datasets.", "output": [ "This paper studied the PUbN classification problem, where we incorporate biased negative (bN) data, i.e., negative data that is not fully representative of the true underlying negative distribution, into positive-unlabeled (PU) learning." ] }, { "id": "task668-72c645e20e624c82a403703b472e8b61", "input": "The ability to generate natural language sequences from source code snippets has a variety of applications such as code summarization, documentation, and retrieval. Sequence-to-sequence (seq2seq) models, adopted from neural machine translation (NMT), have achieved state-of-the-art performance on these tasks by treating source code as a sequence of tokens. We present code2seq: an alternative approach that leverages the syntactic structure of programming languages to better encode source code. Our model represents a code snippet as the set of compositional paths in its abstract syntax tree (AST) and uses attention to select the relevant paths while decoding.\n We demonstrate the effectiveness of our approach for two tasks, two programming languages, and four datasets of up to 16M examples. Our model significantly outperforms previous models that were specifically designed for programming languages, as well as general state-of-the-art NMT models. An interactive online demo of our model is available at http://code2seq.org. Our code, data and trained models are available at http://github.com/tech-srl/code2seq.", "output": [ "We leverage the syntactic structure of source code to generate natural language sequences.", "Presents a method for generating sequences from code by parsing and producing a syntax tree", "This paper introduces an AST-based encoding for programming code and shows its effectiveness in the tasks of extreme code summarization and code captioning.", "This paper presents a new code-to-sequence model that leverages the syntactic structure of programming languages to encode source code snippets and then decode them to natural language" ] }, { "id": "task668-9ffe694c2128477fae63c74cd1ae2c66", "input": "We present trellis networks, a new architecture for sequence modeling. On the one hand, a trellis network is a temporal convolutional network with special structure, characterized by weight tying across depth and direct injection of the input into deep layers. On the other hand, we show that truncated recurrent networks are equivalent to trellis networks with special sparsity structure in their weight matrices. Thus trellis networks with general weight matrices generalize truncated recurrent networks. We leverage these connections to design high-performing trellis networks that absorb structural and algorithmic elements from both recurrent and convolutional models. Experiments demonstrate that trellis networks outperform the current state of the art methods on a variety of challenging benchmarks, including word-level language modeling and character-level language modeling tasks, and stress tests designed to evaluate long-term memory retention. The code is available at https://github.com/locuslab/trellisnet .", "output": [ "Trellis networks are a new sequence modeling architecture that bridges recurrent and convolutional models and sets a new state of the art on word- and character-level language modeling." ] }, { "id": "task668-b1ad72660e474bcc84de2fa836cab96f", "input": "While autoencoders are a key technique in representation learning for continuous structures, such as images or wave forms, developing general-purpose autoencoders for discrete structures, such as text sequence or discretized images, has proven to be more challenging. In particular, discrete inputs make it more difficult to learn a smooth encoder that preserves the complex local relationships in the input space. In this work, we propose an adversarially regularized autoencoder (ARAE) with the goal of learning more robust discrete-space representations. ARAE jointly trains both a rich discrete-space encoder, such as an RNN, and a simpler continuous space generator function, while using generative adversarial network (GAN) training to constrain the distributions to be similar. This method yields a smoother contracted code space that maps similar inputs to nearby codes, and also an implicit latent variable GAN model for generation. Experiments on text and discretized images demonstrate that the GAN model produces clean interpolations and captures the multimodality of the original space, and that the autoencoder produces improvements in semi-supervised learning as well as state-of-the-art results in unaligned text style transfer task using only a shared continuous-space representation.", "output": [ "Adversarially Regularized Autoencoders learn smooth representations of discrete structures allowing for interesting results in text generation, such as unaligned style transfer, semi-supervised learning, and latent space interpolation and arithmetic." ] }, { "id": "task668-786f2f94a79242888c3a4b11c0ed0f24", "input": "The challenge of learning disentangled representation has recently attracted much attention and boils down to a competition. Various methods based on variational auto-encoder have been proposed to solve this problem, by enforcing the independence between the representation and modifying the regularization term in the variational lower bound. However recent work by Locatello et al. (2018) has demonstrated that the proposed methods are heavily influenced by randomness and the choice of the hyper-parameter. This work is built upon the same framework in Stage 1 (Li et al., 2019), but with different settings; to make it self-contained, we provide this manuscript, which is unavoidably very similar to the report for Stage 1. In detail, in this work, instead of designing a new regularization term, we adopt the FactorVAE but improve the reconstruction performance and increase the capacity of network and the training step. The strategy turns out to be very effective in achieving disentanglement.", "output": [ "disentangled representation learning" ] }, { "id": "task668-f7af2fc4ebf24a6888575e5e6d566a38", "input": "Assessing distance betweeen the true and the sample distribution is a key component of many state of the art generative models, such as Wasserstein Autoencoder (WAE). Inspired by prior work on Sliced-Wasserstein Autoencoders (SWAE) and\n kernel smoothing we construct a new generative model – Cramer-Wold AutoEncoder (CWAE). CWAE cost function, based on introduced Cramer-Wold distance between samples, has a simple closed-form in the case of normal prior. As a consequence, while simplifying the optimization procedure (no need of sampling necessary to evaluate the distance function in the training loop), CWAE performance matches quantitatively and qualitatively that of WAE-MMD (WAE using maximum mean discrepancy based distance function) and often improves upon SWAE.", "output": [ "Inspired by prior work on Sliced-Wasserstein Autoencoders (SWAE) and kernel smoothing we construct a new generative model – Cramer-Wold AutoEncoder (CWAE).", "This paper proposes a WAE variant based on a new statistical distance between the encoded data distribution and the latent prior distribution", "Introduces a variation on the Wasserstein AudoEncoders which is a novel regularized auto-encoder architecture that proposes a specific choice of the divergence penalty", "This paper proposes the Cramer-Wold autoencoder, which uses the Cramer-Wold distance between two distributions based on the Cramer-Wold Theorem." ] }, { "id": "task668-e891fbde2e3e40c88db1b44660245041", "input": "We propose to extend existing deep reinforcement learning (Deep RL) algorithms by allowing them to additionally choose sequences of actions as a part of their policy. This modification forces the network to anticipate the reward of action sequences, which, as we show, improves the exploration leading to better convergence. Our proposal is simple, flexible, and can be easily incorporated into any Deep RL framework. We show the power of our scheme by consistently outperforming the state-of-the-art GA3C algorithm on several popular Atari Games.", "output": [ "Anticipation improves convergence of deep reinforcement learning." ] }, { "id": "task668-67792a844df448ffb939c40d11bc9640", "input": "Training generative adversarial networks is unstable in high-dimensions as the true data distribution tends to be concentrated in a small fraction of the ambient space. The discriminator is then quickly able to classify nearly all generated samples as fake, leaving the generator without meaningful gradients and causing it to deteriorate after a point in training. In this work, we propose training a single generator simultaneously against an array of discriminators, each of which looks at a different random low-dimensional projection of the data. Individual discriminators, now provided with restricted views of the input, are unable to reject generated samples perfectly and continue to provide meaningful gradients to the generator throughout training. Meanwhile, the generator learns to produce samples consistent with the full data distribution to satisfy all discriminators simultaneously. We demonstrate the practical utility of this approach experimentally, and show that it is able to produce image samples with higher quality than traditional training with a single discriminator.", "output": [ "Stable GAN training in high dimensions by using an array of discriminators, each with a low dimensional view of generated samples", "The paper proposes to stabilize GAN training by using an ensemble of discriminators, each working on a random projection of the input data, to provide the training signal for the generator model.", "The paper proposes a GAN training method for improving the training stability. ", "The paper proposes a new approach to GAN training, which provides stable gradients to train the generator." ] }, { "id": "task668-95e8c3572403445ca5a66bc27683f9cc", "input": "Many tasks in natural language processing and related domains require high precision output that obeys dataset-specific constraints. This level of fine-grained control can be difficult to obtain in large-scale neural network models. In this work, we propose a structured latent-variable approach that adds discrete control states within a standard autoregressive neural paradigm. Under this formulation, we can include a range of rich, posterior constraints to enforce task-specific knowledge that is effectively trained into the neural model. This approach allows us to provide arbitrary grounding of internal model decisions, without sacrificing any representational power of neural models. Experiments consider applications of this approach for text generation and part-of-speech induction. For natural language generation, we find that this method improves over standard benchmarks, while also providing fine-grained control.", "output": [ "A structured latent-variable approach that adds discrete control states within a standard autoregressive neural paradigm to provide arbitrary grounding of internal model decisions, without sacrificing any representational power of neural models." ] }, { "id": "task668-47074bbf202042ad932e631ff40ddc6f", "input": "While deep learning has been incredibly successful in modeling tasks with large, carefully curated labeled datasets, its application to problems with limited labeled data remains a challenge. The aim of the present work is to improve the label efficiency of large neural networks operating on audio data through a combination of multitask learning and self-supervised learning on unlabeled data. We trained an end-to-end audio feature extractor based on WaveNet that feeds into simple, yet versatile task-specific neural networks. We describe several easily implemented self-supervised learning tasks that can operate on any large, unlabeled audio corpus. We demonstrate that, in scenarios with limited labeled training data, one can significantly improve the performance of three different supervised classification tasks individually by up to 6% through simultaneous training with these additional self-supervised tasks. We also show that incorporating data augmentation into our multitask setting leads to even further gains in performance.", "output": [ "Label-efficient audio classification via multi-task learning and self-supervision" ] }, { "id": "task668-0caff549ae3344279140431cbad58603", "input": "Ensembles, where multiple neural networks are trained individually and their predictions are averaged, have been shown to be widely successful for improving both the accuracy and predictive uncertainty of single neural networks. However, an ensemble's cost for both training and testing increases linearly with the number of networks.\n In this paper, we propose BatchEnsemble, an ensemble method whose computational and memory costs are significantly lower than typical ensembles. BatchEnsemble achieves this by defining each weight matrix to be the Hadamard product of a shared weight among all ensemble members and a rank-one matrix per member. Unlike ensembles, BatchEnsemble is not only parallelizable across devices, where one device trains one member, but also parallelizable within a device, where multiple ensemble members are updated simultaneously for a given mini-batch. Across CIFAR-10, CIFAR-100, WMT14 EN-DE/EN-FR translation, and contextual bandits tasks, BatchEnsemble yields competitive accuracy and uncertainties as typical ensembles; the speedup at test time is 3X and memory reduction is 3X at an ensemble of size 4. We also apply BatchEnsemble to lifelong learning, where on Split-CIFAR-100, BatchEnsemble yields comparable performance to progressive neural networks while having a much lower computational and memory costs. We further show that BatchEnsemble can easily scale up to lifelong learning on Split-ImageNet which involves 100 sequential learning tasks.", "output": [ "We introduced BatchEnsemble, an efficient method for ensembling and lifelong learning which can be used to improve the accuracy and uncertainty of any neural network like typical ensemble methods." ] }, { "id": "task668-25351d21d76e4e5eba4a65fe3c7216ab", "input": "We propose a method which can visually explain the classification decision of deep neural networks (DNNs). There are many proposed methods in machine learning and computer vision seeking to clarify the decision of machine learning black boxes, specifically DNNs. All of these methods try to gain insight into why the network \"chose class A\" as an answer. Humans, when searching for explanations, ask two types of questions. The first question is, \"Why did you choose this answer? \" The second question asks, \"Why did you not choose answer B over A?\" The previously proposed methods are either not able to provide the latter directly or efficiently.\n\n We introduce a method capable of answering the second question both directly and efficiently. In this work, we limit the inputs to be images. In general, the proposed method generates explanations in the input space of any model capable of efficient evaluation and gradient evaluation. We provide results, showing the superiority of this approach for gaining insight into the inner representation of machine learning models.", "output": [ "A method to answer \"why not class B?\" for explaining deep networks", "The paper proposes an approach to provide contrastive visual explanations for deep neural networks." ] }, { "id": "task668-20d0c1dc119441e28c1d202c4b558619", "input": "Knowledge bases, massive collections of facts (RDF triples) on diverse topics, support vital modern applications. However, existing knowledge bases contain very little data compared to the wealth of information on the Web. This is because the industry standard in knowledge base creation and augmentation suffers from a serious bottleneck: they rely on domain experts to identify appropriate web sources to extract data from. Efforts to fully automate knowledge extraction have failed to improve this standard: these automated systems are able to retrieve much more data and from a broader range of sources, but they suffer from very low precision and recall. As a result, these large-scale extractions remain unexploited. In this paper, we present MIDAS, a system that harnesses the results of automated knowledge extraction pipelines to repair the bottleneck in industrial knowledge creation and augmentation processes. MIDAS automates the suggestion of good-quality web sources and describes what to extract with respect to augmenting an existing knowledge base. We make three major contributions. First, we introduce a novel concept, web source slices, to describe the contents of a web source. Second, we define a profit function to quantify the value of a web source slice with respect to augmenting an existing knowledge base. Third, we develop effective and highly-scalable algorithms to derive high-profit web source slices. We demonstrate that MIDAS produces high-profit results and outperforms the baselines significantly on both real-word and synthetic datasets.", "output": [ "This paper focuses on identifying high quality web sources for industrial knowledge base augmentation pipeline." ] }, { "id": "task668-74b5d3261f8049afa0d086705b77e7c4", "input": "In practice it is often found that large over-parameterized neural networks generalize better than their smaller counterparts, an observation that appears to conflict with classical notions of function complexity, which typically favor smaller models. In this work, we investigate this tension between complexity and generalization through an extensive empirical exploration of two natural metrics of complexity related to sensitivity to input perturbations. Our experiments survey thousands of models with different architectures, optimizers, and other hyper-parameters, as well as four different image classification datasets.\n\n We find that trained neural networks are more robust to input perturbations in the vicinity of the training data manifold, as measured by the input-output Jacobian of the network, and that this correlates well with generalization. We further establish that factors associated with poor generalization -- such as full-batch training or using random labels -- correspond to higher sensitivity, while factors associated with good generalization -- such as data augmentation and ReLU non-linearities -- give rise to more robust functions. Finally, we demonstrate how the input-output Jacobian norm can be predictive of generalization at the level of individual test points.", "output": [ "We perform massive experimental studies characterizing the relationships between Jacobian norms, linear regions, and generalization." ] }, { "id": "task668-adc44a83e5ca45d1bd56b24049cad1bf", "input": "We introduce an unsupervised structure learning algorithm for deep, feed-forward, neural networks. We propose a new interpretation for depth and inter-layer connectivity where a hierarchy of independencies in the input distribution is encoded in the network structure. This results in structures allowing neurons to connect to neurons in any deeper layer skipping intermediate layers. Moreover, neurons in deeper layers encode low-order (small condition sets) independencies and have a wide scope of the input, whereas neurons in the first layers encode higher-order (larger condition sets) independencies and have a narrower scope. Thus, the depth of the network is automatically determined---equal to the maximal order of independence in the input distribution, which is the recursion-depth of the algorithm. The proposed algorithm constructs two main graphical models: 1) a generative latent graph (a deep belief network) learned from data and 2) a deep discriminative graph constructed from the generative latent graph. We prove that conditional dependencies between the nodes in the learned generative latent graph are preserved in the class-conditional discriminative graph. Finally, a deep neural network structure is constructed based on the discriminative graph. We demonstrate on image classification benchmarks that the algorithm replaces the deepest layers (convolutional and dense layers) of common convolutional networks, achieving high classification accuracy, while constructing significantly smaller structures. The proposed structure learning algorithm requires a small computational cost and runs efficiently on a standard desktop CPU.", "output": [ "A principled approach for structure learning of deep neural networks with a new interpretation for depth and inter-layer connectivity. " ] }, { "id": "task668-6989b3d380f543e48fa90bc5c0e7ce65", "input": "Human annotation for syntactic parsing is expensive, and large resources are available only for a fraction of languages. A question we ask is whether one can leverage abundant unlabeled texts to improve syntactic parsers, beyond just using the texts to obtain more generalisable lexical features (i.e. beyond word embeddings). To this end, we propose a novel latent-variable generative model for semi-supervised syntactic dependency parsing. As exact inference is intractable, we introduce a differentiable relaxation to obtain approximate samples and compute gradients with respect to the parser parameters. Our method (Differentiable Perturb-and-Parse) relies on differentiable dynamic programming over stochastically perturbed edge scores. We demonstrate effectiveness of our approach with experiments on English, French and Swedish.", "output": [ "Differentiable dynamic programming over perturbed input weights with application to semi-supervised VAE" ] }, { "id": "task668-b8a499f6aa3f4d23a9b838b82265da88", "input": "It is becoming increasingly clear that many machine learning classifiers are vulnerable to adversarial examples. In attempting to explain the origin of adversarial examples, previous studies have typically focused on the fact that neural networks operate on high dimensional data, they overfit, or they are too linear. Here we show that distributions of logit differences have a universal functional form. This functional form is independent of architecture, dataset, and training protocol; nor does it change during training. This leads to adversarial error having a universal scaling, as a power-law, with respect to the size of the adversarial perturbation. We show that this universality holds for a broad range of datasets (MNIST, CIFAR10, ImageNet, and random data), models (including state-of-the-art deep networks, linear models, adversarially trained networks, and networks trained on randomly shuffled labels), and attacks (FGSM, step l.l., PGD). Motivated by these results, we study the effects of reducing prediction entropy on adversarial robustness. Finally, we study the effect of network architectures on adversarial sensitivity. To do this, we use neural architecture search with reinforcement learning to find adversarially robust architectures on CIFAR10. Our resulting architecture is more robust to white \\emph{and} black box attacks compared to previous attempts.\n", "output": [ "Adversarial error has similar power-law form for all datasets and models studied, and architecture matters." ] }, { "id": "task668-5b877fb8ad06476eba43e50513845902", "input": "Stacked hourglass network has become an important model for Human pose estimation. The estimation of human body posture depends on the global information of the keypoints type and the local information of the keypoints location. The consistent processing of inputs and constraints makes it difficult to form differentiated and determined collaboration mechanisms for each stacked hourglass network. In this paper, we propose a Multi-Scale Stacked Hourglass (MSSH) network to high-light the differentiation capabilities of each Hourglass network for human pose estimation. The pre-processing network forms feature maps of different scales,and dispatch them to various locations of the stack hourglass network, where the small-scale features reach the front of stacked hourglass network, and large-scale features reach the rear of stacked hourglass network. And a new loss function is proposed for multi-scale stacked hourglass network. Different keypoints have different weight coefficients of loss function at different scales, and the keypoints weight coefficients are dynamically adjusted from the top-level hourglass network to the bottom-level hourglass network. Experimental results show that the pro-posed method is competitive with respect to the comparison algorithm on MPII and LSP datasets.", "output": [ "Differentiated inputs cause functional differentiation of the network, and the interaction of loss functions between networks can affect the optimization process.", "A modification to the original hourglass network for single pose estimation that yields improvements over the original baseline.", "Authors extend a stacked hourglass network with inception-resnet-A modules and propose a multi-scale approach for human pose estimation in still RGB images." ] }, { "id": "task668-379f8ecaa3bf4a6abc29326379e503e3", "input": "Strong inductive biases allow children to learn in fast and adaptable ways. Children use the mutual exclusivity (ME) bias to help disambiguate how words map to referents, assuming that if an object has one label then it does not need another. In this paper, we investigate whether or not standard neural architectures have a ME bias, demonstrating that they lack this learning assumption. Moreover, we show that their inductive biases are poorly matched to lifelong learning formulations of classification and translation. We demonstrate that there is a compelling case for designing neural networks that reason by mutual exclusivity, which remains an open challenge.", "output": [ "Children use the mutual exclusivity (ME) bias to learn new words, while standard neural nets show the opposite bias, hindering learning in naturalistic scenarios such as lifelong learning." ] }, { "id": "task668-4deeca866e18472c922cf40ab76d20e5", "input": "Group convolutional neural networks (G-CNNs) can be used to improve classical CNNs by equipping them with the geometric structure of groups. Central in the success of G-CNNs is the lifting of feature maps to higher dimensional disentangled representations, in which data characteristics are effectively learned, geometric data-augmentations are made obsolete, and predictable behavior under geometric transformations (equivariance) is guaranteed via group theory. Currently, however, the practical implementations of G-CNNs are limited to either discrete groups (that leave the grid intact) or continuous compact groups such as rotations (that enable the use of Fourier theory). In this paper we lift these limitations and propose a modular framework for the design and implementation of G-CNNs for arbitrary Lie groups. In our approach the differential structure of Lie groups is used to expand convolution kernels in a generic basis of B-splines that is defined on the Lie algebra. This leads to a flexible framework that enables localized, atrous, and deformable convolutions in G-CNNs by means of respectively localized, sparse and non-uniform B-spline expansions. The impact and potential of our approach is studied on two benchmark datasets: cancer detection in histopathology slides (PCam dataset) in which rotation equivariance plays a key role and facial landmark localization (CelebA dataset) in which scale equivariance is important. In both cases, G-CNN architectures outperform their classical 2D counterparts and the added value of atrous and localized group convolutions is studied in detail.", "output": [ "The paper describes a flexible framework for building CNNs that are equivariant to a large class of transformations groups.", "A framework for building group CNN with an arbitrary Lie group G, which shows superiority over a CNN in tumor classification and landmark localization. " ] }, { "id": "task668-6dce432bf0954c3ea7643569b78abd46", "input": "We show that in a variety of large-scale deep learning scenarios the gradient dynamically converges to a very small subspace after a short period of training. The subspace is spanned by a few top eigenvectors of the Hessian (equal to the number of classes in the dataset), and is mostly preserved over long periods of training. A simple argument then suggests that gradient descent may happen mostly in this subspace. We give an example of this effect in a solvable model of classification, and we comment on possible implications for optimization and learning.", "output": [ "For classification problems with k classes, we show that the gradient tends to live in a tiny, slowly-evolving subspace spanned by the eigenvectors corresponding to the k-largest eigenvalues of the Hessian." ] }, { "id": "task668-b122669afb6b4580a0f3bebca76bf9c0", "input": "Inverse problems are ubiquitous in natural sciences and refer to the challenging task of inferring complex and potentially multi-modal posterior distributions over hidden parameters given a set of observations. Typically, a model of the physical process in the form of differential equations is available but leads to intractable inference over its parameters. While the forward propagation of parameters through the model simulates the evolution of the system, the inverse problem of finding the parameters given the sequence of states is not unique. In this work, we propose a generalisation of the Bayesian optimisation framework to approximate inference. The resulting method learns approximations to the posterior distribution by applying Stein variational gradient descent on top of estimates from a Gaussian process model. Preliminary results demonstrate the method's performance on likelihood-free inference for reinforcement learning environments.", "output": [ "An approach to combine variational inference and Bayesian optimisation to solve complicated inverse problems" ] }, { "id": "task668-f461f6a9e06d4a1d986c45c1266e0871", "input": "Federated learning is a recent advance in privacy protection. \n In this context, a trusted curator aggregates parameters optimized in decentralized fashion by multiple clients. The resulting model is then distributed back to all clients, ultimately converging to a joint representative model without explicitly having to share the data. \n However, the protocol is vulnerable to differential attacks, which could originate from any party contributing during federated optimization. In such an attack, a client's contribution during training and information about their data set is revealed through analyzing the distributed model. \n We tackle this problem and propose an algorithm for client sided differential privacy preserving federated optimization. The aim is to hide clients' contributions during training, balancing the trade-off between privacy loss and model performance. \n Empirical studies suggest that given a sufficiently large number of participating clients, our proposed procedure can maintain client-level differential privacy at only a minor cost in model performance.", "output": [ "Ensuring that models learned in federated fashion do not reveal a client's participation." ] }, { "id": "task668-0f3d652daa6642ce837913772f56519b", "input": "A leading hypothesis for the surprising generalization of neural networks is that the dynamics of gradient descent bias the model towards simple solutions, by searching through the solution space in an incremental order of complexity. We formally define the notion of incremental learning dynamics and derive the conditions on depth and initialization for which this phenomenon arises in deep linear models. Our main theoretical contribution is a dynamical depth separation result, proving that while shallow models can exhibit incremental learning dynamics, they require the initialization to be exponentially small for these dynamics to present themselves. However, once the model becomes deeper, the dependence becomes polynomial and incremental learning can arise in more natural settings. We complement our theoretical findings by experimenting with deep matrix sensing, quadratic neural networks and with binary classification using diagonal and convolutional linear networks, showing all of these models exhibit incremental learning.", "output": [ "We study the sparsity-inducing bias of deep models, caused by their learning dynamics." ] }, { "id": "task668-3eda2cd171f3467ab917d1732b993344", "input": "We introduce a neural architecture to perform amortized approximate Bayesian inference over latent random permutations of two sets of objects. The method involves approximating permanents of matrices of pairwise probabilities using recent ideas on functions defined over sets. Each sampled permutation comes with a probability estimate, a quantity unavailable in MCMC approaches. We illustrate the method in sets of 2D points and MNIST images.\n", "output": [ "A novel neural architecture for efficient amortized inference over latent permutations " ] }, { "id": "task668-36ccea874b484f679a89c890431cdd89", "input": " Current end-to-end machine reading and question answering (Q\\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q\\&A architecture called QANet, which does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions. On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models. The speed-up gain allows us to train the model with much more data. We hence combine our model with data generated by backtranslation from a neural machine translation model. \n On the SQuAD dataset, our single model, trained with augmented data, achieves 84.6 F1 score on the test set, which is significantly better than the best published F1 score of 81.8.", "output": [ "A simple architecture consisting of convolutions and attention achieves results on par with the best documented recurrent models.", "A fast high performance paraphrasing based data augmentation method and a non-recurrent reading comprehension model using only convolutions and attention.", "This paper proposes applying CNNs+self-attention modules instead of LSTMs and enhancing the RC model training with passage paraphrases generated by a neural paraphrasing model in order to improve RC performance.", "This paper presents a reading comprehension model using convolutions and attention and propose to augment additional training data by paraphrasing based on off-the-shelf neural machine translation" ] }, { "id": "task668-1d8be5aeb94a47d293024378d66b7934", "input": "Neural networks are known to produce unexpected results on inputs that are far from the training distribution. One approach to tackle this problem is to detect the samples on which the trained network can not answer reliably. ODIN is a recently proposed method for out-of-distribution detection that does not modify the trained network and achieves good performance for various image classification tasks. In this paper we adapt ODIN for sentence classification and word tagging tasks. We show that the scores produced by ODIN can be used as a confidence measure for the predictions on both in-distribution and out-of-distribution datasets.", "output": [ "A recent out-of-distribution detection method helps to measure the confidence of RNN predictions for some NLP tasks" ] }, { "id": "task668-404edf674c8b4f91866725e02c387761", "input": "In many domains, especially enterprise text analysis, there is an abundance of data which can be used for the development of new AI-powered intelligent experiences to improve people's productivity. However, there are strong-guarantees of privacy which prevent broad sampling and labeling of personal text data to learn or evaluate models of interest. Fortunately, in some cases like enterprise email, manual annotation is possible on certain public datasets. The hope is that models trained on these public datasets would perform well on the target private datasets of interest. In this paper, we study the challenges of transferring information from one email dataset to another, for predicting user intent. In particular, we present approaches to characterizing the transfer gap in text corpora from both an intrinsic and extrinsic point-of-view, and evaluate several proposed methods in the literature for bridging this gap. We conclude with raising issues for further discussion in this arena.", "output": [ "Insights on the domain adaptation challenge, when predicting user intent in enterprise email." ] }, { "id": "task668-e908382deb634712b69f03444990fa0b", "input": "Forming perceptual groups and individuating objects in visual scenes is an essential step towards visual intelligence. This ability is thought to arise in the brain from computations implemented by bottom-up, horizontal, and top-down connections between neurons. However, the relative contributions of these connections to perceptual grouping are poorly understood. We address this question by systematically evaluating neural network architectures featuring combinations of these connections on two synthetic visual tasks, which stress low-level \"Gestalt\" vs. high-level object cues for perceptual grouping. We show that increasing the difficulty of either task strains learning for networks that rely solely on bottom-up processing. Horizontal connections resolve this limitation on tasks with Gestalt cues by supporting incremental spatial propagation of activities, whereas top-down connections rescue learning on tasks with high-level object cues by modifying coarse predictions about the position of the target object. Our findings dissociate the computational roles of bottom-up, horizontal and top-down connectivity, and demonstrate how a model featuring all of these interactions can more flexibly learn to form perceptual groups.", "output": [ "Horizontal and top-down feedback connections are responsible for complementary perceptual grouping strategies in biological and recurrent vision systems.", "Using neural networks as a computational model of the brain, examines the efficiency of different strategies for solving two visual challenges." ] }, { "id": "task668-599ba901e72a47ca8188fde28c80dc45", "input": "Most existing GANs architectures that generate images use transposed convolution or resize-convolution as their upsampling algorithm from lower to higher resolution feature maps in the generator. We argue that this kind of fixed operation is problematic for GANs to model objects that have very different visual appearances. We propose a novel adaptive convolution method that learns the upsampling algorithm based on the local context at each location to address this problem. We modify a baseline GANs architecture by replacing normal convolutions with adaptive convolutions in the generator. Experiments on CIFAR-10 dataset show that our modified models improve the baseline model by a large margin. Furthermore, our models achieve state-of-the-art performance on CIFAR-10 and STL-10 datasets in the unsupervised setting.", "output": [ "We replace normal convolutions with adaptive convolutions to improve GANs generator.", "Proposes to replace convolutions in the generator with an Adaptive Convolution Block that learns to generate convolution weigths adn biases of upsampling operations adaptively per pixel location", "Uses Adaptive Convolution in the context of GANs with a block called AdaConvBlock that replaces regular Convolution, this gives more local context per kernel weight so that it can generate locally flexible objects." ] }, { "id": "task668-dc5a318f41b64ecf9d1ffea2f9f1ce25", "input": "Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images. In most applications, GAN models share two aspects in common. On the one hand, GANs training involves solving a challenging saddle point optimization problem, interpreted as an adversarial game between a generator and a discriminator functions. On the other hand, the generator and the discriminator are parametrized in terms of deep convolutional neural networks. The goal of this paper is to disentangle the contribution of these two factors to the success of GANs. In particular, we introduce Generative Latent Optimization (GLO), a framework to train deep convolutional generators without using discriminators, thus avoiding the instability of adversarial optimization problems. Throughout a variety of experiments, we show that GLO enjoys many of the desirable properties of GANs: learning from large data, synthesizing visually-appealing samples, interpolating meaningfully between samples, and performing linear arithmetic with noise vectors.", "output": [ "Are GANs successful because of adversarial training or the use of ConvNets? We show a ConvNet generator trained with a simple reconstruction loss and learnable noise vectors leads many of the desirable properties of a GAN." ] }, { "id": "task668-54fe97e1fc624b62887e8334ddc8197e", "input": "Deep Convolutional Networks (DCNs) have been shown to be sensitive to Universal Adversarial Perturbations (UAPs): input-agnostic perturbations that fool a model on large portions of a dataset. These UAPs exhibit interesting visual patterns, but this phenomena is, as yet, poorly understood. Our work shows that visually similar procedural noise patterns also act as UAPs. In particular, we demonstrate that different DCN architectures are sensitive to Gabor noise patterns. This behaviour, its causes, and implications deserve further in-depth study.", "output": [ "Existing Deep Convolutional Networks in image classification tasks are sensitive to Gabor noise patterns, i.e. small structured changes to the input cause large changes to the output." ] }, { "id": "task668-ba307b560e924d5dbeeb9d42d72cabdd", "input": "Convolution neural networks typically consist of many convolutional layers followed by several fully-connected layers. While convolutional layers map between high-order activation tensors, the fully-connected layers operate on flattened activation vectors. Despite its success, this approach has notable drawbacks. Flattening discards the multi-dimensional structure of the activations, and the fully-connected layers require a large number of parameters. \n We present two new techniques to address these problems. First, we introduce tensor contraction layers which can replace the ordinary fully-connected layers in a neural network. Second, we introduce tensor regression layers, which express the output of a neural network as a low-rank multi-linear mapping from a high-order activation tensor to the softmax layer. Both the contraction and regression weights are learned end-to-end by backpropagation. By imposing low rank on both, we use significantly fewer parameters. Experiments on the ImageNet dataset show that applied to the popular VGG and ResNet architectures, our methods significantly reduce the number of parameters in the fully connected layers (about 65% space savings) while negligibly impacting accuracy.", "output": [ "We propose tensor contraction and low-rank tensor regression layers to preserve and leverage the multi-linear structure throughout the network, resulting in huge space savings with little to no impact on performance.", "This paper proposes new layer architectures of neural networks using a low-rank representation of tensors", "This paper incorporates tensor decomposition and tensor regression into CNN by using a new tensor regression layer." ] }, { "id": "task668-a35e144247414312ba6d0376774f8633", "input": "When deep learning is applied to sensitive data sets, many privacy-related implementation issues arise. These issues are especially evident in the healthcare, finance, law and government industries. Homomorphic encryption could allow a server to make inferences on inputs encrypted by a client, but to our best knowledge, there has been no complete implementation of common deep learning operations, for arbitrary model depths, using homomorphic encryption. This paper demonstrates a novel approach, efficiently implementing many deep learning functions with bootstrapped homomorphic encryption. As part of our implementation, we demonstrate Single and Multi-Layer Neural Networks, for the Wisconsin Breast Cancer dataset, as well as a Convolutional Neural Network for MNIST. Our results give promising directions for privacy-preserving representation learning, and the return of data control to users.\n\n", "output": [ "We made a feature-rich system for deep learning with encrypted inputs, producing encrypted outputs, preserving privacy.", "A framework for private deep learning model inference using FHE schemes that support fast bootstrapping and thus can reduce computation time.", "The paper presents a means of evaluating a neural network securely using homomorphic encryption." ] }, { "id": "task668-7975a2e6149f4351a5adcf35278e4e2f", "input": "We outline the problem of concept drifts for time series data. In this work, we analyze the temporal inconsistency of streaming wireless signals in the context of device-free passive indoor localization. We show that data obtained from WiFi channel state information (CSI) can be used to train a robust system capable of performing room level localization. One of the most challenging issues for such a system is the movement of input data distribution to an unexplored space over time, which leads to an unwanted shift in the learned boundaries of the output space. In this work, we propose a phase and magnitude augmented feature space along with a standardization technique that is little affected by drifts. We show that this robust representation of the data yields better learning accuracy and requires less number of retraining.", "output": [ "We introduce an augmented robust feature space for streaming wifi data that is capable of tackling concept drift for indoor localization" ] }, { "id": "task668-10ccfd3f6d75403ea65737d0346cd0ae", "input": "Recent improvements to Generative Adversarial Networks (GANs) have made it possible to generate realistic images in high resolution based on natural language descriptions such as image captions. Furthermore, conditional GANs allow us to control the image generation process through labels or even natural language descriptions. However, fine-grained control of the image layout, i.e. where in the image specific objects should be located, is still difficult to achieve. This is especially true for images that should contain multiple distinct objects at different spatial locations. We introduce a new approach which allows us to control the location of arbitrarily many objects within an image by adding an object pathway to both the generator and the discriminator. Our approach does not need a detailed semantic layout but only bounding boxes and the respective labels of the desired objects are needed. The object pathway focuses solely on the individual objects and is iteratively applied at the locations specified by the bounding boxes. The global pathway focuses on the image background and the general image layout. We perform experiments on the Multi-MNIST, CLEVR, and the more complex MS-COCO data set. Our experiments show that through the use of the object pathway we can control object locations within images and can model complex scenes with multiple objects at various locations. We further show that the object pathway focuses on the individual objects and learns features relevant for these, while the global pathway focuses on global image characteristics and the image background.", "output": [ "Extend GAN architecture to obtain control over locations and identities of multiple objects within generated images." ] }, { "id": "task668-5b0307b34d834c4c9c37fbe36a60614b", "input": "In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones. However, a critical challenge in meta-learning is the task heterogeneity which cannot be well handled by traditional globally shared meta-learning methods. In addition, current task-specific meta-learning methods may either suffer from hand-crafted structure design or lack the capability to capture complex relations between tasks. In this paper, motivated by the way of knowledge organization in knowledge bases, we propose an automated relational meta-learning (ARML) framework that automatically extracts the cross-task relations and constructs the meta-knowledge graph. When a new task arrives, it can quickly find the most relevant structure and tailor the learned structure knowledge to the meta-learner. As a result, the proposed framework not only addresses the challenge of task heterogeneity by a learned meta-knowledge graph, but also increases the model interpretability. We conduct extensive experiments on 2D toy regression and few-shot image classification and the results demonstrate the superiority of ARML over state-of-the-art baselines.", "output": [ "Addressing task heterogeneity problem in meta-learning by introducing meta-knowledge graph" ] }, { "id": "task668-c841c062bffd4069a408e31051918442", "input": "This paper formalises the problem of online algorithm selection in the context of Reinforcement Learning (RL). The setup is as follows: given an episodic task and a finite number of off-policy RL algorithms, a meta-algorithm has to decide which RL algorithm is in control during the next episode so as to maximize the expected return. The article presents a novel meta-algorithm, called Epochal Stochastic Bandit Algorithm Selection (ESBAS). Its principle is to freeze the policy updates at each epoch, and to leave a rebooted stochastic bandit in charge of the algorithm selection. Under some assumptions, a thorough theoretical analysis demonstrates its near-optimality considering the structural sampling budget limitations. ESBAS is first empirically evaluated on a dialogue task where it is shown to outperform each individual algorithm in most configurations. ESBAS is then adapted to a true online setting where algorithms update their policies after each transition, which we call SSBAS. SSBAS is evaluated on a fruit collection task where it is shown to adapt the stepsize parameter more efficiently than the classical hyperbolic decay, and on an Atari game, where it improves the performance by a wide margin.", "output": [ "This paper formalises the problem of online algorithm selection in the context of Reinforcement Learning." ] }, { "id": "task668-fe1ffff492e74d128753311425a9a834", "input": "Continual learning is a longstanding goal of artificial intelligence, but is often counfounded by catastrophic forgetting that prevents neural networks from learning tasks sequentially. Previous methods in continual learning have demonstrated how to mitigate catastrophic forgetting, and learn new tasks while retaining performance on the previous tasks. We analyze catastrophic forgetting from the perspective of change in classifier likelihood and propose a simple L1 minimization criterion which can be adapted to different use cases. We further investigate two ways to minimize forgetting as quantified by this criterion and propose strategies to achieve finer control over forgetting. Finally, we evaluate our strategies on 3 datasets of varying difficulty and demonstrate improvements over previously known L2 strategies for mitigating catastrophic forgetting.", "output": [ "Another perspective on catastrophic forgetting", "This paper introduces a framework for combatting catastrophic forgetting based upon changing the loss term to minimise changes in classifier likelihood, obtained via a Taylor series approximation.", "This paper tries to solve the continual learning prolem by focusing on regularization approaches, and it proposes a L_1 strategy to mitigate the problem." ] }, { "id": "task668-4644405539754ba3ad278cb926e5bf82", "input": "Watermarks have been used for various purposes. Recently, researchers started to look into using them for deep neural networks. Some works try to hide attack triggers on their adversarial samples when attacking neural networks and others want to watermark neural networks to prove their ownership against plagiarism. Implanting a backdoor watermark module into a neural network is getting more attention from the community. In this paper, we present a general purpose encoder-decoder joint training method, inspired by generative adversarial networks (GANs). Unlike GANs, however, our encoder and decoder neural networks cooperate to find the best watermarking scheme given data samples. In other words, we do not design any new watermarking strategy but our proposed two neural networks will find the best suited method on their own. After being trained, the decoder can be implanted into other neural networks to attack or protect them (see Appendix for their use cases and real implementations). To this end, the decoder should be very tiny in order not to incur any overhead when attached to other neural networks but at the same time provide very high decoding success rates, which is very challenging. Our joint training method successfully solves the problem and in our experiments maintain almost 100\\% encoding-decoding success rates for multiple datasets with very little modifications on data samples to hide watermarks. We also present several real-world use cases in Appendix.", "output": [ "We propose a novel watermark encoder-decoder neural networks. They perform a cooperative game to define their own watermarking scheme. People do not need to design watermarking methods any more." ] }, { "id": "task668-7732ab120f3342f6b3133df66efc71df", "input": "In this paper, we consider the problem of autonomous lane changing for self driving vehicles in a multi-lane, multi-agent setting. We present a framework that demonstrates a more structured and data efficient alternative to end-to-end complete policy learning on problems where the high-level policy is hard to formulate using traditional optimization or rule based methods but well designed low-level controllers are available. Our framework uses deep reinforcement learning solely to obtain a high-level policy for tactical decision making, while still maintaining a tight integration with the low-level controller, thus getting the best of both worlds. We accomplish this with Q-masking, a technique with which we are able to incorporate prior knowledge, constraints, and information from a low-level controller, directly in to the learning process thereby simplifying the reward function and making learning faster and data efficient. We provide preliminary results in a simulator and show our approach to be more efficient than a greedy baseline, and more successful and safer than human driving.", "output": [ "A framework that provides a policy for autonomous lane changing by learning to make high-level tactical decisions with deep reinforcement learning, and maintaining a tight integration with a low-level controller to take low-level actions.", "Considers the problem of autonomous lane changing for self-driving cars in multi-lane multi-agent slot car setting, proposes a new learning strategy Q-masking - coupling a defined low level controller with a high level tactical decision making policy.", "This paper proposes a deep Q-learning approach to the problem of lane change using \"Q-masking,\" which reduces the action space according to contraints or prior knowledge.", "Authors propose a method which uses a Q-learning-based high-level policy that is combined with a contextual mask derived from safety-contraints and low-level controllers, which disable certain actions from being selectable at certain states. " ] }, { "id": "task668-9b8a580fcdd64b748dc8d870b4006cb7", "input": "In the context of optimization, a gradient of a neural network indicates the amount a specific weight should change with respect to the loss. Therefore, small gradients indicate a good value of the weight that requires no change and can be kept frozen during training. This paper provides an experimental study on the importance of a neural network weights, and to which extent do they need to be updated. We wish to show that starting from the third epoch, freezing weights which have no informative gradient and are less likely to be changed during training, results in a very slight drop in the overall accuracy (and in sometimes better). We experiment on the MNIST, CIFAR10 and Flickr8k datasets using several architectures (VGG19,\n ResNet-110 and DenseNet-121). On CIFAR10, we show that freezing 80% of the VGG19 network parameters from the third epoch onwards results in 0.24% drop in accuracy, while freezing 50% of Resnet-110 parameters results in 0.9% drop in accuracy and finally freezing 70% of Densnet-121 parameters results in 0.57% drop in accuracy. Furthermore, to experiemnt with real-life applications, we train an image captioning model with attention mechanism on the Flickr8k dataset using LSTM networks, freezing 60% of the parameters from the third epoch onwards, resulting in a better BLEU-4 score than the fully trained model. Our source code can be found in the appendix.", "output": [ "An experimental paper that proves the amount of redundant weights that can be freezed from the third epoch only, with only a very slight drop in accuracy." ] }, { "id": "task668-0d7c7224de8b4cb49a4049cbf4c529b6", "input": "Emotion is playing a great role in our daily lives. The necessity and importance of an automatic Emotion recognition system is getting increased. Traditional approaches of emotion recognition are based on facial images, measurements of heart rates, blood pressure, temperatures, tones of voice/speech, etc. However, these features can potentially be changed to fake features. So to detect hidden and real features that is not controlled by the person are data measured from brain signals. There are various ways of measuring brain waves: EEG, MEG, FMRI, etc. On the bases of cost effectiveness and performance trade-offs, EEG is chosen for emotion recognition in this work. The main aim of this study is to detect emotion based on EEG signal analysis recorded from brain in response to visual stimuli. The approaches used were the selected visual stimuli were presented to 11 healthy target subjects and EEG signal were recorded in controlled situation to minimize artefacts (muscle or/and eye movements). The signals were filtered and type of frequency band was computed and detected. The proposed method predicts an emotion type (positive/negative) in response to the presented stimuli. Finally, the performance of the proposed approach was tested. The average accuracy of machine learning algorithms (i.e. J48, Bayes Net, Adaboost and Random Forest) are 78.86, 74.76, 77.82 and 82.46 respectively. In this study, we also applied EEG applications in the context of neuro-marketing. The results empirically demonstrated detection of the favourite colour preference of customers in response to the logo colour of an organization or Service.", "output": [ "This paper presents EEG based emotion detection of a person towards an image stimuli and its applicability on neuromarketing." ] }, { "id": "task668-4184defc30054716a424f7a9c97ea803", "input": "We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals. \n To this end, we replace conventional convolution kernels with linear combinations of differential operators that are weighted by learnable parameters. Differential operators can be efficiently estimated on unstructured grids using one-ring neighbors, and learnable parameters can be optimized through standard back-propagation. As a result, we obtain extremely efficient neural networks that match or outperform state-of-the-art network architectures in terms of performance but with a significantly lower number of network parameters. We evaluate our algorithm in an extensive series of experiments on a variety of computer vision and climate science tasks, including shape classification, climate pattern segmentation, and omnidirectional image semantic segmentation. Overall, we present (1) a novel CNN approach on unstructured grids using parameterized differential operators for spherical signals, and (2) we show that our unique kernel parameterization allows our model to achieve the same or higher accuracy with significantly fewer network parameters.", "output": [ "We present a new CNN kernel for unstructured grids for spherical signals, and show significant accuracy and parameter efficiency gain on tasks such as 3D classfication and omnidirectional image segmentation.", "An efficient method enabling deep learning on spherical data that reaches competitive/state-of-the-art numbers with much less parameters than popular approaches.", "The paper proposes a novel convolutional kernel for CNN on the unstructured grids and formulates the convolution by a linear combination of differential operators." ] }, { "id": "task668-370f0a7675c24c8f81f0b4d231c46ed8", "input": "Reservoir computing is a powerful tool to explain how the brain learns temporal sequences, such as movements, but existing learning schemes are either biologically implausible or too inefficient to explain animal performance. We show that a network can learn complicated sequences with a reward-modulated Hebbian learning rule if the network of reservoir neurons is combined with a second network that serves as a dynamic working memory and provides a spatio-temporal backbone signal to the reservoir. In combination with the working memory, reward-modulated Hebbian learning of the readout neurons performs as well as FORCE learning, but with the advantage of a biologically plausible interpretation of both the learning rule and the learning paradigm.", "output": [ "We show that a working memory input to a reservoir network makes a local reward-modulated Hebbian rule perform as well as recursive least-squares (aka FORCE)" ] }, { "id": "task668-621169160c1a4c9baa0c430a1b2d3f4c", "input": "Most state-of-the-art neural machine translation systems, despite being different\n in architectural skeletons (e.g., recurrence, convolutional), share an indispensable\n feature: the Attention. However, most existing attention methods are token-based\n and ignore the importance of phrasal alignments, the key ingredient for the success\n of phrase-based statistical machine translation. In this paper, we propose\n novel phrase-based attention methods to model n-grams of tokens as attention\n entities. We incorporate our phrase-based attentions into the recently proposed\n Transformer network, and demonstrate that our approach yields improvements of\n 1.3 BLEU for English-to-German and 0.5 BLEU for German-to-English translation\n tasks, and 1.75 and 1.35 BLEU points in English-to-Russian and Russian-to-English translation tasks \n on WMT newstest2014 using WMT’16 training data.\n", "output": [ "Phrase-based attention mechanisms to assign attention on phrases, achieving token-to-phrase, phrase-to-token, phrase-to-phrase attention alignments, in addition to existing token-to-token attentions.", "Paper presents an attention mechanism that computes a weighted sum over not only single tokens but ngrams(phrases)." ] }, { "id": "task668-53692dfddc604adfa9ce31dd7fdc1137", "input": "Stemming is the process of removing affixes( i.e. prefixes, infixes and suffixes) that improve the accuracy and performance of information retrieval systems.This paper presents the reduction of Amharic words to corresponding stem where with the intention that it preserves semantic information. The proposed approach efficiently removes affixes from an Amharic word. The process of removing such affixes (prefixes, infixes and suffixes) from a word to its base form is called stemming. While many stemmers exist for dominant languages such as English, under resourced languages such as Amharic which lacks such powerful tool support. In this paper, we design a light Amharic stemmer relying on the rules that receives an Amharic word and then it finds a match to the beginning of a word to the possible prefixes and to its ending with the possible suffixes and finally it checks whether it has infix. The final result is the stem if there is any prefix, infix or/and suffix, otherwise it remains in one of the earlier states. The technique does not rely on any additional resource (e.g. dictionary) to verify the generated stem. The performance of the generated stemmer is evaluated using manually annotated Amharic words. The result is compared with current state-of-the-art stemmer for Amharic showing an increase of 7% in stemmer correctness.", "output": [ "Amharic Light Stemmer is designed for improving performance of Amharic Sentiment Classification.", "This paper studies the stemming for morphologically rich languages with a light stemmer that only removes affixes to the extent that the original semantic information in the word is kept.", "This paper proposes a technique for Amharic light stemming using a cascade of transformations that standardize the form, remove suffixes, prefixes, and infixes." ] }, { "id": "task668-4d4e9ab8ac904650848e66473ec24588", "input": "We introduce the 2-simplicial Transformer, an extension of the Transformer which includes a form of higher-dimensional attention generalising the dot-product attention, and uses this attention to update entity representations with tensor products of value vectors. We show that this architecture is a useful inductive bias for logical reasoning in the context of deep reinforcement learning.\n", "output": [ "We introduce the 2-simplicial Transformer and show that this architecture is a useful inductive bias for logical reasoning in the context of deep reinforcement learning." ] }, { "id": "task668-1eb3687de12e456fac60f4ccf4e8f408", "input": "It is widely known that well-designed perturbations can cause state-of-the-art machine learning classifiers to mis-label an image, with sufficiently small perturbations that are imperceptible to the human eyes. However, by detecting the inconsistency between the image and wrong label, the human observer would be alerted of the attack. In this paper, we aim to design attacks that not only make classifiers generate wrong labels, but also make the wrong labels imperceptible to human observers. To achieve this, we propose an algorithm called LabelFool which identifies a target label similar to the ground truth label and finds a perturbation of the image for this target label. We first find the target label for an input image by a probability model, then move the input in the feature space towards the target label. Subjective studies on ImageNet show that in the label space, our attack is much less recognizable by human observers, while objective experimental results on ImageNet show that we maintain similar performance in the image space as well as attack rates to state-of-the-art attack algorithms.", "output": [ "A trick on adversarial samples so that the mis-classified labels are imperceptible in the label space to human observers", "A method for constructing adversarial attacks that are less detectable by humans without cost in image space by changing the target class to be similar to the original class of the image." ] }, { "id": "task668-c05d62e3bfcc4d9aa380d5c309e03a46", "input": "The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various hand-designed alternatives to ReLU have been proposed, none have managed to replace it due to inconsistent gains. In this work, we propose to leverage automatic search techniques to discover new activation functions. Using a combination of exhaustive and reinforcement learning-based search, we discover multiple novel activation functions. We verify the effectiveness of the searches by conducting an empirical evaluation with the best discovered activation function. Our experiments show that the best discovered activation function, f(x) = x * sigmoid(beta * x), which we name Swish, tends to work better than ReLU on deeper models across a number of challenging datasets. For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9% for Mobile NASNet-A and 0.6% for Inception-ResNet-v2. The simplicity of Swish and its similarity to ReLU make it easy for practitioners to replace ReLUs with Swish units in any neural network.", "output": [ "We use search techniques to discover novel activation functions, and our best discovered activation function, f(x) = x * sigmoid(beta * x), outperforms ReLU on a number of challenging tasks like ImageNet.", "Proposes a reinforcement learning based approach for finding non-linearity by searching through combinations from a set of unary and binary operators.", "This paper utilizes reinforcement learning to search the combination of a set of unary and binary functions resulting in a new activation function", "The author uses reinforcement learning to find new potential activation functions from a rich set of possible candidates. " ] }, { "id": "task668-de9fe20bbf234735bc2988daca9f2ae2", "input": "Massively multi-label prediction/classification problems arise in environments like health-care or biology where it is useful to make very precise predictions. One challenge with massively multi-label problems is that there is often a long-tailed frequency distribution for the labels, resulting in few positive examples for the rare labels. We propose a solution to this problem by modifying the output layer of a neural network to create a Bayesian network of sigmoids which takes advantage of ontology relationships between the labels to help share information between the rare and the more common labels. We apply this method to the two massively multi-label tasks of disease prediction (ICD-9 codes) and protein function prediction (Gene Ontology terms) and obtain significant improvements in per-label AUROC and average precision.", "output": [ " We propose a new method for using ontology information to improve performance on massively multi-label prediction/classification problems." ] }, { "id": "task668-eb685817231b4d7ab1083d7037d8d6c7", "input": "We propose a method to learn stochastic activation functions for use in probabilistic neural networks.\n First, we develop a framework to embed stochastic activation functions based on Gaussian processes in probabilistic neural networks.\n Second, we analytically derive expressions for the propagation of means and covariances in such a network, thus allowing for an efficient implementation and training without the need for sampling.\n Third, we show how to apply variational Bayesian inference to regularize and efficiently train this model.\n The resulting model can deal with uncertain inputs and implicitly provides an estimate of the confidence of its predictions.\n Like a conventional neural network it can scale to datasets of arbitrary size and be extended with convolutional and recurrent connections, if desired.", "output": [ "We model the activation function of each neuron as a Gaussian Process and learn it alongside the weight with Variational Inference.", "Propose placing Gaussian process priors on the functional form of each activation function in the neural net to learn the form of activation functions." ] }, { "id": "task668-b9e49921a36e40eca24effbe9ad2326b", "input": "In this paper, we propose a mix-generator generative adversarial networks (PGAN) model that works in parallel by mixing multiple disjoint generators to approximate a complex real distribution. In our model, we propose an adjustment component that collects all the generated data points from the generators, learns the boundary between each pair of generators, and provides error to separate the support of each of the generated distributions. To overcome the instability in a multiplayer game, a shrinkage adjustment component method is introduced to gradually reduce the boundary between generators during the training procedure. To address the linearly growing training time problem in a multiple generators model, we propose a method to train the generators in parallel. This means that our work can be scaled up to large parallel computation frameworks. We present an efficient loss function for the discriminator, an effective adjustment component, and a suitable generator. We also show how to introduce the decay factor to stabilize the training procedure. We have performed extensive experiments on synthetic datasets, MNIST, and CIFAR-10. These experiments reveal that the error provided by the adjustment component could successfully separate the generated distributions and each of the generators can stably learn a part of the real distribution even if only a few modes are contained in the real distribution.", "output": [ "multi generator to capture Pdata, solve the competition and one-beat-all problem", "Proposes parallel GANs to avoid mode collapse in GANs through a combination of multiple weak generators. " ] }, { "id": "task668-fc7f02b65bfe4564814ad8eca660d0f4", "input": "This research paper describes a simplistic architecture named as AANN: Absolute Artificial Neural Network, which can be used to create highly interpretable representations of the input data. These representations are generated by penalizing the learning of the network in such a way that those learned representations correspond to the respective labels present in the labelled dataset used for supervised training; thereby, simultaneously giving the network the ability to classify the input data. The network can be used in the reverse direction to generate data that closely resembles the input by feeding in representation vectors as required. This research paper also explores the use of mathematical abs (absolute valued) functions as activation functions which constitutes the core part of this neural network architecture. Finally the results obtained on the MNIST dataset by using this technique are presented and discussed in brief.", "output": [ "Tied weights auto-encoder with abs function as activation function, learns to do classification in the forward direction and regression in the backward direction due to specially defined cost function.", "The paper proposes using the absolute value activation function in an autoencoder architecture with an additional supervised learning term in the objective function", "This paper introduces a reversible network with absolute value used as the activation function" ] }, { "id": "task668-c99557292d26460ab02876bfe69acd74", "input": "In a typical deep learning approach to a computer vision task, Convolutional Neural Networks (CNNs) are used to extract features at varying levels of abstraction from an image and compress a high dimensional input into a lower dimensional decision space through a series of transformations. In this paper, we investigate how a class of input images is eventually compressed over the course of these transformations. In particular, we use singular value decomposition to analyze the relevant variations in feature space. These variations are formalized as the effective dimension of the embedding. We consider how the effective dimension varies across layers within class. We show that across datasets and architectures, the effective dimension of a class increases before decreasing further into the network, suggesting some sort of initial whitening transformation. Further, the decrease rate of the effective dimension deeper in the network corresponds with training performance of the model.", "output": [ "Neural networks that do a good job of classification project points into more spherical shapes before compressing them into fewer dimensions." ] }, { "id": "task668-510cfe8188bb46c88f9e2676ef6961d7", "input": "Aspect extraction in online product reviews is a key task in sentiment analysis and opinion mining. Training supervised neural networks for aspect extraction is not possible when ground truth aspect labels are not available, while the unsupervised neural topic models fail to capture the particular aspects of interest. In this work, we propose a weakly supervised approach for training neural networks for aspect extraction in cases where only a small set of seed words, i.e., keywords that describe an aspect, are available. Our main contributions are as follows. First, we show that current weakly supervised networks fail to leverage the predictive power of the available seed words by comparing them to a simple bag-of-words classifier. Second, we propose a distillation approach for aspect extraction where the seed words are considered by the bag-of-words classifier (teacher) and distilled to the parameters of a neural network (student). Third, we show that regularization encourages the student to consider non-seed words for classification and, as a result, the student outperforms the teacher, which only considers the seed words. Finally, we empirically show that our proposed distillation approach outperforms (by up to 34.4% in F1 score) previous weakly supervised approaches for aspect extraction in six domains of Amazon product reviews.", "output": [ "We effectively leverage a few keywords as weak supervision for training neural networks for aspect extraction.", "Discusses a variant of knowledge distillation which uses a \"teacher\" based on a bag-of-words classifier with seed words and a \"student\" which is an embedding-based neural network." ] }, { "id": "task668-4edd920c8a2a4254b56520b174878d10", "input": "Recent work suggests goal-driven training of neural networks can be used to model neural activity in the brain. While response properties of neurons in artificial neural networks bear similarities to those in the brain, the network architectures are often constrained to be different. Here we ask if a neural network can recover both neural representations and, if the architecture is unconstrained and optimized, also the anatomical properties of neural circuits. We demonstrate this in a system where the connectivity and the functional organization have been characterized, namely, the head direction circuit of the rodent and fruit fly. We trained recurrent neural networks (RNNs) to estimate head direction through integration of angular velocity. We found that the two distinct classes of neurons observed in the head direction system, the Ring neurons and the Shifter neurons, emerged naturally in artificial neural networks as a result of training. Furthermore, connectivity analysis and in-silico neurophysiology revealed structural and mechanistic similarities between artificial networks and the head direction system. Overall, our results show that optimization of RNNs in a goal-driven task can recapitulate the structure and function of biological circuits, suggesting that artificial neural networks can be used to study the brain at the level of both neural activity and anatomical organization.", "output": [ "Artificial neural networks trained with gradient descent are capable of recapitulating both realistic neural activity and the anatomical organization of a biological circuit." ] }, { "id": "task668-f4de6c7e3d574804966414f445eb5318", "input": "Successful training of convolutional neural networks is often associated with suffi-\n ciently deep architectures composed of high amounts of features. These networks\n typically rely on a variety of regularization and pruning techniques to converge\n to less redundant states. We introduce a novel bottom-up approach to expand\n representations in fixed-depth architectures. These architectures start from just a\n single feature per layer and greedily increase width of individual layers to attain\n effective representational capacities needed for a specific task. While network\n growth can rely on a family of metrics, we propose a computationally efficient\n version based on feature time evolution and demonstrate its potency in determin-\n ing feature importance and a networks’ effective capacity. We demonstrate how\n automatically expanded architectures converge to similar topologies that benefit\n from lesser amount of parameters or improved accuracy and exhibit systematic\n correspondence in representational complexity with the specified task. In contrast\n to conventional design patterns with a typical monotonic increase in the amount of\n features with increased depth, we observe that CNNs perform better when there is\n more learnable parameters in intermediate, with falloffs to earlier and later layers.", "output": [ "A bottom-up algorithm that expands CNNs starting with one feature per layer to architectures with sufficient representational capacity.", "Proposes to dynamically adjust the feature map depth of a fully convolutional neural network, formulating a measure of self-resemblance and boosting performance.", "Introduces a simple correlation-based metric to measure whether filters in neural networks are being used effectively, as a proxy for effective capacity.", "Aims to address the deep learning architecture search problem via incremental addition and removal of channels in intermediate layers of the network." ] }, { "id": "task668-d8dbb3547d0b4efca9d56260755026b8", "input": "Empirical evidence suggests that neural networks with ReLU activations generalize better with over-parameterization. However, there is currently no theoretical analysis that explains this observation. In this work, we study a simplified learning task with over-parameterized convolutional networks that empirically exhibits the same qualitative phenomenon. For this setting, we provide a theoretical analysis of the optimization and generalization performance of gradient descent. Specifically, we prove data-dependent sample complexity bounds which show that over-parameterization improves the generalization performance of gradient descent.", "output": [ "We show in a simplified learning task that over-parameterization improves generalization of a convnet that is trained with gradient descent." ] }, { "id": "task668-c80733ff528a4f6680276bca6ee2ca94", "input": "Determining the optimal order in which data examples are presented to Deep Neural Networks during training is a non-trivial problem. However, choosing a non-trivial scheduling method may drastically improve convergence. In this paper, we propose a Self-Paced Learning (SPL)-fused Deep Metric Learning (DML) framework, which we call Learning Embeddings for Adaptive Pace (LEAP). Our method parameterizes mini-batches dynamically based on the \\textit{easiness} and \\textit{true diverseness} of the sample within a salient feature representation space. In LEAP, we train an \\textit{embedding} Convolutional Neural Network (CNN) to learn an expressive representation space by adaptive density discrimination using the Magnet Loss. The \\textit{student} CNN classifier dynamically selects samples to form a mini-batch based on the \\textit{easiness} from cross-entropy losses and \\textit{true diverseness} of examples from the representation space sculpted by the \\textit{embedding} CNN. We evaluate LEAP using deep CNN architectures for the task of supervised image classification on MNIST, FashionMNIST, CIFAR-10, CIFAR-100, and SVHN. We show that the LEAP framework converges faster with respect to the number of mini-batch updates required to achieve a comparable or better test performance on each of the datasets.", "output": [ "LEAP combines the strength of adaptive sampling with that of mini-batch online learning and adaptive representation learning to formulate a representative self-paced strategy in an end-to-end DNN training protocol. ", "Introduces a method for creating mini batches for a student network by using a second learned representation space to dynamically select examples by their 'easiness and true diverseness'.", "Experiments the classification accuracy on MNIST, FashionMNIST, and CIFAR-10 datasets to learn a representation with curriculum learning style minibatch selection in an end-to-end framework." ] }, { "id": "task668-3c8b724d2dfd48cc8eda826d8573e06e", "input": "Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. For a given input sample, they assign a relevance score to each individual input variable, such as the pixels of an image. In this work we adapt the information bottleneck concept for attribution. By adding noise to intermediate feature maps we restrict the flow of information and can quantify (in bits) how much information image regions provide. We compare our method against ten baselines using three different metrics on VGG-16 and ResNet-50, and find that our methods outperform all baselines in five out of six settings. The method’s information-theoretic foundation provides an absolute frame of reference for attribution values (bits) and a guarantee that regions scored close to zero are not necessary for the network's decision.", "output": [ "We apply the informational bottleneck concept to attribution.", "The paper proposes a novel perturbation-based method for computing attribution/saliency maps for deep neural network based image classifiers, by injecting crafted noise into an early layer of the network." ] }, { "id": "task668-aa879410faa1494e86764c858d76ffe9", "input": "Large pre-trained Transformers such as BERT have been tremendously effective for many NLP tasks. However, inference in these large-capacity models is prohibitively slow and expensive . Transformers are essentially a stack of self-attention layers which encode each input position using the entire input sequence as its context . However, we find that it may not be necessary to apply this expensive sequence-wide self-attention over at all layers . Based on this observation, we propose a decomposition to a pre-trained Transformer that allows the lower layers to process segments of the input independently enabling parallelism and caching . We show that the information loss due to this decomposition can be recovered in the upper layers with auxiliary supervision during fine-tuning . We evaluate de-composition with pre-trained BERT models on five different paired-input tasks in question answering, sentence similarity, and natural language inference . Results show that decomposition enables faster inference (up to 4x), significant memory reduction (up to 70%) while retaining most (up to 99%) of the original performance . We will release the code at.", "output": [ "Inference in large Transformers is expensive due to the self-attention in multiple layers. We show a simple decomposition technique can yield a faster, low memory-footprint model that is just as accurate of the original models." ] }, { "id": "task668-b671a7fe63af44d892d26ff3c3ba2e9c", "input": "We propose the set autoencoder, a model for unsupervised representation learning for sets of elements. It is closely related to sequence-to-sequence models, which learn fixed-sized latent representations for sequences, and have been applied to a number of challenging supervised sequence tasks such as machine translation, as well as unsupervised representation learning for sequences.\n In contrast to sequences, sets are permutation invariant. The proposed set autoencoder considers this fact, both with respect to the input as well as the output of the model. On the input side, we adapt a recently-introduced recurrent neural architecture using a content-based attention mechanism. On the output side, we use a stable marriage algorithm to align predictions to labels in the learning phase.\n We train the model on synthetic data sets of point clouds and show that the learned representations change smoothly with translations in the inputs, preserve distances in the inputs, and that the set size is represented directly. We apply the model to supervised tasks on the point clouds using the fixed-size latent representation. For a number of difficult classification problems, the results are better than those of a model that does not consider the permutation invariance. Especially for small training sets, the set-aware model benefits from unsupervised pretraining.", "output": [ "We propose the set autoencoder, a model for unsupervised representation learning for sets of elements." ] }, { "id": "task668-c92e802aa1aa4b20b967d823c3758cb5", "input": "We develop end-to-end learned reconstructions for lensless mask-based cameras, including an experimental system for capturing aligned lensless and lensed images for training. Various reconstruction methods are explored, on a scale from classic iterative approaches (based on the physical imaging model) to deep learned methods with many learned parameters. In the middle ground, we present several variations of unrolled alternating direction method of multipliers (ADMM) with varying numbers of learned parameters. The network structure combines knowledge of the physical imaging model with learned parameters updated from the data, which compensate for artifacts caused by physical approximations. Our unrolled approach is 20X faster than classic methods and produces better reconstruction quality than both the classic and deep methods on our experimental system. ", "output": [ "We improve the reconstruction time and quality on an experimental mask-based lensless imager using an end-to-end learning approach which incorporates knowledge of the imaging model." ] }, { "id": "task668-a64ebbf6e72a46c58e5207eb896e8065", "input": "Generative adversarial nets (GANs) are widely used to learn the data sampling process and their performance may heavily depend on the loss functions, given a limited computational budget. This study revisits MMD-GAN that uses the maximum mean discrepancy (MMD) as the loss function for GAN and makes two contributions. First, we argue that the existing MMD loss function may discourage the learning of fine details in data as it attempts to contract the discriminator outputs of real data. To address this issue, we propose a repulsive loss function to actively learn the difference among the real data by simply rearranging the terms in MMD. Second, inspired by the hinge loss, we propose a bounded Gaussian kernel to stabilize the training of MMD-GAN with the repulsive loss function. The proposed methods are applied to the unsupervised image generation tasks on CIFAR-10, STL-10, CelebA, and LSUN bedroom datasets. Results show that the repulsive loss function significantly improves over the MMD loss at no additional computational cost and outperforms other representative loss functions. The proposed methods achieve an FID score of 16.21 on the CIFAR-10 dataset using a single DCGAN network and spectral normalization.", "output": [ "Rearranging the terms in maximum mean discrepancy yields a much better loss function for the discriminator of generative adversarial nets" ] }, { "id": "task668-c924ed165c0447f193212418eb02ed37", "input": "The main goal of network pruning is imposing sparsity on the neural network by increasing the number of parameters with zero value in order to reduce the architecture size and the computational speedup.", "output": [ "Proposing a novel method based on the guided attention to enforce the sparisty in deep neural networks." ] }, { "id": "task668-683a68d2f9544f679d3c124a5e847f20", "input": "Particle-based inference algorithm is a promising method to efficiently generate samples for an intractable target distribution by iteratively updating a set of particles. As a noticeable example, Stein variational gradient descent (SVGD) provides a deterministic and computationally efficient update, but it is known to underestimate the variance in high dimensions, the mechanism of which is poorly understood. In this work we explore a connection between SVGD and MMD-based inference algorithm via Stein's lemma. By comparing the two update rules, we identify the source of bias in SVGD as a combination of high variance and deterministic bias, and empirically demonstrate that the removal of either factors leads to accurate estimation of the variance. In addition, for learning high-dimensional Gaussian target, we analytically derive the converged variance for both algorithms, and confirm that only SVGD suffers from the \"curse of dimensionality\".", "output": [ "Analyze the underlying mechanisms of variance collapse of SVGD in high dimensions." ] }, { "id": "task668-23e7a55681c14016bfbca464edff050a", "input": "Image-to-image translation has recently received significant attention due to advances in deep learning. Most works focus on learning either a one-to-one mapping in an unsupervised way or a many-to-many mapping in a supervised way. However, a more practical setting is many-to-many mapping in an unsupervised way, which is harder due to the lack of supervision and the complex inner- and cross-domain variations. To alleviate these issues, we propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain. We assume that an image comprises of a content component which is shared across domains, and a style component specific to each domain. Under the guidance of an exemplar from the target domain we apply Adaptive Instance Normalization to the shared content component, which allows us to transfer the style information of the target domain to the source domain. To avoid semantic inconsistencies during translation that naturally appear due to the large inner- and cross-domain variations, we introduce the concept of feature masks that provide coarse semantic guidance without requiring the use of any semantic labels. Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process.", "output": [ "We propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain.", "Discusses a core failing and need for I2I translation models.", "The paper explores the idea that an image has two components and applies an attention model where the feature masks that steer the translation process do not require semantic labels" ] }, { "id": "task668-abedb67b73bb4bccbb48ed3287036da2", "input": "Capsule networks are constrained by the parameter-expensive nature of their layers, and the general lack of provable equivariance guarantees. We present a variation of capsule networks that aims to remedy this. We identify that learning all pair-wise part-whole relationships between capsules of successive layers is inefficient. Further, we also realise that the choice of prediction networks and the routing mechanism are both key to equivariance. Based on these, we propose an alternative framework for capsule networks that learns to projectively encode the manifold of pose-variations, termed the space-of-variation (SOV), for every capsule-type of each layer. This is done using a trainable, equivariant function defined over a grid of group-transformations. Thus, the prediction-phase of routing involves projection into the SOV of a deeper capsule using the corresponding function. As a specific instantiation of this idea, and also in order to reap the benefits of increased parameter-sharing, we use type-homogeneous group-equivariant convolutions of shallower capsules in this phase. We also introduce an equivariant routing mechanism based on degree-centrality. We show that this particular instance of our general model is equivariant, and hence preserves the compositional representation of an input under transformations. We conduct several experiments on standard object-classification datasets that showcase the increased transformation-robustness, as well as general performance, of our model to several capsule baselines.", "output": [ "A new scalable, group-equivariant model for capsule networks that preserves compositionality under transformations, and is empirically more transformation-robust to older capsule network models." ] }, { "id": "task668-ef2684bc20de4974baa543a5b66662e5", "input": "While generative models have shown great success in generating high-dimensional samples conditional on low-dimensional descriptors (learning e.g. stroke thickness in MNIST, hair color in CelebA, or speaker identity in Wavenet), their generation out-of-sample poses fundamental problems. The conditional variational autoencoder (CVAE) as a simple conditional generative model does not explicitly relate conditions during training and, hence, has no incentive of learning a compact joint distribution across conditions. We overcome this limitation by matching their distributions using maximum mean discrepancy (MMD) in the decoder layer that follows the bottleneck. This introduces a strong regularization both for reconstructing samples within the same condition and for transforming samples across conditions, resulting in much improved generalization. We refer to the architecture as transformer VAE (trVAE). Benchmarking trVAE on high-dimensional image and tabular data, we demonstrate higher robustness and higher accuracy than existing approaches. In particular, we show qualitatively improved predictions for cellular perturbation response to treatment and disease based on high-dimensional single-cell gene expression data, by tackling previously problematic minority classes and multiple conditions. For generic tasks, we improve Pearson correlations of high-dimensional estimated means and variances with their ground truths from 0.89 to 0.97 and 0.75 to 0.87, respectively.\n", "output": [ "Generates never seen data during training from a desired condition " ] }, { "id": "task668-26fe1856f7224ab3ad9222f8507e9ae1", "input": "We present an approach for anytime predictions in deep neural networks (DNNs). For each test sample, an anytime predictor produces a coarse result quickly, and then continues to refine it until the test-time computational budget is depleted. Such predictors can address the growing computational problem of DNNs by automatically adjusting to varying test-time budgets. In this work, we study a \\emph{general} augmentation to feed-forward networks to form anytime neural networks (ANNs) via auxiliary predictions and losses. Specifically, we point out a blind-spot in recent studies in such ANNs: the importance of high final accuracy. In fact, we show on multiple recognition data-sets and architectures that by having near-optimal final predictions in small anytime models, we can effectively double the speed of large ones to reach corresponding accuracy level. We achieve such speed-up with simple weighting of anytime losses that oscillate during training. We also assemble a sequence of exponentially deepening ANNs, to achieve both theoretically and practically near-optimal anytime results at any budget, at the cost of a constant fraction of additional consumed budget.", "output": [ "By focusing more on the final predictions in anytime predictors (such as the very recent Multi-Scale-DenseNets), we make small anytime models to outperform large ones that don't have such focus. " ] }, { "id": "task668-d4f94f25a6d44708a19f3a1fa2e8e18f", "input": "We present a new approach for efficient exploration which leverages a low-dimensional encoding of the environment learned with a combination of model-based and model-free objectives. Our approach uses intrinsic rewards that are based on a weighted distance of nearest neighbors in the low dimensional representational space to gauge novelty.\n We then leverage these intrinsic rewards for sample-efficient exploration with planning routines in representational space.\n One key element of our approach is that we perform more gradient steps in-between every environment step in order to ensure the model accuracy. We test our approach on a number of maze tasks, as well as a control problem and show that our exploration approach is more sample-efficient compared to strong baselines.", "output": [ "We conduct exploration using intrinsic rewards that are based on a weighted distance of nearest neighbors in representational space.", "This paper proposes a method for efficient exploration in tabular MDPs as well as a simple control environment, using deterministic encoders to learn a low dimensional representation of the environment dynamics.", "This paper proposes a method of sample-efficient exploration for RL agent using a combination of model-based and model-free approaches with a novelty metric." ] }, { "id": "task668-4978e299e43a4ae9bb3b4727bdea321a", "input": "Optimistic initialisation is an effective strategy for efficient exploration in reinforcement learning (RL). In the tabular case, all provably efficient model-free algorithms rely on it. However, model-free deep RL algorithms do not use optimistic initialisation despite taking inspiration from these provably efficient tabular algorithms. In particular, in scenarios with only positive rewards, Q-values are initialised at their lowest possible values due to commonly used network initialisation schemes, a pessimistic initialisation. Merely initialising the network to output optimistic Q-values is not enough, since we cannot ensure that they remain optimistic for novel state-action pairs, which is crucial for exploration. We propose a simple count-based augmentation to pessimistically initialised Q-values that separates the source of optimism from the neural network. We show that this scheme is provably efficient in the tabular setting and extend it to the deep RL setting. Our algorithm, Optimistic Pessimistically Initialised Q-Learning (OPIQ), augments the Q-value estimates of a DQN-based agent with count-derived bonuses to ensure optimism during both action selection and bootstrapping. We show that OPIQ outperforms non-optimistic DQN variants that utilise a pseudocount-based intrinsic motivation in hard exploration tasks, and that it predicts optimistic estimates for novel state-action pairs.", "output": [ "We augment the Q-value estimates with a count-based bonus that ensures optimism during action selection and bootstrapping, even if the Q-value estimates are pessimistic." ] }, { "id": "task668-dc7a8945e98f406fa2eb2f93b5abbe26", "input": "Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error. However, compressive networks are desired in many real world applications and direct training of small networks may be trapped in local optima. In this paper, instead of pruning or distilling over-parameterized models to compressive ones, we propose a new approach based on \\emph{differential inclusions of inverse scale spaces}, that generates a family of models from simple to complex ones by coupling gradient descent and mirror descent to explore model structural sparsity. It has a simple discretization, called the Split Linearized Bregman Iteration (SplitLBI), whose global convergence analysis in deep learning is established that from any initializations, algorithmic iterations converge to a critical point of empirical risks. Experimental evidence shows that\\ SplitLBI may achieve state-of-the-art performance in large scale training on ImageNet-2012 dataset etc., while with \\emph{early stopping} it unveils effective subnet architecture with comparable test accuracies to dense models after retraining instead of pruning well-trained ones.", "output": [ "SplitLBI is applied to deep learning to explore model structural sparsity, achieving state-of-the-art performance in ImageNet-2012 and unveiling effective subnet architecture.", "Proposes an optimization based algorithm for finding important sparse structures of large-scale neural networks by coupling the learning of weight matrix and sparsity constraints, offering guaranteed convergence on nonconvex optimization problems." ] }, { "id": "task668-82c1b802a9584368abe40fde81882ba7", "input": "Neural networks have recently shown excellent performance on numerous classi- fication tasks. These networks often have a large number of parameters and thus require much data to train. When the number of training data points is small, however, a network with high flexibility will quickly overfit the training data, resulting in a large model variance and a poor generalization performance. To address this problem, we propose a new ensemble learning method called InterBoost for small-sample image classification. In the training phase, InterBoost first randomly generates two complementary datasets to train two base networks of the same structure, separately, and then next two complementary datasets for further training the networks are generated through interaction (or information sharing) between the two base networks trained previously. This interactive training process continues iteratively until a stop criterion is met. In the testing phase, the outputs of the two networks are combined to obtain one final score for classification. Detailed analysis of the method is provided for an in-depth understanding of its mechanism.", "output": [ "In the paper, we proposed an ensemble method called InterBoost for training neural networks for small-sample classification. The method has better generalization performance than other ensemble methods, and reduces variances significantly." ] }, { "id": "task668-b975c683a1284fc9a77994f330aa1291", "input": "A weakly supervised learning based clustering framework is proposed in this paper. As the core of this framework, we introduce a novel multiple instance learning task based on a bag level label called unique class count (ucc), which is the number of unique classes among all instances inside the bag. In this task, no annotations on individual instances inside the bag are needed during training of the models. We mathematically prove that with a perfect ucc classifier, perfect clustering of individual instances inside the bags is possible even when no annotations on individual instances are given during training. We have constructed a neural network based ucc classifier and experimentally shown that the clustering performance of our framework with our weakly supervised ucc classifier is comparable to that of fully supervised learning models where labels for all instances are known. Furthermore, we have tested the applicability of our framework to a real world task of semantic segmentation of breast cancer metastases in histological lymph node sections and shown that the performance of our weakly supervised framework is comparable to the performance of a fully supervised Unet model.", "output": [ "A weakly supervised learning based clustering framework performs comparable to that of fully supervised learning models by exploiting unique class count." ] }, { "id": "task668-0a15786fcc184049b005311849542b3e", "input": "Recent image super-resolution(SR) studies leverage very deep convolutional neural networks and the rich hierarchical features they offered, which leads to better reconstruction performance than conventional methods. However, the small receptive fields in the up-sampling and reconstruction process of those models stop them to take full advantage of global contextual information. This causes problems for further performance improvement. In this paper, inspired by image reconstruction principles of human visual system, we propose an image super-resolution global reasoning network (SRGRN) to effectively learn the correlations between different regions of an image, through global reasoning. Specifically, we propose global reasoning up-sampling module (GRUM) and global reasoning reconstruction block (GRRB). They construct a graph model to perform relation reasoning on regions of low resolution (LR) images.They aim to reason the interactions between different regions in the up-sampling and reconstruction process and thus leverage more contextual information to generate accurate details. Our proposed SRGRN are more robust and can handle low resolution images that are corrupted by multiple types of degradation. Extensive experiments on different benchmark data-sets show that our model outperforms other state-of-the-art methods. Also our model is lightweight and consumes less computing power, which makes it very suitable for real life deployment.", "output": [ "A state-of-the-art model based on global reasoning for image super-resolution" ] }, { "id": "task668-05feb9d95cd544e0953c516d63d5d77f", "input": "Recently, Generative Adversarial Networks (GANs) have emerged as a popular alternative for modeling complex high dimensional distributions. Most of the existing works implicitly assume that the clean samples from the target distribution are easily available. However, in many applications, this assumption is violated. In this paper, we consider the observation setting in which the samples from a target distribution are given by the superposition of two structured components, and leverage GANs for learning of the structure of the components. We propose a novel framework, demixing-GAN, which learns the distribution of two components at the same time. Through extensive numerical experiments, we demonstrate that the proposed framework can generate clean samples from unknown distributions, which further can be used in demixing of the unseen test images.", "output": [ "An unsupervised learning approach for separating two structured signals from their superposition" ] }, { "id": "task668-a59d6259fc45492d9914bfd16de9c803", "input": "Supervised deep learning methods require cleanly labeled large-scale datasets, but collecting such data is difficult and sometimes impossible. There exist two popular frameworks to alleviate this problem: semi-supervised learning and robust learning to label noise. Although these frameworks relax the restriction of supervised learning, they are studied independently. Hence, the training scheme that is suitable when only small cleanly-labeled data are available remains unknown. In this study, we consider learning from bi-quality data as a generalization of these studies, in which a small portion of data is cleanly labeled, and the rest is corrupt. Under this framework, we compare recent algorithms for semi-supervised and robust learning. The results suggest that semi-supervised learning outperforms robust learning with noisy labels. We also propose a training strategy for mixing mixup techniques to learn from such bi-quality data effectively.", "output": [ "We propose to compare semi-supervised and robust learning to noisy label under a shared setting", "The authors propose a strategy based on mixup for training a model in a formal setting that includes the semi-supervised and the robust learning tasks as special cases." ] }, { "id": "task668-3b9ce42c91e04ec8b54477a61e15ca3a", "input": "There is a previously identified equivalence between wide fully connected neural networks (FCNs) and Gaussian processes (GPs). This equivalence enables, for instance, test set predictions that would have resulted from a fully Bayesian, infinitely wide trained FCN to be computed without ever instantiating the FCN, but by instead evaluating the corresponding GP. In this work, we derive an analogous equivalence for multi-layer convolutional neural networks (CNNs) both with and without pooling layers, and achieve state of the art results on CIFAR10 for GPs without trainable kernels. We also introduce a Monte Carlo method to estimate the GP corresponding to a given neural network architecture, even in cases where the analytic form has too many terms to be computationally feasible. \n\n Surprisingly, in the absence of pooling layers, the GPs corresponding to CNNs with and without weight sharing are identical. As a consequence, translation equivariance, beneficial in finite channel CNNs trained with stochastic gradient descent (SGD), is guaranteed to play no role in the Bayesian treatment of the infinite channel limit - a qualitative difference between the two regimes that is not present in the FCN case. We confirm experimentally, that while in some scenarios the performance of SGD-trained finite CNNs approaches that of the corresponding GPs as the channel count increases, with careful tuning SGD-trained CNNs can significantly outperform their corresponding GPs, suggesting advantages from SGD training compared to fully Bayesian parameter estimation.", "output": [ "Finite-width SGD trained CNNs vs. infinitely wide fully Bayesian CNNs. Who wins?", "The paper establishes a connection between infinite channel Bayesian convolutional neural network and Gaussian processes." ] }, { "id": "task668-3e01ce286a2849749b1dc3fd1fd4a4a9", "input": "The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists. Two major reasons are that neurons would need to send two different types of signal in the forward and backward phases, and that pairs of neurons would need to communicate through symmetric bidirectional connections.\n We present a simple two-phase learning procedure for fixed point recurrent networks that addresses both these issues.\n In our model, neurons perform leaky integration and synaptic weights are updated through a local mechanism.\n Our learning method extends the framework of Equilibrium Propagation to general dynamics, relaxing the requirement of an energy function.\n As a consequence of this generalization, the algorithm does not compute the true gradient of the objective function,\n but rather approximates it at a precision which is proven to be directly related to the degree of symmetry of the feedforward and feedback weights.\n We show experimentally that the intrinsic properties of the system lead to alignment of the feedforward and feedback weights, and that our algorithm optimizes the objective function.", "output": [ "We describe a biologically plausible learning algorithm for fixed point recurrent networks without tied weights" ] }, { "id": "task668-d1eb3813d6d84aad920c5bd089c11ce7", "input": "Learning can be framed as trying to encode the mutual information between input and output while discarding other information in the input. Since the distribution between input and output is unknown, also the true mutual information is. To quantify how difficult it is to learn a task, we calculate a observed mutual information score by dividing the estimated mutual information by the entropy of the input. We substantiate this score analytically by showing that the estimated mutual information has an error that increases with the entropy of the data. Intriguingly depending on how the data is represented the observed entropy and mutual information can vary wildly. There needs to be a match between how data is represented and how a model encodes it. Experimentally we analyze image-based input data representations and demonstrate that performance outcomes of extensive network architectures searches are well aligned to the calculated score. Therefore to ensure better learning outcomes, representations may need to be tailored to both task and model to align with the implicit distribution of the model.", "output": [ "We take a step towards measuring learning task difficulty and demonstrate that in practice performance strongly depends on the match of the representation of the information and the model interpreting it." ] }, { "id": "task668-6a967c8e97204e97b81df93ea524dfb9", "input": "Disentangled encoding is an important step towards a better representation learning. However, despite the numerous efforts, there still is no clear winner that captures the independent features of the data in an unsupervised fashion. In this work we empirically evaluate the performance of six unsupervised disentanglement approaches on the mpi3d toy dataset curated and released for the NeurIPS 2019 Disentanglement Challenge. The methods investigated in this work are Beta-VAE, Factor-VAE, DIP-I-VAE, DIP-II-VAE, Info-VAE, and Beta-TCVAE. The capacities of all models were progressively increased throughout the training and the hyper-parameters were kept intact across experiments. The methods were evaluated based on five disentanglement metrics, namely, DCI, Factor-VAE, IRS, MIG, and SAP-Score. Within the limitations of this study, the Beta-TCVAE approach was found to outperform its alternatives with respect to the normalized sum of metrics. However, a qualitative study of the encoded latents reveal that there is not a consistent correlation between the reported metrics and the disentanglement potential of the model.", "output": [ "Inadequacy of Disentanglement Metrics" ] }, { "id": "task668-c35a02a9b4f846da95e8e04bf5d2bf39", "input": "Wilson et al. (2017) showed that, when the stepsize schedule is properly designed, stochastic gradient generalizes better than ADAM (Kingma & Ba, 2014). In light of recent work on hypergradient methods (Baydin et al., 2018), we revisit these claims to see if such methods close the gap between the most popular optimizers. As a byproduct, we analyze the true benefit of these hypergradient methods compared to more classical schedules, such as the fixed decay of Wilson et al. (2017). In particular, we observe they are of marginal help since their performance varies significantly when tuning their hyperparameters. Finally, as robustness is a critical quality of an optimizer, we provide a sensitivity analysis of these gradient based optimizers to assess how challenging their tuning is.", "output": [ "We provide a study trying to see how the recent online learning rate adaptation extends the conclusion made by Wilson et al. 2018 about adaptive gradient methods, along with comparison and sensitivity analysis.", "Reports the results of testing several stepsize adjustment related methods including vanilla SGD, SGD with Neserov momentum, and ADAM and compares those methods with hypergradient and without. " ] }, { "id": "task668-187e9fdf1d8f47368186c56148bc5a95", "input": "Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but the underlying mathematics are not well understood. We compute deep convolutional network generators by inverting a fixed embedding operator. Therefore, they do not require to be optimized with a discriminator or an encoder. The embedding is Lipschitz continuous to deformations so that generators transform linear interpolations between input white noise vectors into deformations between output images. This embedding is computed with a wavelet Scattering transform. Numerical experiments demonstrate that the resulting Scattering generators have similar properties as GANs or VAEs, without learning a discriminative network or an encoder.", "output": [ "We introduce generative networks that do not require to be learned with a discriminator or an encoder; they are obtained by inverting a special embedding operator defined by a wavelet Scattering transform.", "Introduces scattering transforms as image generative models in the context of Generative Adversarial Networks and suggest why they could be seen as Gaussianization transforms with controlled information loss and invertibility. ", "The paper proposes a generative model for images that does no require to learn a discriminator (as in GAN’s) or learned embedding." ] }, { "id": "task668-7c4d8b1c9b38497eba4ddf7b3f1519bf", "input": "We present a method which learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents. Our method is based on a graph-structured variational recurrent neural network, which is trained end-to-end to infer the current state of the (partially observed) world, as well as to forecast future states. We show that our method outperforms various baselines on two sports datasets, one based on real basketball trajectories, and one generated by a soccer game engine.", "output": [ "We present a method which learns to integrate temporal information and ambiguous visual information in the context of interacting agents.", "The authors propose Graph VRNN which models the interaction of multiple agents by deploying a VRNN for each agent", "This paper presents a graph neural network based architecture that is trained to locate and model the interactions of agents in an environment directly from pixels and show advantage of model for tracking tasks and forecasting agent locations." ] }, { "id": "task668-8980d73e29f043b7950ace81b90422ae", "input": "In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial- and channel-wise features. To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts. Specifically, we design trunk branch and (non-)local mask branch in each (non-)local attention block. The trunk branch is used to extract hierarchical features. Local and non-local mask branches aim to adaptively rescale these hierarchical features with mixed attentions. The local mask branch concentrates on more local structures with convolutional operations, while non-local attention considers more about long-range dependencies in the whole feature map. Furthermore, we propose residual local and non-local attention learning to train the very deep network, which further enhance the representation ability of the network. Our proposed method can be generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution. Experiments demonstrate that our method obtains comparable or better results compared with recently leading methods quantitatively and visually.", "output": [ "New state-of-the-art framework for image restoration", "The paper proposes a convolutional neural network architecture that includes blocks for local and non-local attention mechanisms, which are claimed to be responsible for achieving excellent results in four image restoration applications.", "This paper proposes a residual non-local attention network for image restoration" ] }, { "id": "task668-02022514e1c845958e4f341e5bf65f61", "input": "In this paper we study the problem of learning the weights of a deep convolutional neural network. We consider a network where convolutions are carried out over non-overlapping patches with a single kernel in each layer. We develop an algorithm for simultaneously learning all the kernels from the training data. Our approach dubbed Deep Tensor Decomposition (DeepTD) is based on a rank-1 tensor decomposition. We theoretically investigate DeepTD under a realizable model for the training data where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to planted convolutional kernels. We show that DeepTD is data-efficient and provably works as soon as the sample size exceeds the total number of convolutional weights in the network. Our numerical experiments demonstrate the effectiveness of DeepTD and verify our theoretical findings.", "output": [ "We consider a simplified deep convolutional neural network model. We show that all layers of this network can be approximately learned with a proper application of tensor decomposition.", "Provides theoretical guarantees for learning deep convolutional neural networks using rank-one tensor decomposition.", "This paper proposes a learning method for a restricted case of deep convolutional networks, where the layers are limited to the non-overlapping case and have only one output channel per layer", "Analyzes the problem of learning a very special class of CNNs: each layers consists of a single filter, applied to non-overlapping patches of the input." ] }, { "id": "task668-37a23f9596c6460bb02fe3f1ee3ed4bc", "input": "Many processes can be concisely represented as a sequence of events leading from a starting state to an end state. Given raw ingredients, and a finished cake, an experienced chef can surmise the recipe. Building upon this intuition, we propose a new class of visual generative models: goal-conditioned predictors (GCP). Prior work on video generation largely focuses on prediction models that only observe frames from the beginning of the video. GCP instead treats videos as start-goal transformations, making video generation easier by conditioning on the more informative context provided by the first and final frames. Not only do existing forward prediction approaches synthesize better and longer videos when modified to become goal-conditioned, but GCP models can also utilize structures that are not linear in time, to accomplish hierarchical prediction . To this end, we study both auto-regressive GCP models and novel tree-structured GCP models that generate frames recursively, splitting the video iteratively into finer and finer segments delineated by subgoals . In experiments across simulated and real datasets, our GCP methods generate high-quality sequences over long horizons . Tree-structured GCPs are also substantially easier to parallelize than auto-regressive GCPs, making training and inference very efficient, and allowing the model to train on sequences that are thousands of frames in length.Finally, we demonstrate the utility of GCP approaches for imitation learning in the setting without access to expert actions . Videos are on the supplementary website: https://sites.google.com/view/video-gcp", "output": [ "We propose a new class of visual generative models: goal-conditioned predictors. We show experimentally that conditioning on the goal allows to reduce uncertainty and produce predictions over much longer horizons.", "This paper reformulates video prediction problem as interpolation instead of extrapolation by conditioning the prediction on the start and end (goal) frame, resulting in higher quality predictions." ] }, { "id": "task668-c835798f2e1a4bd7ba84e8026df72e7f", "input": " We establish the relation between Distributional RL and the Upper Confidence Bound (UCB) approach to exploration.\n In this paper we show that the density of the Q function estimated by Distributional RL can be successfully used for the estimation of UCB. This approach does not require counting and, therefore, generalizes well to the Deep RL. We also point to the asymmetry of the empirical densities estimated by the Distributional RL algorithms like QR-DQN. This observation leads to the reexamination of the variance's performance in the UCB type approach to exploration. We introduce truncated variance as an alternative estimator of the UCB and a novel algorithm based on it. We empirically show that newly introduced algorithm achieves better performance in multi-armed bandits setting. Finally, we extend this approach to high-dimensional setting and test it on the Atari 2600 games. New approach achieves better performance compared to QR-DQN in 26 of games, 13 ties out of 49 games.", "output": [ "Exploration using Distributional RL and truncagted variance.", "Presents an RL method to manage exploration-explotation trade-offs via UCB techniques.", "A method to use the distribution learned by Quantile Regression DQN for exploration, in place of the usual epsilon-greedy strategy.", "Proposes new algorithsms (QUCB and QUCB+) to handle the exploration tradeoff in Multi-Armed Bendits and more generally in Reinforcement Learning" ] }, { "id": "task668-742a3df075504feb91318cca4a6f1dd1", "input": "This paper addresses the problem of incremental domain adaptation (IDA). We assume each domain comes sequentially, and that we could only access data in the current domain. The goal of IDA is to build a unified model performing well on all the encountered domains. We propose to augment a recurrent neural network (RNN) with a directly parameterized memory bank, which is retrieved by an attention mechanism at each step of RNN transition. The memory bank provides a natural way of IDA: when adapting our model to a new domain, we progressively add new slots to the memory bank, which increases the model capacity. We learn the new memory slots and fine-tune existing parameters by back-propagation. \n Experiments show that our approach significantly outperforms naive fine-tuning and previous work on IDA, including elastic weight consolidation and the progressive neural network. Compared with expanding hidden states, our approach is more robust for old domains, shown by both empirical and theoretical results.", "output": [ "We present a neural memory-based architecture for incremental domain adaptation, and provide theoretical and empirical results." ] }, { "id": "task668-867ddfc64ac64618832ff3663de8aaee", "input": "Mini-batch stochastic gradient descent (SGD) is state of the art in large scale distributed training. The scheme can reach a linear speed-up with respect to the number of workers, but this is rarely seen in practice as the scheme often suffers from large network delays and bandwidth limits. To overcome this communication bottleneck recent works propose to reduce the communication frequency. An algorithm of this type is local SGD that runs SGD independently in parallel on different workers and averages the sequences only once in a while. This scheme shows promising results in practice, but eluded thorough theoretical analysis.\n \n We prove concise convergence rates for local SGD on convex problems and show that it converges at the same rate as mini-batch SGD in terms of number of evaluated gradients, that is, the scheme achieves linear speed-up in the number of workers and mini-batch size. The number of communication rounds can be reduced up to a factor of T^{1/2}---where T denotes the number of total steps---compared to mini-batch SGD. This also holds for asynchronous implementations.\n\n Local SGD can also be used for large scale training of deep learning models. The results shown here aim serving as a guideline to further explore the theoretical and practical aspects of local SGD in these applications.", "output": [ "We prove that parallel local SGD achieves linear speedup with much lesser communication than parallel mini-batch SGD.", "Provides a convergence proof for local SGD, and proves that local SGD can provide the same speedup gains as minibatch, but may be able to communicate significantly less.", "This paper presents an analysis of local SGD and bounds on how frequent the estimators obtained by running SGD required to be averaged in order to yield linear parallelization speedups.", "The authors analyze the local SGD algorithm, where $K$ parallel chains of SGD are run, and the iterates are occasionally synchronized across machines by averaging" ] }, { "id": "task668-8f7412239f6a420e8d29242c2dd4d9f1", "input": "The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class. The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and generalizing the model to a new task. Yet, even with such meta-learning, the low-data problem in the novel classification task still remains. In this paper, we propose Transductive Propagation Network (TPN), a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem. Specifically, we propose to learn to propagate labels from labeled instances to unlabeled test instances, by learning a graph construction module that exploits the manifold structure in the data. TPN jointly learns both the parameters of feature embedding and the graph construction in an end-to-end manner. We validate TPN on multiple benchmark datasets, on which it largely outperforms existing few-shot learning approaches and achieves the state-of-the-art results.", "output": [ "We propose a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem.", "\nThis paper proposes to address few-shot learning in a transductive way by learning a label propagation model in an end-to-end manner, the first to learn label propagation for transductive few-shot learning and produced effective empirical results. ", "This paper proposes a meta-learning framework that leverages unlabeled data by learning the graph-based label propogation in an end-to-end manner.", "Studies few-host learning in a transductive setting: using meta learning to learn to propagate labels from training samples to test samples. " ] }, { "id": "task668-5dc35d11bc0c4ded80764c86675ca04e", "input": "With the rise in employment of deep learning methods in safety-critical scenarios, interpretability is more essential than ever before. Although many different directions regarding interpretability have been explored for visual modalities, time-series data has been neglected with only a handful of methods tested due to their poor intelligibility. We approach the problem of interpretability in a novel way by proposing TSInsight where we attach an auto-encoder with a sparsity-inducing norm on its output to the classifier and fine-tune it based on the gradients from the classifier and a reconstruction penalty. The auto-encoder learns to preserve features that are important for the prediction by the classifier and suppresses the ones that are irrelevant i.e. serves as a feature attribution method to boost interpretability. In other words, we ask the network to only reconstruct parts which are useful for the classifier i.e. are correlated or causal for the prediction. In contrast to most other attribution frameworks, TSInsight is capable of generating both instance-based and model-based explanations. We evaluated TSInsight along with other commonly used attribution methods on a range of different time-series datasets to validate its efficacy. Furthermore, we analyzed the set of properties that TSInsight achieves out of the box including adversarial robustness and output space contraction. The obtained results advocate that TSInsight can be an effective tool for the interpretability of deep time-series models.", "output": [ "We present an attribution technique leveraging sparsity inducing norms to achieve interpretability." ] }, { "id": "task668-00f03dfc9cfe4bc2b81201700301f5c7", "input": "Partially observable Markov decision processes (POMDPs) are a widely-used framework to model decision-making with uncertainty about the environment and under stochastic outcome. In conventional POMDP models, the observations that the agent receives originate from fixed known distribution. However, in a variety of real-world scenarios the agent has an active role in its perception by selecting which observations to receive. Due to combinatorial nature of such selection process, it is computationally intractable to integrate the perception decision with the planning decision. To prevent such expansion of the action space, we propose a greedy strategy for observation selection that aims to minimize the uncertainty in state. \n We develop a novel point-based value iteration algorithm that incorporates the greedy strategy to achieve near-optimal uncertainty reduction for sampled belief points. This in turn enables the solver to efficiently approximate the reachable subspace of belief simplex by essentially separating computations related to perception from planning.\n Lastly, we implement the proposed solver and demonstrate its performance and computational advantage in a range of robotic scenarios where the robot simultaneously performs active perception and planning.", "output": [ "We develop a point-based value iteration solver for POMDPs with active perception and planning tasks." ] }, { "id": "task668-b3d34b590086471387bb48d456d5bbfc", "input": "Solving tasks with sparse rewards is one of the most important challenges in reinforcement learning. In the single-agent setting, this challenge has been addressed by introducing intrinsic rewards that motivate agents to explore unseen regions of their state spaces. Applying these techniques naively to the multi-agent setting results in agents exploring independently, without any coordination among themselves. We argue that learning in cooperative multi-agent settings can be accelerated and improved if agents coordinate with respect to what they have explored. In this paper we propose an approach for learning how to dynamically select between different types of intrinsic rewards which consider not just what an individual agent has explored, but all agents, such that the agents can coordinate their exploration and maximize extrinsic returns. Concretely, we formulate the approach as a hierarchical policy where a high-level controller selects among sets of policies trained on different types of intrinsic rewards and the low-level controllers learn the action policies of all agents under these specific rewards. We demonstrate the effectiveness of the proposed approach in a multi-agent gridworld domain with sparse rewards, and then show that our method scales up to more complex settings by evaluating on the VizDoom platform.", "output": [ "We propose several intrinsic reward functions for encouraging coordinated exploration in multi-agent problems, and introduce an approach to dynamically selecting the best exploration method for a given task, online." ] }, { "id": "task668-548479195f734ff5996fd5fed6ccb19d", "input": "We note that common implementations of adaptive gradient algorithms, such as Adam, limit the potential benefit of weight decay regularization, because the weights do not decay multiplicatively (as would be expected for standard weight decay) but by an additive constant factor. \n We propose a simple way to resolve this issue by decoupling weight decay and the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) \ndecouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam, and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter).\n We also demonstrate that longer optimization runs require smaller weight decay values for optimal results and introduce a normalized variant of weight decay to reduce this dependence. Finally, we propose a version of Adam with warm restarts (AdamWR) that has strong anytime performance while achieving state-of-the-art results on CIFAR-10 and ImageNet32x32. \n Our source code will become available after the review process.", "output": [ "Fixing weight decay regularization in adaptive gradient methods such as Adam", "Proposes idea to decouple the weight decay from the number of steps taken by the optimization process.", "The paper presents an alternative way to implement weight decay in Adam with empirical results shown", "Investigates weight decay issues lied in the SGD variants and proposes the decoupling method between weight decay and the gradient-based update." ] }, { "id": "task668-221cce4780924f31bc4b6d45b0679878", "input": "Message-passing neural networks (MPNNs) have been successfully applied in a wide variety of applications in the real world. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the ability to capture long-range dependencies in disassortative graphs. Few studies have noticed the weaknesses from different perspectives. From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. The behind basic idea is the aggregation on a graph can benefit from a continuous space underlying the graph. The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation. We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs. Experimental results show the proposed Geom-GCN achieved state-of-the-art performance on a wide range of open datasets of graphs.", "output": [ "For graph neural networks, the aggregation on a graph can benefit from a continuous space underlying the graph." ] }, { "id": "task668-dd9fdd3eb2324084ac5bcfe3cb753180", "input": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset.", "output": [ "We train generative adversarial networks in a progressive fashion, enabling us to generate high-resolution images with high quality.", "Introduces progressive growing and a simple parameter-free minibatch summary statistic feature for use in GAN training to enable synthesis of high-resolution images." ] }, { "id": "task668-a0a3d813605d4127ab2497a079c62a46", "input": "Generative Adversarial Networks (GANs) have been proposed as an approach to learning generative models. While GANs have demonstrated promising performance on multiple vision tasks, their learning dynamics are not yet well understood, neither in theory nor in practice. In particular, the work in this domain has been focused so far only on understanding the properties of the stationary solutions that this dynamics might converge to, and of the behavior of that dynamics in this solutions’ immediate neighborhood.\n\n To address this issue, in this work we take a first step towards a principled study of the GAN dynamics itself. To this end, we propose a model that, on one hand, exhibits several of the common problematic convergence behaviors (e.g., vanishing gradient, mode collapse, diverging or oscillatory behavior), but on the other hand, is sufficiently simple to enable rigorous convergence analysis.\n\n This methodology enables us to exhibit an interesting phenomena: a GAN with an optimal discriminator provably converges, while guiding the GAN training using only a first order approximation of the discriminator leads to unstable GAN dynamics and mode collapse. This suggests that such usage of the first order approximation of the discriminator, which is a de-facto standard in all the existing GAN dynamics, might be one of the factors that makes GAN training so challenging in practice. Additionally, our convergence result constitutes the first rigorous analysis of a dynamics of a concrete parametric GAN.", "output": [ "To understand GAN training, we define simple GAN dynamics, and show quantitative differences between optimal and first order updates in this model.", "The authors study the impact of GANs in settings where at each iteration, the discriminator trains to convergence and the generator updates with gradient steps, or where a few gradient steps are done for the disciminator and generator.", "This paper studies the dynamics of adversarial training of GANs on a Gaussian mixture model" ] }, { "id": "task668-66ea5d8cd6d04568b11687ecd379e745", "input": "This paper proposes a neural end-to-end text-to-speech (TTS) model which can control latent attributes in the generated speech that are rarely annotated in the training data, such as speaking style, accent, background noise, and recording conditions. The model is formulated as a conditional generative model with two levels of hierarchical latent variables. The first level is a categorical variable, which represents attribute groups (e.g. clean/noisy) and provides interpretability. The second level, conditioned on the first, is a multivariate Gaussian variable, which characterizes specific attribute configurations (e.g. noise level, speaking rate) and enables disentangled fine-grained control over these attributes. This amounts to using a Gaussian mixture model (GMM) for the latent distribution. Extensive evaluation demonstrates its ability to control the aforementioned attributes. In particular, it is capable of consistently synthesizing high-quality clean speech regardless of the quality of the training data for the target speaker.", "output": [ "Building a TTS model with Gaussian Mixture VAEs enables fine-grained control of speaking style, noise condition, and more.", "Describes the conditioned GAN model to generate speaker conditioned Mel spectra by augmenting the z-space corresponding to the identification", "This paper proposes a two layer latent variable model to obtain disentangled latent representation, thus facilitating fine-grained control over various attributes", "This paper proposes a model that can control non-annotated attributes such as speaking style, accent, background noise, etc." ] }, { "id": "task668-fdf0040de86243eea9ea4e8418a4d255", "input": "Recent advances have made it possible to create deep complex-valued neural networks. Despite this progress, the potential power of fully complex intermediate computations and representations has not yet been explored for many challenging learning problems. Building on recent advances, we propose a novel mechanism for extracting signals in the frequency domain. As a case study, we perform audio source separation in the Fourier domain. Our extraction mechanism could be regarded as a local ensembling method that combines a complex-valued convolutional version of Feature-Wise Linear Modulation (FiLM) and a signal averaging operation. We also introduce a new explicit amplitude and phase-aware loss, which is scale and time invariant, taking into account the complex-valued components of the spectrogram. Using the Wall Street Journal Dataset, we compare our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss. When operating in the complex-valued frequency domain, our deep complex-valued network substantially outperforms its real-valued counterparts even with half the depth and a third of the parameters. Our proposed mechanism improves significantly deep complex-valued networks' performance and we demonstrate the usefulness of its regularizing effect.", "output": [ "New Signal Extraction Method in the Fourier Domain", "Contributes a complex-valued convolutional version of the Feature-Wise Linear Modulation which allows parameter optimization and designs a loss which takes into account magnitude and phase." ] }, { "id": "task668-5165b7e4d92d4ed6975d91f259084ecb", "input": " Reinforcement learning methods have recently achieved impressive results on a wide range of control problems. However, especially with complex inputs, they still require an extensive amount of training data in order to converge to a meaningful solution. This limitation largely prohibits their usage for complex input spaces such as video signals, and it is still impossible to use it for a number of complex problems in a real world environments, including many of those for video based control. Supervised learning, on the contrary, is capable of learning on a relatively small number of samples, however it does not take into account reward-based control policies and is not capable to provide independent control policies. In this article we propose a model-free control method, which uses a combination of reinforcement and supervised learning for autonomous control and paves the way towards policy based control in real world environments. We use SpeedDreams/TORCS video game to demonstrate that our approach requires much less samples (hundreds of thousands against millions or tens of millions) comparing to the state-of-the-art reinforcement learning techniques on similar data, and at the same time overcomes both supervised and reinforcement learning approaches in terms of quality. Additionally, we demonstrate the applicability of the method to MuJoCo control problems.", "output": [ "The new combination of reinforcement and supervised learning, dramatically decreasing the number of required samples for training on video", "This paper proposes leveraging labelled controlled data to accelerate reinforcement-based learning of a control policy" ] }, { "id": "task668-098d9319ebbf4691baf2227958d468a5", "input": "Recurrent neural networks (RNNs) are widely used to model sequential data but\n their non-linear dependencies between sequence elements prevent parallelizing\n training over sequence length. We show the training of RNNs with only linear\n sequential dependencies can be parallelized over the sequence length using the\n parallel scan algorithm, leading to rapid training on long sequences even with\n small minibatch size. We develop a parallel linear recurrence CUDA kernel and\n show that it can be applied to immediately speed up training and inference of\n several state of the art RNN architectures by up to 9x. We abstract recent work\n on linear RNNs into a new framework of linear surrogate RNNs and develop a\n linear surrogate model for the long short-term memory unit, the GILR-LSTM, that\n utilizes parallel linear recurrence. We extend sequence learning to new\n extremely long sequence regimes that were previously out of reach by\n successfully training a GILR-LSTM on a synthetic sequence classification task\n with a one million timestep dependency.\n", "output": [ "use parallel scan to parallelize linear recurrent neural nets. train model on length 1 million dependency", "Proposes accelerating RNN by applying the method from Blelloch.", "The authors propose a parallel algorithm for Linear Surrogate RNNs, which produces speedups over the existing implements of Quasi-RNN, SRU, and LSTM." ] }, { "id": "task668-f53cb61a57a04700b23939ed21b1f7fc", "input": "We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker. The aim of training is not to produce a neural network with fixed weights, which is then deployed as a TTS system. Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers. We introduce and benchmark three strategies:\n (i) learning the speaker embedding while keeping the WaveNet core fixed,\n (ii) fine-tuning the entire architecture with stochastic gradient descent, and\n (iii) predicting the speaker embedding with a trained neural network encoder.\n The experiments show that these approaches are successful at adapting the multi-speaker neural network to new speakers, obtaining state-of-the-art results in both sample naturalness and voice similarity with merely a few minutes of audio data from new speakers.", "output": [ "Sample efficient algorithms to adapt a text-to-speech model to a new voice style with the state-of-the-art performance." ] }, { "id": "task668-a843e3508bda424db749c02000d46e58", "input": "This paper presents GumbelClip, a set of modifications to the actor-critic algorithm, for off-policy reinforcement learning. GumbelClip uses the concepts of truncated importance sampling along with additive noise to produce a loss function enabling the use of off-policy samples. The modified algorithm achieves an increase in convergence speed and sample efficiency compared to on-policy algorithms and is competitive with existing off-policy policy gradient methods while being significantly simpler to implement. The effectiveness of GumbelClip is demonstrated against existing on-policy and off-policy actor-critic algorithms on a subset of the Atari domain.", "output": [ "With a set of modifications, under 10 LOC, to A2C you get an off-policy actor-critic that outperforms A2C and performs similarly to ACER. The modifications are large batchsizes, aggressive clamping, and policy \"forcing\" with gumbel noise." ] }, { "id": "task668-f6941fd97187476b87f75cd659271f0b", "input": "Unsupervised monocular depth estimation has made great progress after deep\n learning is involved. Training with binocular stereo images is considered as a\n good option as the data can be easily obtained. However, the depth or disparity\n prediction results show poor performance for the object boundaries. The main\n reason is related to the handling of occlusion areas during the training. In this paper,\n we propose a novel method to overcome this issue. Exploiting disparity maps\n property, we generate an occlusion mask to block the back-propagation of the occlusion\n areas during image warping. We also design new networks with flipped\n stereo images to induce the networks to learn occluded boundaries. It shows that\n our method achieves clearer boundaries and better evaluation results on KITTI\n driving dataset and Virtual KITTI dataset.", "output": [ "This paper propose a mask method which solves the previous blurred results of unsupervised monocular depth estimation caused by occlusion" ] }, { "id": "task668-576fe335c51d4efd94090e1b880ccd60", "input": "Deep network compression seeks to reduce the number of parameters in the network while maintaining a certain level of performance. Deep network distillation seeks to train a smaller network that matches soft-max performance of a larger network. While both regimes have led to impressive performance for their respective goals, neither provide insight into the importance of a given layer in the original model, which is useful if we are to improve our understanding of these highly parameterized models. In this paper, we present the concept of deep net triage, which individually assesses small blocks of convolution layers to understand their collective contribution to the overall performance, which we call \\emph{criticality}. We call it triage because we assess this criticality by answering the question: what is the impact to the health of the overall network if we compress a block of layers into a single layer.\n We propose a suite of triage methods and compare them on problem spaces of varying complexity. We ultimately show that, across these problem spaces, deep net triage is able to indicate the of relative importance of different layers. Surprisingly, our local structural compression technique also leads to an improvement in overall accuracy when the final model is fine-tuned globally.", "output": [ "We seek to understand learned representations in compressed networks via an experimental regime we call deep net triage", "Compares various initialization and training methods of transferring knowledge from VGG network to a smaller student network by replacing blocks of layers with single layers.", "This paper presents five methods for doing triaging or block layer compression for deep networks.", "The paper proposes a method to compress a block of layers in a NN that evaluates several different sub-approaches" ] }, { "id": "task668-402aa4be9ac34e7aa4b89ec9a786e76d", "input": "Derivative-free optimization (DFO) using trust region methods is frequently used for machine learning applications, such as (hyper-)parameter optimization without the derivatives of objective functions known. Inspired by the recent work in continuous-time minimizers, our work models the common trust region methods with the exploration-exploitation using a dynamical system coupling a pair of dynamical processes. While the first exploration process searches the minimum of the blackbox function through minimizing a time-evolving surrogation function, another exploitation process updates the surrogation function time-to-time using the points traversed by the exploration process. The efficiency of derivative-free optimization thus depends on ways the two processes couple. In this paper, we propose a novel dynamical system, namely \\ThePrev---\\underline{S}tochastic \\underline{H}amiltonian \\underline{E}xploration and \\underline{E}xploitation, that surrogates the subregions of blackbox function using a time-evolving quadratic function, then explores and tracks the minimum of the quadratic functions using a fast-converging Hamiltonian system. The \\ThePrev\\ algorithm is later provided as a discrete-time numerical approximation to the system. To further accelerate optimization, we present \\TheName\\ that parallelizes multiple \\ThePrev\\ threads for concurrent exploration and exploitation. Experiment results based on a wide range of machine learning applications show that \\TheName\\ outperform a boarder range of derivative-free optimization algorithms with faster convergence speed under the same settings.", "output": [ "a new derivative-free optimization algorithms derived from Nesterov's accelerated gradient methods and Hamiltonian dynamics" ] }, { "id": "task668-81a2075ee674402bb3927130c9232151", "input": "As Machine Learning (ML) gets applied to security-critical or sensitive domains, there is a growing need for integrity and privacy for outsourced ML computations. A pragmatic solution comes from Trusted Execution Environments (TEEs), which use hardware and software protections to isolate sensitive computations from the untrusted software stack. However, these isolation guarantees come at a price in performance, compared to untrusted alternatives. This paper initiates the study of high performance execution of Deep Neural Networks (DNNs) in TEEs by efficiently partitioning DNN computations between trusted and untrusted devices. Building upon an efficient outsourcing scheme for matrix multiplication, we propose Slalom, a framework that securely delegates execution of all linear layers in a DNN from a TEE (e.g., Intel SGX or Sanctum) to a faster, yet untrusted, co-located processor. We evaluate Slalom by running DNNs in an Intel SGX enclave, which selectively delegates work to an untrusted GPU. For canonical DNNs (VGG16, MobileNet and ResNet variants) we obtain 6x to 20x increases in throughput for verifiable inference, and 4x to 11x for verifiable and private inference.", "output": [ "We accelerate secure DNN inference in trusted execution environments (by a factor 4x-20x) by selectively outsourcing the computation of linear layers to a faster yet untrusted co-processor." ] }, { "id": "task668-24ba1101809840a3b7d9c9afcd76eb44", "input": "While Generative Adversarial Networks (GANs) have empirically produced impressive results on learning complex real-world distributions, recent works have shown that they suffer from lack of diversity or mode collapse. The theoretical work of Arora et al. (2017a) suggests a dilemma about GANs’ statistical properties: powerful discriminators cause overfitting, whereas weak discriminators cannot detect mode collapse.\n By contrast, we show in this paper that GANs can in principle learn distributions in Wasserstein distance (or KL-divergence in many cases) with polynomial sample complexity, if the discriminator class has strong distinguishing power against the particular generator class (instead of against all possible generators). For various generator classes such as mixture of Gaussians, exponential families, and invertible and injective neural networks generators, we design corresponding discriminators (which are often neural nets of specific architectures) such that the Integral Probability Metric (IPM) induced by the discriminators can provably approximate the Wasserstein distance and/or KL-divergence. This implies that if the training is successful, then the learned distribution is close to the true distribution in Wasserstein distance or KL divergence, and thus cannot drop modes. Our preliminary experiments show that on synthetic datasets the test IPM is well correlated with KL divergence or the Wasserstein distance, indicating that the lack of diversity in GANs may be caused by the sub-optimality in optimization instead of statistical inefficiency.", "output": [ "GANs can in principle learn distributions sample-efficiently, if the discriminator class is compact and has strong distinguishing power against the particular generator class.", "Proposes the notion of restricted approximability, and provides a sample complexity bound, polynomial in the dimension, which is useful in investigating lack of diversity in GANs.", "Analyzes that the Integral Probability Metric can be a good approximation of Wasserstein distance under some mild assumptions." ] }, { "id": "task668-6304783d4940456486019296b5eddefb", "input": "Topic modeling of text documents is one of the most important tasks in representation learning. In this work, we propose iTM-VAE, which is a Bayesian nonparametric (BNP) topic model with variational auto-encoders. On one hand, as a BNP topic model, iTM-VAE potentially has infinite topics and can adapt the topic number to data automatically. On the other hand, different with the other BNP topic models, the inference of iTM-VAE is modeled by neural networks, which has rich representation capacity and can be computed in a simple feed-forward manner. Two variants of iTM-VAE are also proposed in this paper, where iTM-VAE-Prod models the generative process in products-of-experts fashion for better performance and iTM-VAE-G places a prior over the concentration parameter such that the model can adapt a suitable concentration parameter to data automatically. Experimental results on 20News and Reuters RCV1-V2 datasets show that the proposed models outperform the state-of-the-arts in terms of perplexity, topic coherence and document retrieval tasks. Moreover, the ability of adjusting the concentration parameter to data is also confirmed by experiments.", "output": [ "A Bayesian Nonparametric Topic Model with Variational Auto-Encoders which achieves the state-of-the-arts on public benchmarks in terms of perplexity, topic coherence and retrieval tasks.", "This paper constructs an infinite Topic Model with Variational Auto-Encoders by combining Nalisnick & Smith's stick-breaking variational auto-encoder with latent Dirichlet allocation and several inference techniques used in Miao." ] }, { "id": "task668-fb328dcae0d144b986c52b8dda28a74b", "input": "Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are also easy to interpret. We propose a variational message-passing algorithm for variational inference in such models. We make three contributions. First, we propose structured inference networks that incorporate the structure of the graphical model in the inference network of variational auto-encoders (VAE). Second, we establish conditions under which such inference networks enable fast amortized inference similar to VAE. Finally, we derive a variational message passing algorithm to perform efficient natural-gradient inference while retaining the efficiency of the amortized inference. By simultaneously enabling structured, amortized, and natural-gradient inference for deep structured models, our method simplifies and generalizes existing methods.", "output": [ "We propose a variational message-passing algorithm for models that contain both the deep model and probabilistic graphical model." ] }, { "id": "task668-264cddab94a0420091fdcd3aa6d38b8c", "input": "The huge size of deep networks hinders their use in small computing devices. In this paper, we consider compressing the network by weight quantization. We extend a recently proposed loss-aware weight binarization scheme to ternarization, with possibly different scaling parameters for the positive and negative weights, and m-bit (where m > 2) quantization. Experiments on feedforward and recurrent neural networks show that the proposed scheme outperforms state-of-the-art weight quantization algorithms, and is as accurate (or even more accurate) than the full-precision network.", "output": [ "A loss-aware weight quantization algorithm that directly considers its effect on the loss is proposed.", "Proposes a method of compressing network by means of weight ternarization. ", "The paper proposes a new method to train DNNs with quantized weights, by including the quantization as a constraint in a proximal quasi-Newton algorithm, which simultaneously learns a scaling for the quantized values.", "The paper extends the loss-aware weight binarization scheme to terarization and arbitrary m-bit quantization and demonstrate its promising performance." ] }, { "id": "task668-8e668b04fa8243e6bc06c1e20c74d706", "input": "A patient’s health information is generally fragmented across silos. Though it is technically feasible to unite data for analysis in a manner that underpins a rapid learning healthcare system, privacy concerns and regulatory barriers limit data centralization. Machine learning can be conducted in a federated manner on patient datasets with the same set of variables, but separated across sites of care. But federated learning cannot handle the situation where different data types for a given\n patient are separated vertically across different organizations. We call methods that enable machine learning model training on data separated by two or more degrees “confederated machine learning.” We built and evaluated a confederated machine\nlearning model to stratify the risk of accidental falls among the elderly.", "output": [ "a confederated learning method that train model from horizontally and vertically separated medical data ", "A \"confederated\" machine learning method that learns across divides in medical data separated both horizontally and vertically." ] }, { "id": "task668-8d8d8d6a274f4069936ab40ffec35c14", "input": "In this work, we first conduct mathematical analysis on the memory, which is\n defined as a function that maps an element in a sequence to the current output,\n of three RNN cells; namely, the simple recurrent neural network (SRN), the long\n short-term memory (LSTM) and the gated recurrent unit (GRU). Based on the\n analysis, we propose a new design, called the extended-long short-term memory\n (ELSTM), to extend the memory length of a cell. Next, we present a multi-task\n RNN model that is robust to previous erroneous predictions, called the dependent\n bidirectional recurrent neural network (DBRNN), for the sequence-in-sequenceout\n (SISO) problem. Finally, the performance of the DBRNN model with the\n ELSTM cell is demonstrated by experimental results.", "output": [ "A recurrent neural network cell with extended-long short-term memory and a multi-task RNN model for sequence-in-sequence-out problems" ] }, { "id": "task668-4f5f7ca3bf934ca69a48bfece12e8b4d", "input": "Inspired by neurophysiological discoveries of navigation cells in the mammalian\n brain, we introduce the first deep neural network architecture for modeling Egocentric\n Spatial Memory (ESM). It learns to estimate the pose of the agent and\n progressively construct top-down 2D global maps from egocentric views in a spatially\n extended environment. During the exploration, our proposed ESM network\n model updates belief of the global map based on local observations using a recurrent\n neural network. It also augments the local mapping with a novel external\n memory to encode and store latent representations of the visited places based on\n their corresponding locations in the egocentric coordinate. This enables the agents\n to perform loop closure and mapping correction. This work contributes in the\n following aspects: first, our proposed ESM network provides an accurate mapping\n ability which is vitally important for embodied agents to navigate to goal locations.\n In the experiments, we demonstrate the functionalities of the ESM network in\n random walks in complicated 3D mazes by comparing with several competitive\n baselines and state-of-the-art Simultaneous Localization and Mapping (SLAM)\n algorithms. Secondly, we faithfully hypothesize the functionality and the working\n mechanism of navigation cells in the brain. Comprehensive analysis of our model\n suggests the essential role of individual modules in our proposed architecture and\n demonstrates efficiency of communications among these modules. We hope this\n work would advance research in the collaboration and communications over both\n fields of computer science and computational neuroscience.", "output": [ "first deep neural network for modeling Egocentric Spatial Memory inspired by neurophysiological discoveries of navigation cells in mammalian brain" ] }, { "id": "task668-7da1c6d7deb749239e84d5bc934750b5", "input": "We seek to auto-generate stronger input features for ML methods faced with limited training data.\n Biological neural nets (BNNs) excel at fast learning, implying that they extract highly informative features. In particular, the insect olfactory network learns new odors very rapidly, by means of three key elements: A competitive inhibition layer; randomized, sparse connectivity into a high-dimensional sparse plastic layer; and Hebbian updates of synaptic weights. In this work we deploy MothNet, a computational model of the moth olfactory network, as an automatic feature generator. Attached as a front-end pre-processor, MothNet's readout neurons provide new features, derived from the original features, for use by standard ML classifiers. These ``insect cyborgs'' (part BNN and part ML method) have significantly better performance than baseline ML methods alone on vectorized MNIST and Omniglot data sets, reducing test set error averages 20% to 55%. The MothNet feature generator also substantially out-performs other feature generating methods including PCA, PLS, and NNs. These results highlight the potential value of BNN-inspired feature generators in the ML context.", "output": [ "Features auto-generated by the bio-mimetic MothNet model significantly improve the test accuracy of standard ML methods on vectorized MNIST. The MothNet-generated features also outperform standard feature generators." ] }, { "id": "task668-a842e26f9e124385b73424cbbbae43f9", "input": "Multi-agent cooperation is an important feature of the natural world. Many tasks involve individual incentives that are misaligned with the common good, yet a wide range of organisms from bacteria to insects and humans are able to overcome their differences and collaborate. Therefore, the emergence of cooperative behavior amongst self-interested individuals is an important question for the fields of multi-agent reinforcement learning (MARL) and evolutionary theory. Here, we study a particular class of multi-agent problems called intertemporal social dilemmas (ISDs), where the conflict between the individual and the group is particularly sharp. By combining MARL with appropriately structured natural selection, we demonstrate that individual inductive biases for cooperation can be learned in a model-free way. To achieve this, we introduce an innovative modular architecture for deep reinforcement learning agents which supports multi-level selection. We present results in two challenging environments, and interpret these in the context of cultural and ecological evolution.", "output": [ "We introduce a biologically-inspired modular evolutionary algorithm in which deep RL agents learn to cooperate in a difficult multi-agent social game, which could help to explain the evolution of altruism." ] }, { "id": "task668-d73bf914566d48eda72cd5fe6d1b3403", "input": "Typical amortized inference in variational autoencoders is specialized for a single probabilistic query. Here we propose an inference network architecture that generalizes to unseen probabilistic queries. Instead of an encoder-decoder pair, we can train a single inference network directly from data, using a cost function that is stochastic not only over samples, but also over queries. We can use this network to perform the same inference tasks as we would in an undirected graphical model with hidden variables, without having to deal with the intractable partition function. The results can be mapped to the learning of an actual undirected model, which is a notoriously hard problem. Our network also marginalizes nuisance variables as required. We show that our approach generalizes to unseen probabilistic queries on also unseen test data, providing fast and flexible inference. Experiments show that this approach outperforms or matches PCD and AdVIL on 9 benchmark datasets.", "output": [ "Instead of learning the parameters of a graphical model from data, learn an inference network that can answer the same probabilistic queries." ] }, { "id": "task668-6d91430ae75e469685c8f3f5c3ffe50e", "input": "Detecting the emergence of abrupt property changes in time series is a challenging problem. Kernel two-sample test has been studied for this task which makes fewer assumptions on the distributions than traditional parametric approaches. However, selecting kernels is non-trivial in practice. Although kernel selection for the two-sample test has been studied, the insufficient samples in change point detection problem hinder the success of those developed kernel selection algorithms. In this paper, we propose KL-CPD, a novel kernel learning framework for time series CPD that optimizes a lower bound of test power via an auxiliary generative model. With deep kernel parameterization, KL-CPD endows kernel two-sample test with the data-driven kernel to detect different types of change-points in real-world applications. The proposed approach significantly outperformed other state-of-the-art methods in our comparative evaluation of benchmark datasets and simulation studies.", "output": [ "In this paper, we propose KL-CPD, a novel kernel learning framework for time series CPD that optimizes a lower bound of test power via an auxiliary generative model as a surrogate to the abnormal distribution. ", "Describes a novel approach to optimising the choice of kernel towards increased testing power and shown to offer improvements over alternatives." ] }, { "id": "task668-71013957c4b34be99e17fcfe3c14b88c", "input": "Graph-structured data such as social networks, functional brain networks, gene regulatory networks, communications networks have brought the interest in generalizing deep learning techniques to graph domains. In this paper, we are interested to design neural networks for graphs with variable length in order to solve learning problems such as vertex classification, graph classification, graph regression, and graph generative tasks. Most existing works have focused on recurrent neural networks (RNNs) to learn meaningful representations of graphs, and more recently new convolutional neural networks (ConvNets) have been introduced. In this work, we want to compare rigorously these two fundamental families of architectures to solve graph learning tasks. We review existing graph RNN and ConvNet architectures, and propose natural extension of LSTM and ConvNet to graphs with arbitrary size. Then, we design a set of analytically controlled experiments on two basic graph problems, i.e. subgraph matching and graph clustering, to test the different architectures. Numerical results show that the proposed graph ConvNets are 3-17% more accurate and 1.5-4x faster than graph RNNs. Graph ConvNets are also 36% more accurate than variational (non-learning) techniques. Finally, the most effective graph ConvNet architecture uses gated edges and residuality. Residuality plays an essential role to learn multi-layer architectures as they provide a 10% gain of performance.", "output": [ "We compare graph RNNs and graph ConvNets, and we consider the most generic class of graph ConvNets with residuality." ] }, { "id": "task668-e247a90473da47eaaf8119ea6c265be6", "input": "In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum. In contrast to these settings, success in the real world commonly requires humans to collaborate and communicate with others, in settings that are, at least partially, cooperative. In the last year, the card game Hanabi has been established as a new benchmark environment for AI to fill this gap. In particular, Hanabi is interesting to humans since it is entirely focused on theory of mind, i.e. the ability to effectively reason over the intentions, beliefs and point of view of other agents when observing their actions. Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, RL requires agents to explore in order to discover good policies. However, when done naively, this randomness will inherently make their actions less informative to others during training. We present a new deep multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this contradiction exploiting the centralized training phase. During training SAD allows other agents to not only observe the (exploratory) action chosen, but agents instead also observe the greedy action of their team mates. By combining this simple intuition with an auxiliary task for state prediction and best practices for multi-agent learning, SAD establishes a new state of the art for 2-5 players on the self-play part of the Hanabi challenge.", "output": [ "We develop Simplified Action Decoder, a simple MARL algorithm that beats previous SOTA on Hanabi by a big margin across 2- to 5-player games." ] }, { "id": "task668-86e018bc64854100a4947f55015a3d49", "input": "We propose the Fixed Grouping Layer (FGL); a novel feedforward layer designed to incorporate the inductive bias of structured smoothness into a deep learning model. FGL achieves this goal by connecting nodes across layers based on spatial similarity. The use of structured smoothness, as implemented by FGL, is motivated by applications to structured spatial data, which is, in turn, motivated by domain knowledge. The proposed model architecture outperforms conventional neural network architectures across a variety of simulated and real datasets with structured smoothness.", "output": [ "A feedforward layer to incorporate structured smoothness into a deep learning model" ] }, { "id": "task668-4b8479f0bfa64b879b18a1d04cd0fba6", "input": "Anatomical studies demonstrate that brain reformats input information to generate reliable responses for performing computations. However, it remains unclear how neural circuits encode complex spatio-temporal patterns. We show that neural dynamics are strongly influenced by the phase alignment between the input and the spontaneous chaotic activity. Input alignment along the dominant chaotic projections causes the chaotic trajectories to become stable channels (or attractors), hence, improving the computational capability of a recurrent network. Using mean field analysis, we derive the impact of input alignment on the overall stability of attractors formed. Our results indicate that input alignment determines the extent of intrinsic noise suppression and hence, alters the attractor state stability, thereby controlling the network's inference ability.", "output": [ "Input Structuring along Chaos for Stability" ] }, { "id": "task668-a7e37c4d0a1941a285684dd4adac6b39", "input": "Natural Language Processing models lack a unified approach to robustness testing. In this paper we introduce WildNLP - a framework for testing model stability in a natural setting where text corruptions such as keyboard errors or misspelling occur. We compare robustness of models from 4 popular NLP tasks: Q&A, NLI, NER and Sentiment Analysis by testing their performance on aspects introduced in the framework. In particular, we focus on a comparison between recent state-of-the- art text representations and non-contextualized word embeddings. In order to improve robust- ness, we perform adversarial training on se- lected aspects and check its transferability to the improvement of models with various cor- ruption types. We find that the high perfor- mance of models does not ensure sufficient robustness, although modern embedding tech- niques help to improve it. We release cor- rupted datasets and code for WildNLP frame- work for the community.", "output": [ "We compare robustness of models from 4 popular NLP tasks: Q&A, NLI, NER and Sentiment Analysis by testing their performance on perturbed inputs." ] }, { "id": "task668-d6308c23ffbe4d7d902fcd790464b1ef", "input": "Deep reinforcement learning (DRL) algorithms have demonstrated progress in learning to find a goal in challenging environments. As the title of the paper by Mirowski et al. (2016) suggests, one might assume that DRL-based algorithms are able to “learn to navigate” and are thus ready to replace classical mapping and path-planning algorithms, at least in simulated environments. Yet, from experiments and analysis in this earlier work, it is not clear what strategies are used by these algorithms in navigating the mazes and finding the goal. In this paper, we pose and study this underlying question: are DRL algorithms doing some form of mapping and/or path-planning? Our experiments show that the algorithms are not memorizing the maps of mazes at the testing stage but, rather, at the training stage. Hence, the DRL algorithms fall short of qualifying as mapping or path-planning algorithms with any reasonable definition of mapping. We extend the experiments in Mirowski et al. (2016) by separating the set of training and testing maps and by a more ablative coverage of the space of experiments. Our systematic experiments show that the NavA3C-D1-D2-L algorithm, when trained and tested on the same maps, is able to choose the shorter paths to the goal. However, when tested on unseen maps the algorithm utilizes a wall-following strategy to find the goal without doing any mapping or path planning.", "output": [ "We quantitatively and qualitatively evaluate deep reinforcement learning based navigation methods under a variety of conditions to answer the question of how close they are to replacing classical path planners and mapping algorithms.", "Evaluate a Deep RL-based model on training mazes by measuring repeated latency to goal and comparison to shortest route" ] }, { "id": "task668-43d046d9ddce4d27a847d45ca3971498", "input": "We present a new latent model of natural images that can be learned on large-scale datasets. The learning process provides a latent embedding for every image in the training dataset, as well as a deep convolutional network that maps the latent space to the image space. After training, the new model provides a strong and universal image prior for a variety of image restoration tasks such as large-hole inpainting, superresolution, and colorization. To model high-resolution natural images, our approach uses latent spaces of very high dimensionality (one to two orders of magnitude higher than previous latent image models). To tackle this high dimensionality, we use latent spaces with a special manifold structure (convolutional manifolds) parameterized by a ConvNet of a certain architecture. In the experiments, we compare the learned latent models with latent models learned by autoencoders, advanced variants of generative adversarial networks, and a strong baseline system using simpler parameterization of the latent space. Our model outperforms the competing approaches over a range of restoration tasks.", "output": [ "We present a new deep latent model of natural images that can be trained from unlabeled datasets and can be utilized to solve various image restoration tasks." ] }, { "id": "task668-dffa0c1260dc4237890d45c12e819ccf", "input": "Due to the success of deep learning to solving a variety of challenging machine learning tasks, there is a rising interest in understanding loss functions for training neural networks from a theoretical aspect. Particularly, the properties of critical points and the landscape around them are of importance to determine the convergence performance of optimization algorithms. In this paper, we provide a necessary and sufficient characterization of the analytical forms for the critical points (as well as global minimizers) of the square loss functions for linear neural networks. We show that the analytical forms of the critical points characterize the values of the corresponding loss functions as well as the necessary and sufficient conditions to achieve global minimum. Furthermore, we exploit the analytical forms of the critical points to characterize the landscape properties for the loss functions of linear neural networks and shallow ReLU networks. One particular conclusion is that: While the loss function of linear networks has no spurious local minimum, the loss function of one-hidden-layer nonlinear networks with ReLU activation function does have local minimum that is not global minimum.", "output": [ "We provide necessary and sufficient analytical forms for the critical points of the square loss functions for various neural networks, and exploit the analytical forms to characterize the landscape properties for the loss functions of these neural networks." ] }, { "id": "task668-0fe9595a3bc94bf7a043c8f8e174841e", "input": "Conventionally, convolutional neural networks (CNNs) process different images with the same set of filters. However, the variations in images pose a challenge to this fashion. In this paper, we propose to generate sample-specific filters for convolutional layers in the forward pass. Since the filters are generated on-the-fly, the model becomes more flexible and can better fit the training data compared to traditional CNNs. In order to obtain sample-specific features, we extract the intermediate feature maps from an autoencoder. As filters are usually high dimensional, we propose to learn a set of coefficients instead of a set of filters. These coefficients are used to linearly combine the base filters from a filter repository to generate the final filters for a CNN. The proposed method is evaluated on MNIST, MTFL and CIFAR10 datasets. Experiment results demonstrate that the classification accuracy of the baseline model can be improved by using the proposed filter generation method.", "output": [ "dynamically generate filters conditioned on the input image for CNNs in each forward pass " ] }, { "id": "task668-29f0920ab8eb43b99b3dfe9cad589ae8", "input": "Most existing defenses against adversarial attacks only consider robustness to L_p-bounded distortions. In reality, the specific attack is rarely known in advance and adversaries are free to modify images in ways which lie outside any fixed distortion model; for example, adversarial rotations lie outside the set of L_p-bounded distortions. In this work, we advocate measuring robustness against a much broader range of unforeseen attacks, attacks whose precise form is unknown during defense design.\n\n We propose several new attacks and a methodology for evaluating a defense against a diverse range of unforeseen distortions. First, we construct novel adversarial JPEG, Fog, Gabor, and Snow distortions to simulate more diverse adversaries. We then introduce UAR, a summary metric that measures the robustness of a defense against a given distortion. Using UAR to assess robustness against existing and novel attacks, we perform an extensive study of adversarial robustness. We find that evaluation against existing L_p attacks yields redundant information which does not generalize to other attacks; we instead recommend evaluating against our significantly more diverse set of attacks. We further find that adversarial training against either one or multiple distortions fails to confer robustness to attacks with other distortion types. These results underscore the need to evaluate and study robustness against unforeseen distortions.", "output": [ "We propose several new attacks and a methodology to measure robustness against unforeseen adversarial attacks." ] }, { "id": "task668-a6c7654441b54779a78ab5735a87dc19", "input": "Brain-Computer Interfaces (BCI) may help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech by direct neural processing. However, their practical realization has proven difficult due to limitations in speed, accuracy, and generalizability of existing interfaces. To this end, we aim to create a BCI that decodes text directly from neural signals. We implement a framework that initially isolates frequency bands in the input signal encapsulating differential information regarding production of various phonemic classes. These bands form a feature set that feeds into an LSTM which discerns at each time point probability distributions across all phonemes uttered by a subject. Finally, a particle filtering algorithm temporally smooths these probabilities incorporating prior knowledge of the English language to output text corresponding to the decoded word. Further, in producing an output, we abstain from constraining the reconstructed word to be from a given bag-of-words, unlike previous studies. The empirical success of our proposed approach, offers promise for the employment of such an interface by patients in unfettered, naturalistic environments.", "output": [ "We present an open-loop brain-machine interface whose performance is unconstrained to the traditionally used bag-of-words approach." ] }, { "id": "task668-07d844595289491eb3b9e6053cf0f691", "input": "Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues, learn morphology, and move us closer to completely end-to-end translation systems. Unfortunately, they are also very brittle and easily falter when presented with noisy data. In this paper, we confront NMT models with synthetic and natural sources of noise. We find that state-of-the-art models fail to translate even moderately noisy texts that humans have no trouble comprehending. We explore two approaches to increase model robustness: structure-invariant word representations and robust training on noisy texts. We find that a model based on a character convolutional neural network is able to simultaneously learn representations robust to multiple kinds of noise.", "output": [ "CharNMT is brittle", "This paper investigates the impact of character-level noise on 4 different neural machine translation systems", "This paper empirically investigates the performance of character-level NMT systems in the face of character-level noise, both synthesized and natural.", "This paper investigates the impact of noisy input on Machine Translation and tests ways to make NMT models more robust" ] }, { "id": "task668-fe64800c30ab4ae2b48eb3a1fc444322", "input": "The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks. However, many machine learning tasks have inputs naturally represented as graphs; existing Seq2Seq models face a significant challenge in achieving accurate conversion from graph form to the appropriate sequence. To address this challenge, we introduce a general end-to-end graph-to-sequence neural encoder-decoder architecture that maps an input graph to a sequence of vectors and uses an attention-based LSTM method to decode the target sequence from these vectors. Our method first generates the node and graph embeddings using an improved graph-based neural network with a novel aggregation strategy to incorporate edge direction information in the node embeddings. We further introduce an attention mechanism that aligns node embeddings and the decoding sequence to better cope with large graphs. Experimental results on bAbI, Shortest Path, and Natural Language Generation tasks demonstrate that our model achieves state-of-the-art performance and significantly outperforms existing graph neural networks, Seq2Seq, and Tree2Seq models; using the proposed bi-directional node embedding aggregation strategy, the model can converge rapidly to the optimal performance.", "output": [ "Graph to Sequence Learning with Attention-Based Neural Networks", "A graph2seq architecture that combines a graph encoder mixing GGNN and GCN components with an attentional sequence encoder, and that shows improvements over baselines.", "This work proposes an end-to-end graph encoder to sequence decoder models with an attention mechanism in between." ] }, { "id": "task668-7855432465a4430ea560d413f6351cc6", "input": "We build a theoretical framework for understanding practical meta-learning methods that enables the integration of sophisticated formalizations of task-similarity with the extensive literature on online convex optimization and sequential prediction algorithms in order to provide within-task performance guarantees. Our approach improves upon recent analyses of parameter-transfer by enabling the task-similarity to be learned adaptively and by improving transfer-risk bounds in the setting of statistical learning-to-learn. It also leads to straightforward derivations of average-case regret bounds for efficient algorithms in settings where the task-environment changes dynamically or the tasks share a certain geometric structure.", "output": [ "Practical adaptive algorithms for gradient-based meta-learning with provable guarantees." ] }, { "id": "task668-65f332f84e404640927cd3ca68f5aa2c", "input": "Learning to imitate expert behavior from demonstrations can be challenging, especially in environments with high-dimensional, continuous observations and unknown dynamics. Supervised learning methods based on behavioral cloning (BC) suffer from distribution shift: because the agent greedily imitates demonstrated actions, it can drift away from demonstrated states due to error accumulation. Recent methods based on reinforcement learning (RL), such as inverse RL and generative adversarial imitation learning (GAIL), overcome this issue by training an RL agent to match the demonstrations over a long horizon. Since the true reward function for the task is unknown, these methods learn a reward function from the demonstrations, often using complex and brittle approximation techniques that involve adversarial training. We propose a simple alternative that still uses RL, but does not require learning a reward function. The key idea is to provide the agent with an incentive to match the demonstrations over a long horizon, by encouraging it to return to demonstrated states upon encountering new, out-of-distribution states. We accomplish this by giving the agent a constant reward of r=+1 for matching the demonstrated action in a demonstrated state, and a constant reward of r=0 for all other behavior. Our method, which we call soft Q imitation learning (SQIL), can be implemented with a handful of minor modifications to any standard Q-learning or off-policy actor-critic algorithm. Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation. Empirically, we show that SQIL outperforms BC and achieves competitive results compared to GAIL, on a variety of image-based and low-dimensional tasks in Box2D, Atari, and MuJoCo. This paper is a proof of concept that illustrates how a simple imitation method based on RL with constant rewards can be as effective as more complex methods that use learned rewards.", "output": [ "A simple and effective alternative to adversarial imitation learning: initialize experience replay buffer with demonstrations, set their reward to +1, set reward for all other data to 0, run Q-learning or soft actor-critic to train." ] }, { "id": "task668-2de4e823bcbf479ab65c6def93e04b99", "input": "Object recognition in real-world requires handling long-tailed or even open-ended data. An ideal visual system needs to reliably recognize the populated visual concepts and meanwhile efficiently learn about emerging new categories with a few training instances. Class-balanced many-shot learning and few-shot learning tackle one side of this problem, via either learning strong classifiers for populated categories or learning to learn few-shot classifiers for the tail classes. In this paper, we investigate the problem of generalized few-shot learning (GFSL) -- a model during the deployment is required to not only learn about \"tail\" categories with few shots, but simultaneously classify the \"head\" and \"tail\" categories. We propose the Classifier Synthesis Learning (CASTLE), a learning framework that learns how to synthesize calibrated few-shot classifiers in addition to the multi-class classifiers of ``head'' classes, leveraging a shared neural dictionary. CASTLE sheds light upon the inductive GFSL through optimizing one clean and effective GFSL learning objective. It demonstrates superior performances than existing GFSL algorithms and strong baselines on MiniImageNet and TieredImageNet data sets. More interestingly, it outperforms previous state-of-the-art methods when evaluated on standard few-shot learning.", "output": [ "We propose to learn synthesizing few-shot classifiers and many-shot classifiers using one single objective function for GFSL." ] }, { "id": "task668-f99d82dc47444fc9913d4a60559cdfbb", "input": "We present a weakly-supervised data augmentation approach to improve Named Entity Recognition (NER) in a challenging domain: extracting biomedical entities (e.g., proteins) from the scientific literature. First, we train a neural NER (NNER) model over a small seed of fully-labeled examples. Second, we use a reference set of entity names (e.g., proteins in UniProt) to identify entity mentions with high precision, but low recall, on an unlabeled corpus. Third, we use the NNER model to assign weak labels to the corpus. Finally, we retrain our NNER model iteratively over the augmented training set, including the seed, the reference-set examples, and the weakly-labeled examples, which results in refined labels. We show empirically that this augmented bootstrapping process significantly improves NER performance, and discuss the factors impacting the efficacy of the approach.", "output": [ "Augmented bootstrapping approach combining information from a reference set with iterative refinements of soft labels to improve Name Entity Recognition from biomedical literature." ] }, { "id": "task668-9d820377b2d844a88e4f37ed889231ff", "input": "Retinal prostheses for treating incurable blindness are designed to electrically stimulate surviving retinal neurons, causing them to send artificial visual signals to the brain. However, electrical stimulation generally cannot precisely reproduce normal patterns of neural activity in the retina. Therefore, an electrical stimulus must be selected that produces a neural response as close as possible to the desired response. This requires a technique for computing a distance between the desired response and the achievable response that is meaningful in terms of the visual signal being conveyed. Here we propose a method to learn such a metric on neural responses, directly from recorded light responses of a population of retinal ganglion cells (RGCs) in the primate retina. The learned metric produces a measure of similarity of RGC population responses that accurately reflects the similarity of the visual input. Using data from electrical stimulation experiments, we demonstrate that this metric may improve the performance of a prosthesis.", "output": [ "Using triplets to learn a metric for comparing neural responses and improve the performance of a prosthesis.", "Authors develop new spike train distance metrics, including neural networks and quadratic metrics. These metrics are shown to outperform the naive Hamming distance metric, and implicitly captures some structure in neural code.", "With the application of improving neural prosthesis in mind, the authors propose to learn a metric between neural responses by either optimizing a quadratic form or a deep neural network ." ] }, { "id": "task668-9f689c4f182e4f7f84f57a673ff8731b", "input": "We present a general-purpose method to train Markov chain Monte Carlo kernels, parameterized by deep neural networks, that converge and mix quickly to their target distribution. Our method generalizes Hamiltonian Monte Carlo and is trained to maximize expected squared jumped distance, a proxy for mixing speed. We demonstrate large empirical gains on a collection of simple but challenging distributions, for instance achieving a 106x improvement in effective sample size in one case, and mixing when standard HMC makes no measurable progress in a second. Finally, we show quantitative and qualitative gains on a real-world task: latent-variable generative modeling. Python source code will be open-sourced with the camera-ready paper.", "output": [ "General method to train expressive MCMC kernels parameterized with deep neural networks. Given a target distribution p, our method provides a fast-mixing sampler, able to efficiently explore the state space.", "Proposes a generalized HMC by modifying the leapfrog integrator using neural networks to make the sampler to converge and mix quickly. " ] }, { "id": "task668-23ae7a2d32094aa5a116a0ed3bf48b2b", "input": "Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e.g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models. Since collecting new training data could be costly, we focus on better utilizing the given data by inducing the regions with high sample density in the feature space, which could lead to locally sufficient samples for robust learning. We first formally show that the softmax cross-entropy (SCE) loss and its variants convey inappropriate supervisory signals, which encourage the learned feature points to spread over the space sparsely in training. This inspires us to propose the Max-Mahalanobis center (MMC) loss to explicitly induce dense feature regions in order to benefit robustness. Namely, the MMC loss encourages the model to concentrate on learning ordered and compact representations, which gather around the preset optimal centers for different classes. We empirically demonstrate that applying the MMC loss can significantly improve robustness even under strong adaptive attacks, while keeping state-of-the-art accuracy on clean inputs with little extra computation compared to the SCE loss.", "output": [ "Applying the softmax function in training leads to indirect and unexpected supervision on features. We propose a new training objective to explicitly induce dense feature regions for locally sufficient samples to benefit adversarial robustness." ] }, { "id": "task668-e8806b2113d440358a50478f0c6c4ac3", "input": "We study the following three fundamental problems about ridge regression: (1) what is the structure of the estimator? (2) how to correctly use cross-validation to choose the regularization parameter? and (3) how to accelerate computation without losing too much accuracy? We consider the three problems in a unified large-data linear model. We give a precise representation of ridge regression as a covariance matrix-dependent linear combination of the true parameter and the noise. \n We study the bias of $K$-fold cross-validation for choosing the regularization parameter, and propose a simple bias-correction. We analyze the accuracy of primal and dual sketching for ridge regression, showing they are surprisingly accurate. Our results are illustrated by simulations and by analyzing empirical data.", "output": [ "We study the structure of ridge regression in a high-dimensional asymptotic framework, and get insights about cross-validation and sketching.", "A theoretical study of ridge regression by exploiting a new asymptotic characterisation of the ridge regression estimator." ] }, { "id": "task668-5e63eeb2aed042cf9bee353c6fdc0396", "input": "Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning. A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but on the agent's uncertainty about the environment. Computing a Bayes-optimal policy is however intractable for all but the smallest tasks. In this paper, we introduce variational Bayes-Adaptive Deep RL (variBAD), a way to meta-learn to perform approximate inference in an unknown environment, and incorporate task uncertainty directly during action selection. In a grid-world domain, we illustrate how variBAD performs structured online exploration as a function of task uncertainty. We also evaluate variBAD on MuJoCo domains widely used in meta-RL and show that it achieves higher return during training than existing methods.", "output": [ "VariBAD opens a path to tractable approximate Bayes-optimal exploration for deep RL using ideas from meta-learning, Bayesian RL, and approximate variational inference.", "This paper presents a new deep reinforcement learning method that can efficiently trade-off exploration and exploitation that combines meta-learning, variational inference, and bayesian RL." ] }, { "id": "task668-618572a41bd8474ca13320f4ffe66f73", "input": "Recent advances have illustrated that it is often possible to learn to solve linear inverse problems in imaging using training data that can outperform more traditional regularized least squares solutions. Along these lines, we present some extensions of the Neumann network, a recently introduced end-to-end learned architecture inspired by a truncated Neumann series expansion of the solution map to a regularized least squares problem. Here we summarize the Neumann network approach, and show that it has a form compatible with the optimal reconstruction function for a given inverse problem. We also investigate an extension of the Neumann network that incorporates a more sample efficient patch-based regularization approach.", "output": [ "Neumann networks are an end-to-end, sample-efficient learning approach to solving linear inverse problems in imaging that are compatible with the MSE optimal approach and admit an extension to patch-based learning." ] }, { "id": "task668-658e136f54d445ef913c26a8d8256cdf", "input": "Partially observable Markov decision processes (POMDPs) are a natural model for scenarios where one has to deal with incomplete knowledge and random events.\n Applications include, but are not limited to, robotics and motion planning.\n However, many relevant properties of POMDPs are either undecidable or very expensive to compute in terms of both runtime and memory consumption.\n In our work, we develop a game-based abstraction method that is able to deliver safe bounds and tight\n approximations for important sub-classes of such properties.\n We discuss the theoretical implications and showcase the applicability of our results on a broad spectrum of benchmarks.\n", "output": [ "This paper provides a game-based abstraction scheme to compute provably sound policies for POMDPs." ] }, { "id": "task668-4fbeb71767674aaf844e12ea99fd8aa9", "input": "Though deep neural networks have achieved the state of the art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversarial examples. To solve the problem, some regularization adversarial training methods, constraining the output label or logit, have been studied. In this paper, we propose a novel regularized adversarial training framework ATLPA,namely Adversarial Tolerant Logit Pairing with Attention. Instead of constraining a hard distribution (e.g., one-hot vectors or logit) in adversarial training, ATLPA uses Tolerant Logit which consists of confidence distribution on top-k classes and captures inter-class similarities at the image level. Specifically, in addition to minimizing the empirical loss, ATLPA encourages attention map for pairs of examples to be similar. When applied to clean examples and their adversarial counterparts, ATLPA improves accuracy on adversarial examples over adversarial training. We evaluate ATLPA with the state of the art algorithms, the experiment results show that our method outperforms these baselines with higher accuracy. Compared with previous work, our work is evaluated under highly challenging PGD attack: the maximum perturbation $\\epsilon$ is 64 and 128 with 10 to 200 attack iterations.", "output": [ "In this paper, we propose a novel regularized adversarial training framework ATLPA,namely Adversarial Tolerant Logit Pairing with Attention." ] }, { "id": "task668-5a7760d9ac344d84a7d739712adf33e9", "input": "Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence. The absence of any framework to analyze the precision requirements of partial sum accumulations results in conservative design choices. This imposes an upper-bound on the reduction of complexity of multiply-accumulate units. We present a statistical approach to analyze the impact of reduced accumulation precision on deep learning training. Observing that a bad choice for accumulation precision results in loss of information that manifests itself as a reduction in variance in an ensemble of partial sums, we derive a set of equations that relate this variance to the length of accumulation and the minimum number of bits needed for accumulation. We apply our analysis to three benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet AlexNet. In each case, with accumulation precision set in accordance with our proposed equations, the networks successfully converge to the single precision floating-point baseline. We also show that reducing accumulation precision further degrades the quality of the trained network, proving that our equations produce tight bounds. Overall this analysis enables precise tailoring of computation hardware to the application, yielding area- and power-optimal systems.", "output": [ "We present an analytical framework to determine accumulation bit-width requirements in all three deep learning training GEMMs and verify the validity and tightness of our method via benchmarking experiments.", "The authors propose an analytical method to predict the number of mantissa bits needed for partial summations for convolutional and fully connected layers", "The authors conduct a thorough analysis of the numeric precision required for the accumulation operations in neural network training and show the theoretical impact of reducing number of bits in the floating point accumulator." ] }, { "id": "task668-c18aa5f53a0b4d6ba4eb63c351246543", "input": "A key component for many reinforcement learning agents is to learn a value function, either for policy evaluation or control. Many of the algorithms for learning values, however, are designed for linear function approximation---with a fixed basis or fixed representation. Though there have been a few sound extensions to nonlinear function approximation, such as nonlinear gradient temporal difference learning, these methods have largely not been adopted, eschewed in favour of simpler but not sound methods like temporal difference learning and Q-learning. In this work, we provide a two-timescale network (TTN) architecture that enables linear methods to be used to learn values, with a nonlinear representation learned at a slower timescale. The approach facilitates the use of algorithms developed for the linear setting, such as data-efficient least-squares methods, eligibility traces and the myriad of recently developed linear policy evaluation algorithms, to provide nonlinear value estimates. We prove convergence for TTNs, with particular care given to ensure convergence of the fast linear component under potentially dependent features provided by the learned representation. We empirically demonstrate the benefits of TTNs, compared to other nonlinear value function approximation algorithms, both for policy evaluation and control. ", "output": [ "We propose an architecture for learning value functions which allows the use of any linear policy evaluation algorithm in tandem with nonlinear feature learning.", "The paper proposes a two-timescale framework for learning the value function and a state representation altogether with nonlinear approximators.", "This paper proposes Two-Timescale Networks (TTNs) and prove the convergence of this method using methods from two time-scale stochastic approximation. ", "This paper presents a Two-Timescale Network (TTN) that enables linear methods to be used to learn values. " ] }, { "id": "task668-dc8e329f88e844229bf66a7cb1b57d6b", "input": "While adversarial training can improve robust accuracy (against an adversary), it sometimes hurts standard accuracy (when there is no adversary). Previous work has studied this tradeoff between standard and robust accuracy, but only in the setting where no predictor performs well on both objectives in the infinite data limit. In this paper, we show that even when the optimal predictor with infinite data performs well on both objectives, a tradeoff can still manifest itself with finite data. Furthermore, since our construction is based on a convex learning problem, we rule out optimization concerns, thus laying bare a fundamental tension between robustness and generalization. Finally, we show that robust self-training mostly eliminates this tradeoff by leveraging unlabeled data.", "output": [ "Even if there is no tradeoff in the infinite data limit, adversarial training can have worse standard accuracy even in a convex problem." ] }, { "id": "task668-9a923bbb99294e82b6530e3987027df8", "input": "Convolutional neural networks (CNNs) are commonly trained using a fixed spatial image size predetermined for a given model. Although trained on images of a specific size, it is well established that CNNs can be used to evaluate a wide range of image sizes at test time, by adjusting the size of intermediate feature maps. \n In this work, we describe and evaluate a novel mixed-size training regime that mixes several image sizes at training time. We demonstrate that models trained using our method are more resilient to image size changes and generalize well even on small images. This allows faster inference by using smaller images at test time. For instance, we receive a 76.43% top-1 accuracy using ResNet50 with an image size of 160, which matches the accuracy of the baseline model with 2x fewer computations.\n Furthermore, for a given image size used at test time, we show this method can be exploited either to accelerate training or the final test accuracy. For example, we are able to reach a 79.27% accuracy with a model evaluated at a 288 spatial size for a relative improvement of 14% over the baseline.", "output": [ "Training convnets with mixed image size can improve results across multiple sizes at evaluation" ] }, { "id": "task668-cd50356f8fb0475abb66752c04b4d92c", "input": "There have been multiple attempts with variational auto-encoders (VAE) to learn powerful global representations of complex data using a combination of latent stochastic variables and an autoregressive model over the dimensions of the data. However, for the most challenging natural image tasks the purely autoregressive model with stochastic variables still outperform the combined stochastic autoregressive models. In this paper, we present simple additions to the VAE framework that generalize to natural images by embedding spatial information in the stochastic layers. We significantly improve the state-of-the-art results on MNIST, OMNIGLOT, CIFAR10 and ImageNet when the feature map parameterization of the stochastic variables are combined with the autoregressive PixelCNN approach. Interestingly, we also observe close to state-of-the-art results without the autoregressive part. This opens the possibility for high quality image generation with only one forward-pass.\n", "output": [ "We present a generative model that proves state-of-the-art results on gray-scale and natural images." ] }, { "id": "task668-71363659f2ea477ba3c38df219ec9f43", "input": "We present a deep generative model for unsupervised text style transfer that unifies previously proposed non-generative techniques. Our probabilistic approach models non-parallel data from two domains as a partially observed parallel corpus. By hypothesizing a parallel latent sequence that generates each observed sequence, our model learns to transform sequences from one domain to another in a completely unsupervised fashion. In contrast with traditional generative sequence models (e.g. the HMM), our model makes few assumptions about the data it generates: it uses a recurrent language model as a prior and an encoder-decoder as a transduction distribution. While computation of marginal data likelihood is intractable in this model class, we show that amortized variational inference admits a practical surrogate. Further, by drawing connections between our variational objective and other recent unsupervised style transfer and machine translation techniques, we show how our probabilistic view can unify some known non-generative objectives such as backtranslation and adversarial loss. Finally, we demonstrate the effectiveness of our method on a wide range of unsupervised style transfer tasks, including sentiment transfer, formality transfer, word decipherment, author imitation, and related language translation. Across all style transfer tasks, our approach yields substantial gains over state-of-the-art non-generative baselines, including the state-of-the-art unsupervised machine translation techniques that our approach generalizes. Further, we conduct experiments on a standard unsupervised machine translation task and find that our unified approach matches the current state-of-the-art.", "output": [ "We formulate a probabilistic latent sequence model to tackle unsupervised text style transfer, and show its effectiveness across a suite of unsupervised text style transfer tasks. " ] }, { "id": "task668-47488309524442acb7d1491f69307046", "input": "The domain of time-series forecasting has been extensively studied because it is of fundamental importance in many real-life applications. Weather prediction, traffic flow forecasting or sales are compelling examples of sequential phenomena. Predictive models generally make use of the relations between past and future values. However, in the case of stationary time-series, observed values also drastically depend on a number of exogenous features that can be used to improve forecasting quality. In this work, we propose a change of paradigm which consists in learning such features in embeddings vectors within recurrent neural networks. We apply our framework to forecast smart cards tap-in logs in the Parisian subway network. Results show that context-embedded models perform quantitatively better in one-step ahead and multi-step ahead forecasting.", "output": [ "In order to forecast multivariate stationary time-series we learn embeddings containing contextual features within a RNN; we apply the framework on public transportation data" ] }, { "id": "task668-4a8b3aea9662432e8f7743a0cc5cd356", "input": "Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time. Given that it is impractical to train separate policies to accommodate all situations the agent may see in the real world, this work proposes to learn how to quickly and effectively adapt online to new tasks. To enable sample-efficient learning, we consider learning online adaptation in the context of model-based reinforcement learning. Our approach uses meta-learning to train a dynamics model prior such that, when combined with recent data, this prior can be rapidly adapted to the local context. Our experiments demonstrate online adaptation for continuous control tasks on both simulated and real-world agents. We first show simulated agents adapting their behavior online to novel terrains, crippled body parts, and highly-dynamic environments. We also illustrate the importance of incorporating online adaptation into autonomous agents that operate in the real world by applying our method to a real dynamic legged millirobot: We demonstrate the agent's learned ability to quickly adapt online to a missing leg, adjust to novel terrains and slopes, account for miscalibration or errors in pose estimation, and compensate for pulling payloads.", "output": [ "A model-based meta-RL algorithm that enables a real robot to adapt online in dynamic environments" ] }, { "id": "task668-cc83b3cf277c448ebb83551a8074ddfe", "input": "In vanilla backpropagation (VBP), activation function matters considerably in terms of non-linearity and differentiability.\n Vanishing gradient has been an important problem related to the bad choice of activation function in deep learning (DL).\n This work shows that a differentiable activation function is not necessary any more for error backpropagation. \n The derivative of the activation function can be replaced by an iterative temporal differencing (ITD) using fixed random feedback weight alignment (FBA).\n Using FBA with ITD, we can transform the VBP into a more biologically plausible approach for learning deep neural network architectures.\n We don't claim that ITD works completely the same as the spike-time dependent plasticity (STDP) in our brain but this work can be a step toward the integration of STDP-based error backpropagation in deep learning.", "output": [ "Iterative temporal differencing with fixed random feedback alignment support spike-time dependent plasticity in vanilla backpropagation for deep learning." ] }, { "id": "task668-6eb01e7e3c5c40ee909d2c12f76e5d00", "input": "We leverage recent insights from second-order optimisation for neural networks to construct a Kronecker factored Laplace approximation to the posterior over the weights of a trained network. Our approximation requires no modification of the training procedure, enabling practitioners to estimate the uncertainty of their models currently used in production without having to retrain them. We extensively compare our method to using Dropout and a diagonal Laplace approximation for estimating the uncertainty of a network. We demonstrate that our Kronecker factored method leads to better uncertainty estimates on out-of-distribution data and is more robust to simple adversarial attacks. Our approach only requires calculating two square curvature factor matrices for each layer. Their size is equal to the respective square of the input and output size of the layer, making the method efficient both computationally and in terms of memory usage. We illustrate its scalability by applying it to a state-of-the-art convolutional network architecture.", "output": [ "We construct a Kronecker factored Laplace approximation for neural networks that leads to an efficient matrix normal distribution over the weights." ] }, { "id": "task668-f4d9cc40358a4346ac52856d8817db86", "input": "In lifelong learning, the learner is presented with a sequence of tasks, incrementally building a data-driven prior which may be leveraged to speed up learning of a new task. In this work, we investigate the efficiency of current lifelong approaches, in terms of sample complexity, computational and memory cost. Towards this end, we first introduce a new and a more realistic evaluation protocol, whereby learners observe each example only once and hyper-parameter selection is done on a small and disjoint set of tasks, which is not used for the actual learning experience and evaluation. Second, we introduce a new metric measuring how quickly a learner acquires a new skill. Third, we propose an improved version of GEM (Lopez-Paz & Ranzato, 2017), dubbed Averaged GEM (A-GEM), which enjoys the same or even better performance as GEM, while being almost as computationally and memory efficient as EWC (Kirkpatrick et al., 2016) and other regularization-based methods. Finally, we show that all algorithms including A-GEM can learn even more quickly if they are provided with task descriptors specifying the classification tasks under consideration. Our experiments on several standard lifelong learning benchmarks demonstrate that A-GEM has the best trade-off between accuracy and efficiency", "output": [ "An efficient lifelong learning algorithm that provides a better trade-off between accuracy and time/ memory complexity compared to other algorithms. " ] }, { "id": "task668-74ff8646678d4741b81aa5a4ba9d694d", "input": "A growing number of learning methods are actually differentiable games whose players optimise multiple, interdependent objectives in parallel – from GANs and intrinsic curiosity to multi-agent RL. Opponent shaping is a powerful approach to improve learning dynamics in these games, accounting for player influence on others’ updates. Learning with Opponent-Learning Awareness (LOLA) is a recent algorithm that exploits this response and leads to cooperation in settings like the Iterated Prisoner’s Dilemma. Although experimentally successful, we show that LOLA agents can exhibit ‘arrogant’ behaviour directly at odds with convergence. In fact, remarkably few algorithms have theoretical guarantees applying across all (n-player, non-convex) games. In this paper we present Stable Opponent Shaping (SOS), a new method that interpolates between LOLA and a stable variant named LookAhead. We prove that LookAhead converges locally to equilibria and avoids strict saddles in all differentiable games. SOS inherits these essential guarantees, while also shaping the learning of opponents and consistently either matching or outperforming LOLA experimentally.", "output": [ "Opponent shaping is a powerful approach to multi-agent learning but can prevent convergence; our SOS algorithm fixes this with strong guarantees in all differentiable games." ] }, { "id": "task668-6c62e10a85464543956a91c944ca849c", "input": "In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer. In the field of Natural Language Processing, word embeddings such as word2vec and GLoVe are state-of-the-art methods for applying neural network models on textual data. Attempts have been made for utilizing GANs with word embeddings for text generation. This work presents an approach to text generation using Skip-Thought sentence embeddings in conjunction with GANs based on gradient penalty functions and f-measures. The results of using sentence embeddings with GANs for generating text conditioned on input information are comparable to the approaches where word embeddings are used.", "output": [ "Generating text using sentence embeddings from Skip-Thought Vectors with the help of Generative Adversarial Networks.", "Describes application of generative adversarial networks for modeling textual data with the help of ski-thought vectors and experiments with different flavors of GANs for two different datasets." ] }, { "id": "task668-5d47695fb10541d0a20bf644b46245e9", "input": "Humans have a natural curiosity to imagine what it feels like to exist as someone or something else. This curiosity becomes even stronger for the pets we care for. Humans cannot truly know what it is like to be our pets, but we can deepen our understanding of what it is like to perceive and explore the world like them. We investigate how wearables can offer people animal perspective-taking opportunities to experience the world through animal senses that differ from those biologically natural to us. To assess the potential of wearables in animal perspective-taking, we developed a sensory-augmenting wearable that gives wearers cat-like whiskers. We then created a maze exploration experience where blindfolded participants utilized the whiskers to navigate the maze. We draw on animal behavioral research to evaluate how the whisker activity supported authentically cat-like experiences, and discuss the implications of this work for future learning experiences.", "output": [ "This paper explores using wearable sensory augmenting technology to facilitate first-hand perspective-taking of what it is like to have cat-like whiskers." ] }, { "id": "task668-add5de5da18b45b48a4a0d8ed456d17e", "input": "Interpretability has largely focused on local explanations, i.e. explaining why a model made a particular prediction for a sample. These explanations are appealing due to their simplicity and local fidelity. However, they do not provide information about the general behavior of the model. We propose to leverage model distillation to learn global additive explanations that describe the relationship between input features and model predictions. These global explanations take the form of feature shapes, which are more expressive than feature attributions. Through careful experimentation, we show qualitatively and quantitatively that global additive explanations are able to describe model behavior and yield insights about models such as neural nets. A visualization of our approach applied to a neural net as it is trained is available at https://youtu.be/ErQYwNqzEdc", "output": [ "We propose to leverage model distillation to learn global additive explanations in the form of feature shapes (that are more expressive than feature attributions) for models such as neural nets trained on tabular data.", "This paper incorporates Generalized Additive Models (GAMs) with model distillation to provide global explanations of neural nets." ] }, { "id": "task668-46d8b2e91a4b4d8b8bb8b7b2708e041a", "input": "The interpretability of an AI agent's behavior is of utmost importance for effective human-AI interaction. To this end, there has been increasing interest in characterizing and generating interpretable behavior of the agent. An alternative approach to guarantee that the agent generates interpretable behavior would be to design the agent's environment such that uninterpretable behaviors are either prohibitively expensive or unavailable to the agent. To date, there has been work under the umbrella of goal or plan recognition design exploring this notion of environment redesign in some specific instances of interpretable of behavior. In this position paper, we scope the landscape of interpretable behavior and environment redesign in all its different flavors. Specifically, we focus on three specific types of interpretable behaviors -- explicability, legibility, and predictability -- and present a general framework for the problem of environment design that can be instantiated to achieve each of the three interpretable behaviors. We also discuss how specific instantiations of this framework correspond to prior works on environment design and identify exciting opportunities for future work.", "output": [ "We present an approach to redesign the environment such that uninterpretable agent behaviors are minimized or eliminated." ] }, { "id": "task668-80c14a404b2e440f8fab9cd7af50d810", "input": "In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks (MPNNs). More specifically, we introduce a graph neural network called Colored Local Iterative Procedure (CLIP) that uses colors to disambiguate identical node attributes, and show that this representation is a universal approximator of continuous functions on graphs with node attributes. Our method relies on separability, a key topological characteristic that allows to extend well-chosen neural networks into universal representations. Finally, we show experimentally that CLIP is capable of capturing structural characteristics that traditional MPNNs fail to distinguish, while being state-of-the-art on benchmark graph classification datasets.", "output": [ "This paper introduces a coloring scheme for node disambiguation in graph neural networks based on separability, proven to be a universal MPNN extension." ] }, { "id": "task668-da327634de5e4137851e0a3fe85822ec", "input": "Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales. Generative adversarial networks (GANs) have seen wide success at generating images that are both locally and globally coherent, but they have seen little application to audio generation. In this paper we introduce WaveGAN, a first attempt at applying GANs to unsupervised synthesis of raw-waveform audio. WaveGAN is capable of synthesizing one second slices of audio waveforms with global coherence, suitable for sound effect generation. Our experiments demonstrate that—without labels—WaveGAN learns to produce intelligible words when trained on a small-vocabulary speech dataset, and can also synthesize audio from other domains such as drums, bird vocalizations, and piano. We compare WaveGAN to a method which applies GANs designed for image generation on image-like audio feature representations, finding both approaches to be promising.", "output": [ "Learning to synthesize raw waveform audio with GANs" ] }, { "id": "task668-9e7d1462cb254317aa5acb4a2dd290dd", "input": "\nThe recent expansion of machine learning applications to molecular biology proved to have a significant contribution to our understanding of biological systems, and genome functioning in particular. Technological advances enabled the collection of large epigenetic datasets, including information about various DNA binding factors (ChIP-Seq) and DNA spatial structure (Hi-C). Several studies have confirmed the correlation between DNA binding factors and Topologically Associating Domains (TADs) in DNA structure. However, the information about physical proximity represented by genomic coordinate was not yet used for the improvement of the prediction models.\n\n In this research, we focus on Machine Learning methods for prediction of folding patterns of DNA in a classical model organism Drosophila melanogaster. The paper considers linear models with four types of regularization, Gradient Boosting and Recurrent Neural Networks for the prediction of chromatin folding patterns from epigenetic marks. The bidirectional LSTM RNN model outperformed all the models and gained the best prediction scores. This demonstrates the utilization of complex models and the importance of memory of sequential DNA states for the chromatin folding. We identify informative epigenetic features that lead to the further conclusion of their biological significance.", "output": [ "We apply RNN to solve the biological problem of chromatin folding patterns prediction from epigenetic marks and demonstrate for the first time that utilization of memory of sequential states on DNA molecule is significant for the best performance." ] }, { "id": "task668-a346548125694aa7b5ded2af454416fa", "input": "Automatic Essay Scoring (AES) has been an active research area as it can greatly reduce the workload of teachers and prevents subjectivity bias . Most recent AES solutions apply deep neural network (DNN)-based models with regression, where the neural neural-based encoder learns an essay representation that helps differentiate among the essays and the corresponding essay score is inferred by a regressor. Such DNN approach usually requires a lot of expert-rated essays as training data in order to learn a good essay representation for accurate scoring. However, such data is usually expensive and thus is sparse. Inspired by the observation that human usually scores an essay by comparing it with some references, we propose a Siamese framework called Referee Network (RefNet) which allows the model to compare the quality of two essays by capturing the relative features that can differentiate the essay pair. The proposed framework can be applied as an extension to regression models as it can capture additional relative features on top of internal information. Moreover, it intrinsically augment the data by pairing thus is ideal for handling data sparsity. Experiment shows that our framework can significantly improve the existing regression models and achieve acceptable performance even when the training data is greatly reduced.", "output": [ "Automatically score essays on sparse data by comparing new essays with known samples with Referee Network. " ] }, { "id": "task668-3ae9536176834926bbe3bf79d3406e57", "input": "RMSProp and ADAM continue to be extremely popular algorithms for training neural nets but their theoretical convergence properties have remained unclear. Further, recent work has seemed to suggest that these algorithms have worse generalization properties when compared to carefully tuned stochastic gradient descent or its momentum variants. In this work, we make progress towards a deeper understanding of ADAM and RMSProp in two ways. First, we provide proofs that these adaptive gradient algorithms are guaranteed to reach criticality for smooth non-convex objectives, and we give bounds on the running time.\n\n Next we design experiments to empirically study the convergence and generalization properties of RMSProp and ADAM against Nesterov's Accelerated Gradient method on a variety of common autoencoder setups and on VGG-9 with CIFAR-10. Through these experiments we demonstrate the interesting sensitivity that ADAM has to its momentum parameter \\beta_1. We show that at very high values of the momentum parameter (\\beta_1 = 0.99) ADAM outperforms a carefully tuned NAG on most of our experiments, in terms of getting lower training and test losses. On the other hand, NAG can sometimes do better when ADAM's \\beta_1 is set to the most commonly used value: \\beta_1 = 0.9, indicating the importance of tuning the hyperparameters of ADAM to get better generalization performance.\n\n We also report experiments on different autoencoders to demonstrate that NAG has better abilities in terms of reducing the gradient norms, and it also produces iterates which exhibit an increasing trend for the minimum eigenvalue of the Hessian of the loss function at the iterates.", "output": [ "In this paper we prove convergence to criticality of (stochastic and deterministic) RMSProp and deterministic ADAM for smooth non-convex objectives and we demonstrate an interesting beta_1 sensitivity for ADAM on autoencoders. ", "This paper presents a convergence analysis of RMSProp and ADAM in the case of smooth non-convex functions" ] }, { "id": "task668-e6caeabac3b44be18f9ca84b4d3b9cfe", "input": "In order to mimic the human ability of continual acquisition and transfer of knowledge across various tasks, a learning system needs the capability for life-long learning, effectively utilizing the previously acquired skills. As such, the key challenge is to transfer and generalize the knowledge learned from one task to other tasks, avoiding interference from previous knowledge and improving the overall performance. In this paper, within the continual learning paradigm, we introduce a method that effectively forgets the less useful data samples continuously across different tasks. The method uses statistical leverage score information to measure the importance of the data samples in every task and adopts frequent directions approach to enable a life-long learning property. This effectively maintains a constant training size across all tasks. We first provide some mathematical intuition for the method and then demonstrate its effectiveness with experiments on variants of MNIST and CIFAR100 datasets.", "output": [ "A new method uses statistical leverage score information to measure the importance of the data samples in every task and adopts frequent directions approach to enable a life-long learning property." ] }, { "id": "task668-b43c587d46c64adbb0af802e898f2721", "input": "Emoji suggestion systems based on typed text have been proposed to encourage emoji usage and enrich text messaging; however, such systems’ actual effects on the chat experience remain unknown. We built an Android keyboard with both lexical (word-based) and semantic (meaning-based) emoji suggestion capabilities and compared these in two different studies. To investigate the effect of emoji suggestion in online conversations, we conducted a laboratory text-messaging study with 24 participants, and also a 15-day longitudinal field deployment with 18 participants. We found that lexical emoji suggestions increased emoji usage by 31.5% over a keyboard without suggestions, while semantic suggestions increased emoji usage by 125.1%. However, suggestion mechanisms did not affect the chatting experience significantly. From these studies, we formulate a set of design guidelines for future emoji suggestion systems that better support users’ needs.", "output": [ "We built an Android keyboard with both lexical (word-based) and semantic (meaning-based) emoji suggestion capabilities and compared their effects in two different chat studies. " ] }, { "id": "task668-7f94570687634215ad710808ca2bebc9", "input": "We extend the learning from demonstration paradigm by providing a method for learning unknown constraints shared across tasks, using demonstrations of the tasks, their cost functions, and knowledge of the system dynamics and control constraints. Given safe demonstrations, our method uses hit-and-run sampling to obtain lower cost, and thus unsafe, trajectories. Both safe and unsafe trajectories are used to obtain a consistent representation of the unsafe set via solving a mixed integer program. Additionally, by leveraging a known parameterization of the constraint, we modify our method to learn parametric constraints in high dimensions. We show that our method can learn a six-dimensional pose constraint for a 7-DOF robot arm.", "output": [ "We can learn high-dimensional constraints from demonstrations by sampling unsafe trajectories and leveraging a known constraint parameterization." ] }, { "id": "task668-707c17be9e364e6ab8ceff35228e5563", "input": "Deep neural networks (DNNs) have attained surprising achievement during the last decade due to the advantages of automatic feature learning and freedom of expressiveness. However, their interpretability remains mysterious because DNNs are complex combinations of linear and nonlinear transformations. Even though many models have been proposed to explore the interpretability of DNNs, several challenges remain unsolved: 1) The lack of interpretability quantity measures for DNNs, 2) the lack of theory for stability of DNNs, and 3) the difficulty to solve nonconvex DNN problems with interpretability constraints. To address these challenges simultaneously, this paper presents a novel intrinsic interpretability evaluation framework for DNNs. Specifically, Four independent properties of interpretability are defined based on existing works. Moreover, we investigate the theory for the stability of DNNs, which is an important aspect of interpretability, and prove that DNNs are generally stable given different activation functions. Finally, an extended version of deep learning Alternating Direction Method of Multipliers (dlADMM) are proposed to solve DNN problems with interpretability constraints efficiently and accurately. Extensive experiments on several benchmark datasets validate several DNNs by our proposed interpretability framework.", "output": [ "We propose a novel framework to evaluate the interpretability of neural network." ] }, { "id": "task668-3bac6c15c236428abe1e666530a5562c", "input": "The task of Reading Comprehension with Multiple Choice Questions, requires a human (or machine) to read a given \\{\\textit{passage, question}\\} pair and select one of the $n$ given options. The current state of the art model for this task first computes a query-aware representation for the passage and then \\textit{selects} the option which has the maximum similarity with this representation. However, when humans perform this task they do not just focus on option selection but use a combination of \\textit{elimination} and \\textit{selection}. Specifically, a human would first try to eliminate the most irrelevant option and then read the document again in the light of this new information (and perhaps ignore portions corresponding to the eliminated option). This process could be repeated multiple times till the reader is finally ready to select the correct option. We propose \\textit{ElimiNet}, a neural network based model which tries to mimic this process. Specifically, it has gates which decide whether an option can be eliminated given the \\{\\textit{document, question}\\} pair and if so it tries to make the document representation orthogonal to this eliminatedd option (akin to ignoring portions of the document corresponding to the eliminated option). The model makes multiple rounds of partial elimination to refine the document representation and finally uses a selection module to pick the best option. We evaluate our model on the recently released large scale RACE dataset and show that it outperforms the current state of the art model on 7 out of the 13 question types in this dataset. Further we show that taking an ensemble of our \\textit{elimination-selection} based method with a \\textit{selection} based method gives us an improvement of 7\\% (relative) over the best reported performance on this dataset. \n", "output": [ "A model combining elimination and selection for answering multiple choice questions", "Gives an elaboration on the Gated Attention Reader adding gates based on answer elimination in multiple choice reading comprehension", "This paper proposes the use of an elimination gate in model architectures for reading comprehension tasks but does not achieve state-of-the-art results", "This paper propses a new multi-choice reading comprehension model based on the idea that some options should be eliminated to infer better passage/question representations." ] }, { "id": "task668-1867938eaa504a1d817d871063b65028", "input": "Supervised machine learning models for high-value computer vision applications such as medical image classification often require large datasets labeled by domain experts, which are slow to collect, expensive to maintain, and static with respect to changes in the data distribution. In this context, we assess the utility of observational supervision, where we take advantage of passively-collected signals such as eye tracking or “gaze” data, to reduce the amount of hand-labeled data needed for model training. Specifically, we leverage gaze information to directly supervise a visual attention layer by penalizing disagreement between the spatial regions the human labeler looked at the longest and those that most heavily influence model output. We present evidence that constraining the model in this way can reduce the number of labeled examples required to achieve a given performance level by as much as 50%, and that gaze information is most helpful on more difficult tasks.", "output": [ "We explore using passively collected eye-tracking data to reduce the amount of labeled data needed during training.", "A method to use gaze information to reduce the sample complexity of a model and the needed labeling effort to get a target performance, with improved results in middle-sized samples and harder tasks.", "A method to incorporate gaze signals into standard CNNs for image classification, adding a loss function term based in the difference between the model's Class Activation Map and the map constructed from eye tracking information." ] }, { "id": "task668-e45cac88c16d4b6a84ab06428364ceb1", "input": "It is clear that users should own and control their data and privacy. Utility providers are also becoming more interested in guaranteeing data privacy. Therefore, users and providers can and should collaborate in privacy protecting challenges, and this paper addresses this new paradigm. We propose a framework where the user controls what characteristics of the data they want to share (utility) and what they want to keep private (secret), without necessarily asking the utility provider to change its existing machine learning algorithms. We first analyze the space of privacy-preserving representations and derive natural information-theoretic bounds on the utility-privacy trade-off when disclosing a sanitized version of the data X. We present explicit learning architectures to learn privacy-preserving representations that approach this bound in a data-driven fashion. We describe important use-case scenarios where the utility providers are willing to collaborate with the sanitization process. We study space-preserving transformations where the utility provider can use the same algorithm on original and sanitized data, a critical and novel attribute to help service providers accommodate varying privacy requirements with a single set of utility algorithms. We illustrate this framework through the implementation of three use cases; subject-within-subject, where we tackle the problem of having a face identity detector that works only on a consenting subset of users, an important application, for example, for mobile devices activated by face recognition; gender-and-subject, where we preserve facial verification while hiding the gender attribute for users who choose to do so; and emotion-and-gender, where we hide independent variables, as is the case of hiding gender while preserving emotion detection.", "output": [ "Learning privacy-preserving transformations from data. A collaborative approach" ] }, { "id": "task668-54782907a7394fd5999fef3428dbddfd", "input": "Graphs possess exotic features like variable size and absence of natural ordering of the nodes that make them difficult to analyze and compare. To circumvent this problem and learn on graphs, graph feature representation is required. Main difficulties with feature extraction lie in the trade-off between expressiveness, consistency and efficiency, i.e. the capacity to extract features that represent the structural information of the graph while being deformation-consistent and isomorphism-invariant. While state-of-the-art methods enhance expressiveness with powerful graph neural-networks, we propose to leverage natural spectral properties of graphs to study a simple graph feature: the graph Laplacian spectrum (GLS). We analyze the representational power of this object that satisfies both isomorphism-invariance, expressiveness and deformation-consistency. In particular, we propose a theoretical analysis based on graph perturbation to understand what kind of comparison between graphs we do when comparing GLS. To do so, we derive bounds for the distance between GLS that are related to the divergence to isomorphism, a standard computationally expensive graph divergence. Finally, we experiment GLS as graph representation through consistency tests and classification tasks, and show that it is a strong graph feature representation baseline.", "output": [ "We study theoretically the consistency the Laplacian spectrum and use it as whole-graph embeddding", "This paper forcuses on the laplacian spectrum of a graph as means to generate a representation to be used to compare graphs and classify them.", "This work proposed to use Graph Laplacian spectrum to learn graph representation." ] }, { "id": "task668-fd032517de9b43be9e040fa103bdb0c5", "input": "We study the use of knowledge distillation to compress the U-net architecture. We show that, while standard distillation is not sufficient to reliably train a compressed U-net, introducing other regularization methods, such as batch normalization and class re-weighting, in knowledge distillation significantly improves the training process. This allows us to compress a U-net by over 1000x, i.e., to 0.1% of its original number of parameters, at a negligible decrease in performance.", "output": [ "We present additional techniques to use knowledge distillation to compress U-net by over 1000x.", "The authors introduced a modified distillation strategy to compress a U-net architecture by over 1000x while retaining an accuracy close to the original U-net." ] }, { "id": "task668-9badb573ea7a4530bc9fe6e36bd79af0", "input": "We propose a practical method for $L_0$ norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. AIC and BIC, well-known model selection criteria, are special cases of $L_0$ regularization. However, since the $L_0$ norm of weights is non-differentiable, we cannot incorporate it directly as a regularization term in the objective function. We propose a solution through the inclusion of a collection of non-negative stochastic gates, which collectively determine which weights to set to zero. We show that, somewhat surprisingly, for certain distributions over the gates, the expected $L_0$ regularized objective is differentiable with respect to the distribution parameters. We further propose the \\emph{hard concrete} distribution for the gates, which is obtained by ``stretching'' a binary concrete distribution and then transforming its samples with a hard-sigmoid. The parameters of the distribution over the gates can then be jointly optimized with the original network parameters. As a result our method allows for straightforward and efficient learning of model structures with stochastic gradient descent and allows for conditional computation in a principled way. We perform various experiments to demonstrate the effectiveness of the resulting approach and regularizer.", "output": [ "We show how to optimize the expected L_0 norm of parametric models with gradient descent and introduce a new distribution that facilitates hard gating.", "The authors introduce a gradient-based approach to minimize an objective function with an L0 sparse penalty to help learn sparse neural networks" ] }, { "id": "task668-b983dcdeaccb46f6965727aa93f1660c", "input": "This paper proposes a method for efficient training of Q-function for continuous-state Markov Decision Processes (MDP), such that the traces of the resulting policies satisfy a Linear Temporal Logic (LTL) property. LTL, a modal logic, can express a wide range of time-dependent logical properties including safety and liveness. We convert the LTL property into a limit deterministic Buchi automaton with which a synchronized product MDP is constructed. The control policy is then synthesised by a reinforcement learning algorithm assuming that no prior knowledge is available from the MDP. The proposed method is evaluated in a numerical study to test the quality of the generated control policy and is compared against conventional methods for policy synthesis such as MDP abstraction (Voronoi quantizer) and approximate dynamic programming (fitted value iteration).", "output": [ "As safety is becoming a critical notion in machine learning we believe that this work can act as a foundation for a number of research directions such as safety-aware learning algorithms." ] }, { "id": "task668-f2002e3799744548aa3299a6e164721a", "input": "The phase problem in diffraction physics is one of the oldest inverse problems in all of science. The central difficulty that any approach to solving this inverse problem must overcome is that half of the information, namely the phase of the diffracted beam, is always missing. In the context of electron microscopy, the phase problem is generally non-linear and solutions provided by phase-retrieval techniques are known to be poor approximations to the physics of electrons interacting with matter. Here, we show that a semi-supervised learning approach can effectively solve the phase problem in electron microscopy/scattering. In particular, we introduce a new Deep Neural Network (DNN), Y-net, which simultaneously learns a reconstruction algorithm via supervised training in addition to learning a physics-based regularization via unsupervised training. We demonstrate that this constrained, semi-supervised approach is an order of magnitude more data-efficient and accurate than the same model trained in a purely supervised fashion. In addition, the architecture of the Y-net model provides for a straightforward evaluation of the consistency of the model's prediction during inference and is generally applicable to the phase problem in other settings.", "output": [ "We introduce a semi-supervised deep neural network to approximate the solution of the phase problem in electron microscopy" ] }, { "id": "task668-6ba11158891b4c99aacc816510df52ac", "input": "Contextualized word representations such as ELMo and BERT have become the de facto starting point for incorporating pretrained representations for downstream NLP tasks. In these settings, contextual representations have largely made obsolete their static embedding predecessors such as Word2Vec and GloVe. However, static embeddings do have their advantages in that they are straightforward to understand and faster to use. Additionally, embedding analysis methods for static embeddings are far more diverse and mature than those available for their dynamic counterparts. In this work, we introduce simple methods for generating static lookup table embeddings from existing pretrained contextual representations and demonstrate they outperform Word2Vec and GloVe embeddings on a variety of word similarity and word relatedness tasks. In doing so, our results also reveal insights that may be useful for subsequent downstream tasks using our embeddings or the original contextual models. Further, we demonstrate the increased potential for analysis by applying existing approaches for estimating social bias in word embeddings. Our analysis constitutes the most comprehensive study of social bias in contextual word representations (via the proxy of our distilled embeddings) and reveals a number of inconsistencies in current techniques for quantifying social bias in word embeddings. We publicly release our code and distilled word embeddings to support reproducible research and the broader NLP community.", "output": [ "A procedure for distilling contextual models into static embeddings; we apply our method to 9 popular models and demonstrate clear gains in representation quality wrt Word2Vec/GloVe and improved analysis potential by thoroughly studying social bias." ] }, { "id": "task668-deb5ba3d859441ed8aebe678fd85d69e", "input": "We analyze the dynamics of training deep ReLU networks and their implications on generalization capability. Using a teacher-student setting, we discovered a novel relationship between the gradient received by hidden student nodes and the activations of teacher nodes for deep ReLU networks. With this relationship and the assumption of small overlapping teacher node activations, we prove that (1) student nodes whose weights are initialized to be close to teacher nodes converge to them at a faster rate, and (2) in over-parameterized regimes and 2-layer case, while a small set of lucky nodes do converge to the teacher nodes, the fan-out weights of other nodes converge to zero. This framework provides insight into multiple puzzling phenomena in deep learning like over-parameterization, implicit regularization, lottery tickets, etc. We verify our assumption by showing that the majority of BatchNorm biases of pre-trained VGG11/16 models are negative. Experiments on (1) random deep teacher networks with Gaussian inputs, (2) teacher network pre-trained on CIFAR-10 and (3) extensive ablation studies validate our multiple theoretical predictions.", "output": [ "A theoretical framework for deep ReLU network that can explains multiple puzzling phenomena like over-parameterization, implicit regularization, lottery tickets, etc. " ] }, { "id": "task668-11c2f5b203a74d45acd1385857e48f95", "input": "In complex tasks, such as those with large combinatorial action spaces, random exploration may be too inefficient to achieve meaningful learning progress. In this work, we use a curriculum of progressively growing action spaces to accelerate learning. We assume the environment is out of our control, but that the agent may set an internal curriculum by initially restricting its action space. Our approach uses off-policy reinforcement learning to estimate optimal value functions for multiple action spaces simultaneously and efficiently transfers data, value estimates, and state representations from restricted action spaces to the full task. We show the efficacy of our approach in proof-of-concept control tasks and on challenging large-scale StarCraft micromanagement tasks with large, multi-agent action spaces.", "output": [ "Progressively growing the available action space is a great curriculum for learning agents" ] }, { "id": "task668-3acf377c067e4337bd38146233459dda", "input": "The gap between the empirical success of deep learning and the lack of strong theoretical guarantees calls for studying simpler models. By observing that a ReLU neuron is a product of a linear function with a gate (the latter determines whether the neuron is active or not), where both share a jointly trained weight vector, we propose to decouple the two. We introduce GaLU networks — networks in which each neuron is a product of a Linear Unit, defined by a weight vector which is being trained, with a Gate, defined by a different weight vector which is not being trained. Generally speaking, given a base model and a simpler version of it, the two parameters that determine the quality of the simpler version are whether its practical performance is close enough to the base model and whether it is easier to analyze it theoretically. We show that GaLU networks perform similarly to ReLU networks on standard datasets and we initiate a study of their theoretical properties, demonstrating that they are indeed easier to analyze. We believe that further research of GaLU networks may be fruitful for the development of a theory of deep learning.", "output": [ "We propose Gated Linear Unit networks — a model that performs similarly to ReLU networks on real data while being much easier to analyze theoretically." ] }, { "id": "task668-a81ad36e5f4c462eb4650b7e346b20a3", "input": "Real-world Relation Extraction (RE) tasks are challenging to deal with, either due to limited training data or class imbalance issues. In this work, we present Data Augmented Relation Extraction (DARE), a simple method to augment training data by properly finetuning GPT2 to generate examples for specific relation types. The generated training data is then used in combination with the gold dataset to train a BERT-based RE classifier. In a series of experiments we show the advantages of our method, which leads in improvements of up to 11 F1 score points compared to a strong baseline. Also, DARE achieves new state-of-the-art in three widely used biomedical RE datasets surpassing the previous best results by 4.7 F1 points on average.", "output": [ "Data Augmented Relation Extraction with GPT-2" ] }, { "id": "task668-adabbc07aa1e4e48857cceb825d9e67d", "input": "Recurrent neural networks (RNNs) can model natural language by sequentially ''reading'' input tokens and outputting a distributed representation of each token. Due to the sequential nature of RNNs, inference time is linearly dependent on the input length, and all inputs are read regardless of their importance. Efforts to speed up this inference, known as ''neural speed reading'', either ignore or skim over part of the input. We present Structural-Jump-LSTM: the first neural speed reading model to both skip and jump text during inference. The model consists of a standard LSTM and two agents: one capable of skipping single words when reading, and one capable of exploiting punctuation structure (sub-sentence separators (,:), sentence end symbols (.!?), or end of text markers) to jump ahead after reading a word.\n A comprehensive experimental evaluation of our model against all five state-of-the-art neural reading models shows that \n Structural-Jump-LSTM achieves the best overall floating point operations (FLOP) reduction (hence is faster), while keeping the same accuracy or even improving it compared to a vanilla LSTM that reads the whole text.", "output": [ "We propose a new model for neural speed reading that utilizes the inherent punctuation structure of a text to define effective jumping and skipping behavior.", "The paper proposes a Structural-Jump-LSTM model to speed up machine reading with two agents instead of one", "Proposes a novel model for neural speed reading in which the new reader has the ability to skip a word or sequence of words.", "The paper proposes a fast-reading method using skip and jump actions, showing that the proposed method is as accurate as LSTM but uses much less computation." ] }, { "id": "task668-5878ffd9a53b4f6899aa6fa2041f94d8", "input": "We investigate the robustness properties of image recognition models equipped with two features inspired by human vision, an explicit episodic memory and a shape bias, at the ImageNet scale. As reported in previous work, we show that an explicit episodic memory improves the robustness of image recognition models against small-norm adversarial perturbations under some threat models. It does not, however, improve the robustness against more natural, and typically larger, perturbations. Learning more robust features during training appears to be necessary for robustness in this second sense. We show that features derived from a model that was encouraged to learn global, shape-based representations (Geirhos et al., 2019) do not only improve the robustness against natural perturbations, but when used in conjunction with an episodic memory, they also provide additional robustness against adversarial perturbations. Finally, we address three important design choices for the episodic memory: memory size, dimensionality of the memories and the retrieval method. We show that to make the episodic memory more compact, it is preferable to reduce the number of memories by clustering them, instead of reducing their dimensionality.", "output": [ "systematic study of large-scale cache-based image recognition models, focusing particularly on their robustness properties", "This paper proposed to use memory cache to improve robustness against adversarial image examples, and concluded that using a large continous cache is not superior to hard attention." ] }, { "id": "task668-839fbeac432d43a0819596cfc8add6e0", "input": "Ranking is a central task in machine learning and information retrieval. In this task, it is especially important to present the user with a slate of items that is appealing as a whole. This in turn requires taking into account interactions between items, since intuitively, placing an item on the slate affects the decision of which other items should be chosen alongside it.\n In this work, we propose a sequence-to-sequence model for ranking called seq2slate. At each step, the model predicts the next item to place on the slate given the items already chosen. The recurrent nature of the model allows complex dependencies between items to be captured directly in a flexible and scalable way. We show how to learn the model end-to-end from weak supervision in the form of easily obtained click-through data. We further demonstrate the usefulness of our approach in experiments on standard ranking benchmarks as well as in a real-world recommendation system.", "output": [ "A pointer network architecture for re-ranking items, learned from click-through logs." ] }, { "id": "task668-3fd023aa7c324f0bbcc657a5263615a1", "input": "Bitcoin is a virtual coinage system that enables users to trade virtually free of a central trusted authority. All transactions on the Bitcoin blockchain are publicly available for viewing, yet as Bitcoin is built mainly for security it’s original structure does not allow for direct analysis of address transactions. \n Existing analysis methods of the Bitcoin blockchain can be complicated, computationally expensive or inaccurate. We propose a computationally efficient model to analyze bitcoin blockchain addresses and allow for their use with existing machine learning algorithms. We compare our approach against Multi Level Sequence Learners (MLSLs), one of the best performing models on bitcoin address data.", "output": [ "a 2vec model for cryptocurrency transaction graphs", "The paper proposes to use an autoencoder, networkX, and node2Vec to predict whether a Bitcoin address will become empty after a year, but the results are worse than an existing baseline." ] }, { "id": "task668-6332af166cab4f5a9281fed9fe994e72", "input": "We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features. In general, our superstructure is a tree structure of multiple super latent variables and it is automatically learned from data. When there is only one latent variable in the superstructure, our model reduces to one that assumes the latent features to be generated from a Gaussian mixture model. We call our model the latent tree variational autoencoder (LTVAE). Whereas previous deep learning methods for clustering produce only one partition of data, LTVAE produces multiple partitions of data, each being given by one super latent variable. This is desirable because high dimensional data usually have many different natural facets and can be meaningfully partitioned in multiple ways.", "output": [ "We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features." ] }, { "id": "task668-8f823d21ba574646ba71afc874a2b5ec", "input": "Universal approximation property of neural networks is one of the motivations to use these models in various real-world problems. However, this property is not the only characteristic that makes neural networks unique as there is a wide range of other approaches with similar property. Another characteristic which makes these models interesting is that they can be trained with the backpropagation algorithm which allows an efficient gradient computation and gives these universal approximators the ability to efficiently learn complex manifolds from a large amount of data in different domains. Despite their abundant use in practice, neural networks are still not well understood and a broad range of ongoing research is to study the interpretability of neural networks. On the other hand, topological data analysis (TDA) relies on strong theoretical framework of (algebraic) topology along with other mathematical tools for analyzing possibly complex datasets. In this work, we leverage a universal approximation theorem originating from algebraic topology to build a connection between TDA and common neural network training framework. We introduce the notion of automatic subdivisioning and devise a particular type of neural networks for regression tasks: Simplicial Complex Networks (SCNs). SCN's architecture is defined with a set of bias functions along with a particular policy during the forward pass which alternates the common architecture search framework in neural networks. We believe the view of SCNs can be used as a step towards building interpretable deep learning models. Finally, we verify its performance on a set of regression problems.", "output": [ "A novel method for supervised learning through subdivisioning the input space along with function approximation." ] }, { "id": "task668-0caef17fe28c42478bf034c35aad2da6", "input": "Real world images often contain large amounts of private / sensitive information that should be carefully protected without reducing their utilities. In this paper, we propose a privacy-preserving deep learning framework with a learnable ob- fuscator for the image classification task. Our framework consists of three mod- els: learnable obfuscator, classifier and reconstructor. The learnable obfuscator is used to remove the sensitive information in the images and extract the feature maps from them. The reconstructor plays the role as an attacker, which tries to recover the image from the feature maps extracted by the obfuscator. In order to best protect users’ privacy in images, we design an adversarial training methodol- ogy for our framework to optimize the obfuscator. Through extensive evaluations on real world datasets, both the numerical metrics and the visualization results demonstrate that our framework is qualified to protect users’ privacy and achieve a relatively high accuracy on the image classification task.", "output": [ "We proposed a novel deep learning image classification framework that can both accurately classify images and protect users' privacy.", "This paper proposes a framework which preserves the private information in the image and doesn’t compromise the usability of the image.", "This current work suggests using adversarial networks to obfuscate images and thus allow collecting them without privacy concerns to use them for training machine learning models." ] }, { "id": "task668-dce826be6d16483ca2c395599aefc905", "input": "Deep learning is becoming more widespread in its application due to its power in solving complex classification problems. However, deep learning models often require large memory and energy consumption, which may prevent them from being deployed effectively on embedded platforms, limiting their applications. This work addresses the problem by proposing methods {\\em Weight Reduction Quantisation} for compressing the memory footprint of the models, including reducing the number of weights and the number of bits to store each weight. Beside, applying with sparsity-inducing regularization, our work focuses on speeding up stochastic variance reduced gradients (SVRG) optimization on non-convex problem. Our method that mini-batch SVRG with $\\ell$1 regularization on non-convex problem has faster and smoother convergence rates than SGD by using adaptive learning rates. Experimental evaluation of our approach uses MNIST and CIFAR-10 datasets on LeNet-300-100 and LeNet-5 models, showing our approach can reduce the memory requirements both in the convolutional and fully connected layers by up to 60$\\times$ without affecting their test accuracy.", "output": [ "Compression of Deep neural networks deployed on embedded device. ", "The authors present an l-1 regularized SVRG based training algorithm that is able to force many weights of the network to be 0.", "This work reduces memory requirements." ] }, { "id": "task668-20a570c324274bf289c1a42ec3521dd2", "input": "Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction. To better understand such attacks, a characterization is needed of the properties of regions (the so-called `adversarial subspaces') in which adversarial examples lie. We tackle this challenge by characterizing the dimensional properties of adversarial regions, via the use of Local Intrinsic Dimensionality (LID). LID assesses the space-filling capability of the region surrounding a reference example, based on the distance distribution of the example to its neighbors. We first provide explanations about how adversarial perturbation can affect the LID characteristic of adversarial regions, and then show empirically that LID characteristics can facilitate the distinction of adversarial examples generated using state-of-the-art attacks. As a proof-of-concept, we show that a potential application of LID is to distinguish adversarial examples, and the preliminary results show that it can outperform several state-of-the-art detection measures by large margins for five attack strategies considered in this paper across three benchmark datasets. Our analysis of the LID characteristic for adversarial regions not only motivates new directions of effective adversarial defense, but also opens up more challenges for developing new attacks to better understand the vulnerabilities of DNNs.", "output": [ "We characterize the dimensional properties of adversarial subspaces in the neighborhood of adversarial examples via the use of Local Intrinsic Dimensionality (LID)." ] }, { "id": "task668-fe1be226477141aabfb8f217e8312fba", "input": "Adversarial training is one of the most popular ways to learn robust models but is usually attack-dependent and time costly. In this paper, we propose the MACER algorithm, which learns robust models without using adversarial training but performs better than all existing provable l2-defenses. Recent work shows that randomized smoothing can be used to provide certified l2 radius to smoothed classifiers, and our algorithm trains provably robust smoothed classifiers via MAximizing the CErtified Radius (MACER). The attack-free characteristic makes MACER faster to train and easier to optimize. In our experiments, we show that our method can be applied to modern deep neural networks on a wide range of datasets, including Cifar-10, ImageNet, MNIST, and SVHN. For all tasks, MACER spends less training time than state-of-the-art adversarial training algorithms, and the learned models achieve larger average certified radius.", "output": [ "We propose MACER: a provable defense algorithm that trains robust models by maximizing the certified radius. It does not use adversarial training but performs better than all existing provable l2-defenses." ] }, { "id": "task668-321572df540f4b298e3b4b8a8a21ea71", "input": "Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources. There has been continuous progress in machine learning research for ICU management, and most of this work has focused on using time series signals recorded by ICU instruments. In our work, we show that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management. While the time-series data is measured at regular intervals, doctor notes are charted at irregular times, making it challenging to model them together. We propose a method to model them jointly, achieving considerable improvement across benchmark tasks over baseline time-series model.", "output": [ "We demostarte that using clinical notes in conjuntion with ICU instruments data improves the perfomance on ICU management benchmark tasks" ] }, { "id": "task668-1736399b15384ad3a5ac22ed69e2fbac", "input": "Deep learning approaches usually require a large amount of labeled data to generalize. However, humans can learn a new concept only by a few samples. One of the high cogntition human capablities is to learn several concepts at the same time. In this paper, we address the task of classifying multiple objects by seeing only a few samples from each category. To the best of authors' knowledge, there is no dataset specially designed for few-shot multiclass classification. We design a task of mutli-object few class classification and an environment for easy creating controllable datasets for this task. We demonstrate that the proposed dataset is sound using a method which is an extension of prototypical networks.", "output": [ "We introduce a diagnostic task which is a variation of few-shot learning and introduce a dataset for it." ] }, { "id": "task668-e335744b586d4a7093e4fdf773b6dc19", "input": "Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks. However, typical GANs require fully-observed data during training. In this paper, we present a GAN-based framework for learning from complex, high-dimensional incomplete data. The proposed framework learns a complete data generator along with a mask generator that models the missing data distribution. We further demonstrate how to impute missing data by equipping our framework with an adversarially trained imputer. We evaluate the proposed framework using a series of experiments with several types of missing data processes under the missing completely at random assumption.", "output": [ "This paper presents a GAN-based framework for learning the distribution from high-dimensional incomplete data." ] }, { "id": "task668-6088e0bf666f43ca8f4b4d5dc5f220a6", "input": "The Deep Image Prior (DIP, Ulyanov et al., 2017) is a fascinating recent approach for recovering images which appear natural, yet is not fully understood. This work aims at shedding some further light on this approach by investigating the properties of the early outputs of the DIP. First, we show that these early iterations demonstrate invariance to adversarial perturbations by classifying progressive DIP outputs and using a novel saliency map approach. Next we explore using DIP as a defence against adversaries, showing good potential. Finally, we examine the adversarial invariancy of the early DIP outputs, and hypothesize that these outputs may remove non-robust image features. By comparing classification confidence values we show some evidence confirming this hypothesis.", "output": [ "We investigate properties of the recently introduced Deep Image Prior (Ulyanov et al, 2017)" ] }, { "id": "task668-52009c57f1cf4ba09571c1262fcc1cd5", "input": "Long Short-Term Memory (LSTM) is one of the most powerful sequence models. Despite the strong performance, however, it lacks the nice interpretability as in state space models. In this paper, we present a way to combine the best of both worlds by introducing State Space LSTM (SSL), which generalizes the earlier work \\cite{zaheer2017latent} of combining topic models with LSTM. However, unlike \\cite{zaheer2017latent}, we do not make any factorization assumptions in our inference algorithm. We present an efficient sampler based on sequential Monte Carlo (SMC) method that draws from the joint posterior directly. Experimental results confirms the superiority and stability of this SMC inference algorithm on a variety of domains.", "output": [ "We present State Space LSTM models, a combination of state space models and LSTMs, and propose an inference algorithm based on sequential Monte Carlo. " ] }, { "id": "task668-7305222203ad456886211001c70c425e", "input": "We present a novel approach to train a natural media painting using reinforcement learning. Given a reference image, our formulation is based on stroke-based rendering that imitates human drawing and can be learned from scratch without supervision. Our painting agent computes a sequence of actions that represent the primitive painting strokes. In order to ensure that the generated policy is predictable and controllable, we use a constrained learning method and train the painting agent using the environment model and follows the commands encoded in an observation. We have applied our approach on many benchmarks and our results demonstrate that our constrained agent can handle different painting media and different constraints in the action space to collaborate with humans or other agents.\n", "output": [ "We train a natural media painting agent using environment model. Based on our painting agent, we present a novel approach to train a constrained painting agent that follows the command encoded in the observation." ] }, { "id": "task668-067f2ab1de3640da90464b1673f7f2e3", "input": "Analogical reasoning has been a principal focus of various waves of AI research. Analogy is particularly challenging for machines because it requires relational structures to be represented such that they can be flexibly applied across diverse domains of experience. Here, we study how analogical reasoning can be induced in neural networks that learn to perceive and reason about raw visual data. We find that the critical factor for inducing such a capacity is not an elaborate architecture, but rather, careful attention to the choice of data and the manner in which it is presented to the model. The most robust capacity for analogical reasoning is induced when networks learn analogies by contrasting abstract relational structures in their input domains, a training method that uses only the input data to force models to learn about important abstract features. Using this technique we demonstrate capacities for complex, visual and symbolic analogy making and generalisation in even the simplest neural network architectures.", "output": [ "The most robust capacity for analogical reasoning is induced when networks learn analogies by contrasting abstract relational structures in their input domains.", "The paper investigates the ability of a neural network to learn analogy, showing that a simple neural network is able to solve certain analogy problems", "This paper describes an approach to train neural networks for analogical reasoning tasks, specifically considering visual analogy and symbolic analogies." ] }, { "id": "task668-b586d20d46c14c729c338b640cf4839b", "input": "High-dimensional data often lie in or close to low-dimensional subspaces. Sparse subspace clustering methods with sparsity induced by L0-norm, such as L0-Sparse Subspace Clustering (L0-SSC), are demonstrated to be more effective than its L1 counterpart such as Sparse Subspace Clustering (SSC). However, these L0-norm based subspace clustering methods are restricted to clean data that lie exactly in subspaces. Real data often suffer from noise and they may lie close to subspaces. We propose noisy L0-SSC to handle noisy data so as to improve the robustness. We show that the optimal solution to the optimization problem of noisy L0-SSC achieves subspace detection property (SDP), a key element with which data from different subspaces are separated, under deterministic and randomized models. Our results provide theoretical guarantee on the correctness of noisy L0-SSC in terms of SDP on noisy data. We further propose Noisy-DR-L0-SSC which provably recovers the subspaces on dimensionality reduced data. Noisy-DR-L0-SSC first projects the data onto a lower dimensional space by linear transformation, then performs noisy L0-SSC on the dimensionality reduced data so as to improve the efficiency. The experimental results demonstrate the effectiveness of noisy L0-SSC and Noisy-DR-L0-SSC.", "output": [ "We propose Noisy-DR-L0-SSC (Noisy Dimension Reduction L0-Sparse Subspace Clustering) to efficiently partition noisy data in accordance to their underlying subspace structure." ] }, { "id": "task668-59654a1c97d945f8b5cff1fb1a55cfb8", "input": "Reduced precision computation is one of the key areas addressing the widening’compute gap’, driven by an exponential growth in deep learning applications. In recent years, deep neural network training has largely migrated to 16-bit precision,with significant gains in performance and energy efficiency. However, attempts to train DNNs at 8-bit precision have met with significant challenges, because of the higher precision and dynamic range requirements of back-propagation. In this paper, we propose a method to train deep neural networks using 8-bit floating point representation for weights, activations, errors, and gradients. We demonstrate state-of-the-art accuracy across multiple data sets (imagenet-1K, WMT16)and a broader set of workloads (Resnet-18/34/50, GNMT, and Transformer) than previously reported. We propose an enhanced loss scaling method to augment the reduced subnormal range of 8-bit floating point, to improve error propagation. We also examine the impact of quantization noise on generalization, and propose a stochastic rounding technique to address gradient noise. As a result of applying all these techniques, we report slightly higher validation accuracy compared to full precision baseline.", "output": [ "We demonstrated state-of-the-art training results using 8-bit floating point representation, across Resnet, GNMT, Transformer." ] }, { "id": "task668-292a21b9dfb646858695ab20a8b54ca1", "input": "Recent research about margin theory has proved that maximizing the minimum margin like support vector machines does not necessarily lead to better performance, and instead, it is crucial to optimize the margin distribution. In the meantime, margin theory has been used to explain the empirical success of deep network in recent studies. In this paper, we present ODN (the Optimal margin Distribution Network), a network which embeds a loss function in regard to the optimal margin distribution. We give a theoretical analysis for our method using the PAC-Bayesian framework, which confirms the significance of the margin distribution for classification within the framework of deep networks. In addition, empirical results show that the ODN model always outperforms the baseline cross-entropy loss model consistently across different regularization situations. And our ODN\n model also outperforms the cross-entropy loss (Xent), hinge loss and soft hinge loss model in generalization task through limited training data.", "output": [ "This paper presents a deep neural network embedding a loss function in regard to the optimal margin distribution, which alleviates the overfitting problem theoretically and empirically.", "Presents a PAC-Bayesian bound for a margin loss" ] }, { "id": "task668-63fb8077fde04509987eebb2457a1de6", "input": "Through many recent advances in graph representation learning, performance achieved on tasks involving graph-structured data has substantially increased in recent years---mostly on tasks involving node-level predictions. The setup of prediction tasks over entire graphs (such as property prediction for a molecule, or side-effect prediction for a drug), however, proves to be more challenging, as the algorithm must combine evidence about several structurally relevant patches of the graph into a single prediction.\n Most prior work attempts to predict these graph-level properties while considering only one graph at a time---not allowing the learner to directly leverage structural similarities and motifs across graphs. Here we propose a setup in which a graph neural network receives pairs of graphs at once, and extend it with a co-attentional layer that allows node representations to easily exchange structural information across them. We first show that such a setup provides natural benefits on a pairwise graph classification task (drug-drug interaction prediction), and then expand to a more generic graph regression setup: enhancing predictions over QM9, a standard molecular prediction benchmark. Our setup is flexible, powerful and makes no assumptions about the underlying dataset properties, beyond anticipating the existence of multiple training graphs.", "output": [ "We use graph co-attention in a paired graph training system for graph classification and regression.", "This paper injects a multi-head co-attention mechanism in GCN that allows one drug to attend to another drug during drug side effect prediction.", "A method to extend graph-based learning with a co-attentional layer, which outperforms other previous ones on a pairwise graph classification task." ] }, { "id": "task668-edb01f9e7ca74787a2d1b9dc2d32b145", "input": "In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML, Finn et al., 2017) for low resource neural machine translation (NMT). We frame low-resource translation as a meta-learning problem, and we learn to adapt to low-resource languages based on multilingual high-resource language tasks. We use the universal lexical representation (Gu et al., 2018b) to overcome the input-output mismatch across different languages. We evaluate the proposed meta-learning strategy using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt, Nl, Pl, Pt, Sk, Sl, Sv and Ru) as source tasks and five diverse languages (Ro, Lv, Fi, Tr, and Ko) as target tasks. We show that the proposed approach significantly outperforms the multilingual, transfer learning based approach (Zoph et al., 2016) and enables us to train a competitive NMT system with only a fraction of training examples. For instance, the proposed approach can achieve as high as 22.04 BLEU on Romanian-English WMT’16 by seeing only 16,000 translated words (\u0018~600 parallel sentences).", "output": [ "we propose a meta-learning approach for low-resource neural machine translation that can rapidly learn to translate on a new language" ] }, { "id": "task668-01c961f920c047d3985597f78d6fc3f6", "input": "In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date. The most popular sequence to sequence models typically “generate and hope” generic utterances that can be memorized in the weights of the model when mapping from input utterance(s) to output, rather than employing recalled knowledge as context. Use of knowledge has so far proved difficult, in part because of the lack of a supervised learning benchmark task which exhibits knowledgeable open dialogue with clear grounding. To that end we collect and release a large dataset with conversations directly grounded with knowledge retrieved from Wikipedia. We then design architectures capable of retrieving knowledge, reading and conditioning on it, and finally generating natural responses. Our best performing dialogue models are able to conduct knowledgeable discussions on open-domain topics as evaluated by automatic metrics and human evaluations, while our new benchmark allows for measuring further improvements in this important research direction.", "output": [ "We build knowledgeable conversational agents by conditioning on Wikipedia + a new supervised task." ] }, { "id": "task668-d2e6732aba84452d98244c36e80dce32", "input": "We propose a neural language model capable of unsupervised syntactic structure induction. The model leverages the structure information to form better semantic representations and better language modeling. Standard recurrent neural networks are limited by their structure and fail to efficiently use syntactic information. On the other hand, tree-structured recursive networks usually require additional structural supervision at the cost of human expert annotation. In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model. In our model, the gradient can be directly back-propagated from the language model loss into the neural parsing network. Experiments show that the proposed model can discover the underlying syntactic structure and achieve state-of-the-art performance on word/character-level language model tasks.", "output": [ "In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model." ] }, { "id": "task668-54f895d2a03f40a1a8d4f6ba576c6d72", "input": "Foveation is an important part of human vision, and a number of deep networks have also used foveation. However, there have been few systematic comparisons between foveating and non-foveating deep networks, and between different variable-resolution downsampling methods. Here we define several such methods, and compare their performance on ImageNet recognition with a Densenet-121 network. The best variable-resolution method slightly outperforms uniform downsampling. Thus in our experiments, foveation does not substantially help or hinder object recognition in deep networks.", "output": [ "We compare object recognition performance on images that are downsampled uniformly and with three different foveation schemes." ] }, { "id": "task668-b59febd61b0f4d5f8400e672e355a40c", "input": "There exists a plethora of techniques for inducing structured sparsity in parametric models during the optimization process, with the final goal of resource-efficient inference. However, to the best of our knowledge, none target a specific number of floating-point operations (FLOPs) as part of a single end-to-end optimization objective, despite reporting FLOPs as part of the results. Furthermore, a one-size-fits-all approach ignores realistic system constraints, which differ significantly between, say, a GPU and a mobile phone -- FLOPs on the former incur less latency than on the latter; thus, it is important for practitioners to be able to specify a target number of FLOPs during model compression. In this work, we extend a state-of-the-art technique to directly incorporate FLOPs as part of the optimization objective and show that, given a desired FLOPs requirement, different neural networks can be successfully trained for image classification.", "output": [ "We extend a state-of-the-art technique to directly incorporate FLOPs as part of the optimization objective, and we show that, given a desired FLOPs requirement, different neural networks are successfully trained." ] }, { "id": "task668-615f9756d3fb45bbae29ab400d20e8eb", "input": "Deep networks have achieved impressive results across a variety of important tasks. However, a known weakness is a failure to perform well when evaluated on data which differ from the training distribution, even if these differences are very small, as is the case with adversarial examples. We propose \\emph{Fortified Networks}, a simple transformation of existing networks, which “fortifies” the hidden layers in a deep network by identifying when the hidden states are off of the data manifold, and maps these hidden states back to parts of the data manifold where the network performs well. Our principal contribution is to show that fortifying these hidden states improves the robustness of deep networks and our experiments (i) demonstrate improved robustness to standard adversarial attacks in both black-box and white-box threat models; (ii) suggest that our improvements are not primarily due to the problem of deceptively good results due to degraded quality in the gradient signal (the gradient masking problem) and (iii) show the advantage of doing this fortification in the hidden layers instead of the input space. We demonstrate improvements in adversarial robustness on three datasets (MNIST, Fashion MNIST, CIFAR10), across several attack parameters, both white-box and black-box settings, and the most widely studied attacks (FGSM, PGD, Carlini-Wagner). We show that these improvements are achieved across a wide variety of hyperparameters. ", "output": [ "Better adversarial training by learning to map back to the data manifold with autoencoders in the hidden states. " ] }, { "id": "task668-1890903122274a20bd9522852a79970a", "input": "We introduce CBF, an exploration method that works in the absence of rewards or end of episode signal. CBF is based on intrinsic reward derived from the error of a dynamics model operating in feature space. It was inspired by (Pathak et al., 2017), is easy to implement, and can achieve results such as passing four levels of Super Mario Bros, navigating VizDoom mazes and passing two levels of SpaceInvaders. We investigated the effect of combining the method with several auxiliary tasks, but find inconsistent improvements over the CBF baseline.\n", "output": [ "A simple intrinsic motivation method using forward dynamics model error in feature space of the policy." ] }, { "id": "task668-1f8589f6678146da8d92c36af3cd321d", "input": "Recent theoretical and experimental results suggest the possibility of using current and near-future quantum hardware in challenging sampling tasks. In this paper, we introduce free-energy-based reinforcement learning (FERL) as an application of quantum hardware. We propose a method for processing a quantum annealer’s measured qubit spin configurations in approximating the free energy of a quantum Boltzmann machine (QBM). We then apply this method to perform reinforcement learning on the grid-world problem using the D-Wave 2000Q quantum annealer. The experimental results show that our technique is a promising method for harnessing the power of quantum sampling in reinforcement learning tasks.", "output": [ "We train Quantum Boltzmann Machines using a replica stacking method and a quantum annealer to perform a reinforcement learning task." ] }, { "id": "task668-8005559fbae6449c9ad6ed47aa3daaf0", "input": "For natural language understanding (NLU) technology to be maximally useful, it must be able to process language in a way that is not exclusive to a single task, genre, or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation (GLUE) benchmark, a collection of tools for evaluating the performance of models across a diverse set of existing NLU tasks. By including tasks with limited training data, GLUE is designed to favor and encourage models that share general linguistic knowledge across tasks. GLUE also includes a hand-crafted diagnostic test suite that enables detailed linguistic analysis of models. We evaluate baselines based on current methods for transfer and representation learning and find that multi-task training on all tasks performs better than training a separate model per task. However, the low absolute performance of our best model indicates the need for improved general NLU systems.", "output": [ "We present a multi-task benchmark and analysis platform for evaluating generalization in natural language understanding systems." ] }, { "id": "task668-ae3b615e49d44b8b864a246190e3a9b6", "input": "Neural networks have succeeded in many reasoning tasks. Empirically, these tasks require specialized network structures, e.g., Graph Neural Networks (GNNs) perform well on many such tasks, while less structured networks fail. Theoretically, there is limited understanding of why and when a network structure generalizes better than other equally expressive ones. We develop a framework to characterize which reasoning tasks a network can learn well, by studying how well its structure aligns with the algorithmic structure of the relevant reasoning procedure. We formally define algorithmic alignment and derive a sample complexity bound that decreases with better alignment. This framework explains the empirical success of popular reasoning models and suggests their limitations. We unify seemingly different reasoning tasks, such as intuitive physics, visual question answering, and shortest paths, via the lens of a powerful algorithmic paradigm, dynamic programming (DP). We show that GNNs can learn DP and thus solve these tasks. On several reasoning tasks, our theory aligns with empirical results.", "output": [ "We develop a theoretical framework to characterize which reasoning tasks a neural network can learn well.", "The paper proposes a measure of classes of algorithmic alignment that measure how \"close\" neural networks are to known algorithms, proving the link between several classes of known algorithms and neural network architectures." ] }, { "id": "task668-f8b63ec196e74ef183d73f1b2547b71f", "input": "We present a simple and effective algorithm designed to address the covariate shift problem in imitation learning. It operates by training an ensemble of policies on the expert demonstration data, and using the variance of their predictions as a cost which is minimized with RL together with a supervised behavioral cloning cost. Unlike adversarial imitation methods, it uses a fixed reward function which is easy to optimize. We prove a regret bound for the algorithm in the tabular setting which is linear in the time horizon multiplied by a coefficient which we show to be low for certain problems in which behavioral cloning fails. We evaluate our algorithm empirically across multiple pixel-based Atari environments and continuous control tasks, and show that it matches or significantly outperforms behavioral cloning and generative adversarial imitation learning.", "output": [ "Method for addressing covariate shift in imitation learning using ensemble uncertainty" ] }, { "id": "task668-26fa585625bf4e1d8ad9624961597972", "input": "We propose a framework to understand the unprecedented performance and robustness of deep neural networks using field theory. Correlations between the weights within the same layer can be described by symmetries in that layer, and networks generalize better if such symmetries are broken to reduce the redundancies of the weights. Using a two parameter field theory, we find that the network can break such symmetries itself towards the end of training in a process commonly known in physics as spontaneous symmetry breaking. This corresponds to a network generalizing itself without any user input layers to break the symmetry, but by communication with adjacent layers. In the layer decoupling limit applicable to residual networks (He et al., 2015), we show that the remnant symmetries that survive the non-linear layers are spontaneously broken based on empirical results. The Lagrangian for the non-linear and weight layers together has striking similarities with the one in quantum field theory of a scalar. Using results from quantum field theory we show that our framework is able to explain many experimentally observed phenomena, such as training on random labels with zero error (Zhang et al., 2017), the information bottleneck and the phase transition out of it (Shwartz-Ziv & Tishby, 2017), shattered gradients (Balduzzi et al., 2017), and many more.", "output": [ "Closed form results for deep learning in the layer decoupling limit applicable to Residual Networks" ] }, { "id": "task668-185ad3ea883d4788b9ece8a5090cd9ce", "input": "Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering. Such an organization significantly constricts the types of shared structure that can be learned. The necessity of parallel ordering for deep MTL is first tested by comparing it with permuted ordering of shared layers. The results indicate that a flexible ordering can enable more effective sharing, thus motivating the development of a soft ordering approach, which learns how shared layers are applied in different ways for different tasks. Deep MTL with soft ordering outperforms parallel ordering methods across a series of domains. These results suggest that the power of deep MTL comes from learning highly general building blocks that can be assembled to meet the demands of each task.", "output": [ "Relaxing the constraint of shared hierarchies enables more effective deep multitask learning." ] }, { "id": "task668-f68af0a168d048fba0a8786179dcafd6", "input": "We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids. To do this, we propose a motor architecture that has the general structure of an inverse model with a latent-variable bottleneck. We show that it is possible to train this model entirely offline to compress thousands of expert policies and learn a motor primitive embedding space. The trained neural probabilistic motor primitive system can perform one-shot imitation of whole-body humanoid behaviors, robustly mimicking unseen trajectories. Additionally, we demonstrate that it is also straightforward to train controllers to reuse the learned motor primitive space to solve tasks, and the resulting movements are relatively naturalistic. To support the training of our model, we compare two approaches for offline policy cloning, including an experience efficient method which we call linear feedback policy cloning. We encourage readers to view a supplementary video (https://youtu.be/CaDEf-QcKwA ) summarizing our results.", "output": [ "Neural Probabilistic Motor Primitives compress motion capture tracking policies into one flexible model capable of one-shot imitation and reuse as a low-level controller." ] }, { "id": "task668-4a2ca02d85b44c9cb597630f20beb9ba", "input": "Recent findings show that deep generative models can judge out-of-distribution samples as more likely than those drawn from the same distribution as the training data. In this work, we focus on variational autoencoders (VAEs) and address the problem of misaligned likelihood estimates on image data. We develop a novel likelihood function that is based not only on the parameters returned by the VAE but also on the features of the data learned in a self-supervised fashion. In this way, the model additionally captures the semantic information that is disregarded by the usual VAE likelihood function. We demonstrate the improvements in reliability of the estimates with experiments on the FashionMNIST and MNIST datasets.", "output": [ "Improved likelihood estimates in variational autoencoders using self-supervised feature learning" ] }, { "id": "task668-3d8fb38f6a394d42b2d95618f446d2bd", "input": "We propose two approaches of locally adaptive activation functions namely, layer-wise and neuron-wise locally adaptive activation functions, which improve the performance of deep and physics-informed neural networks. The local adaptation of activation function is achieved by introducing scalable hyper-parameters in each layer (layer-wise) and for every neuron separately (neuron-wise), and then optimizing it using the stochastic gradient descent algorithm. Introduction of neuron-wise activation function acts like a vector activation function as opposed to the traditional scalar activation function given by fixed, global and layer-wise activations. In order to further increase the training speed, an activation slope based slope recovery term is added in the loss function, which further accelerate convergence, thereby reducing the training cost. For numerical experiments, a nonlinear discontinuous function is approximated using a deep neural network with layer-wise and neuron-wise locally adaptive activation functions with and without the slope recovery term and compared with its global counterpart. Moreover, solution of the nonlinear Burgers equation, which exhibits steep gradients, is also obtained using the proposed methods. On the theoretical side, we prove that in the proposed method the gradient descent algorithms are not attracted to sub-optimal critical points or local minima under practical conditions on the initialization and learning rate. Furthermore, the proposed adaptive activation functions with the slope recovery are shown to accelerate the training process in standard deep learning benchmarks using CIFAR-10, CIFAR-100, SVHN, MNIST, KMNIST, Fashion-MNIST, and Semeion data sets with and without data augmentation.", "output": [ "Proposing locally adaptive activation functions in deep and physics-informed neural networks for faster convergence" ] }, { "id": "task668-fc5f4f7b170143dcbd4e753dbf04de1c", "input": "We present Line-Storm, an interactive computer system for creative performance. The context we investigated was writing on paper using Line-Storm. We used self-report questionnaires as part of research involving human participants, to evaluate Line-Storm. Line-Storm consisted of a writing stylus and writing pad, augmented with electronics. The writing pad was connected to a contact microphone, and the writing stylus had a small micro-controller board and peripherals attached to it. The signals from these electronic augmentations were fed into the audio-synthesis environment Max/MSP to produce an interactive soundscape. We attempted to discover whether Line-Storm enhanced a self-reported sense of being present and engaged during a writing task, and we compared Line-Storm to a non-interactive control condition. After performing statistical analysis in SPSS, we were unable to support our research hypothesis, that presence and engagement were enhanced by Line-Storm. Participants reported they were, on average, no more present and engaged during the experimental condition than during the control condition. As creativity is subtle, and varies with person, time, context, space and so many other factors, this result was somewhat expected by us. A statistically significant result of our study is that some participants responded to Line-Storm more positively than others. These Preservers of Line-Storm were a group, distinct from other participants, who reported greater presence and engagement and who wrote more words with Line-Storm and during the control condition. We discuss the results of our research and place Line-Storm in an artistic-technological context, drawing upon writings by Martin Heidegger when considering the nature of Line-Storm. Future work includes modifying interactive components, improving aesthetics and using more miniaturized electronics, experimenting with a drawing task instead of a writing task, and collaborating with a composer of electronic music to make a more interesting, immersive, and engaging interactive soundscape for writing or drawing performance.", "output": [ "Interactive stylus based sound incorporating writing system" ] }, { "id": "task668-862bf9f2ab564688a5e6098213023450", "input": "The Tensor-Train factorization (TTF) is an efficient way to compress large weight matrices of fully-connected layers and recurrent layers in recurrent neural networks (RNNs). However, high Tensor-Train ranks for all the core tensors of parameters need to be element-wise fixed, which results in an unnecessary redundancy of model parameters. This work applies Riemannian stochastic gradient descent (RSGD) to train core tensors of parameters in the Riemannian Manifold before finding vectors of lower Tensor-Train ranks for parameters. The paper first presents the RSGD algorithm with a convergence analysis and then tests it on more advanced Tensor-Train RNNs such as bi-directional GRU/LSTM and Encoder-Decoder RNNs with a Tensor-Train attention model. The experiments on digit recognition and machine translation tasks suggest the effectiveness of the RSGD algorithm for Tensor-Train RNNs.", "output": [ "Applying the Riemannian SGD (RSGD) algorithm for training Tensor-Train RNNs to further reduce model parameters.", "The paper proposes to use Riemannian stochastic gradient algorithm for low-rank tensor train learning in deep networks.", "Proposes an algorithm for optimizing neural networks parametrized by Tensor Train decomposition based on the Riemannian optimization and rank adaptation, and designs a bidirectional TT LSTM architecture." ] }, { "id": "task668-131c16fb3e6d42aabc47fbb467c92814", "input": "We introduce dynamic instance hardness (DIH) to facilitate the training of machine learning models. DIH is a property of each training sample and is computed as the running mean of the sample's instantaneous hardness as measured over the training history. We use DIH to evaluate how well a model retains knowledge about each training sample over time. We find that for deep neural nets (DNNs), the DIH of a sample in relatively early training stages reflects its DIH in later stages and as a result, DIH can be effectively used to reduce the set of training samples in future epochs. Specifically, during each epoch, only samples with high DIH are trained (since they are historically hard) while samples with low DIH can be safely ignored. DIH is updated each epoch only for the selected samples, so it does not require additional computation. Hence, using DIH during training leads to an appreciable speedup. Also, since the model is focused on the historically more challenging samples, resultant models are more accurate. The above, when formulated as an algorithm, can be seen as a form of curriculum learning, so we call our framework DIH curriculum learning (or DIHCL). The advantages of DIHCL, compared to other curriculum learning approaches, are: (1) DIHCL does not require additional inference steps over the data not selected by DIHCL in each epoch, (2) the dynamic instance hardness, compared to static instance hardness (e.g., instantaneous loss), is more stable as it integrates information over the entire training history up to the present time. Making certain mathematical assumptions, we formulate the problem of DIHCL as finding a curriculum that maximizes a multi-set function $f(\\cdot)$, and derive an approximation bound for a DIH-produced curriculum relative to the optimal curriculum. Empirically, DIHCL-trained DNNs significantly outperform random mini-batch SGD and other recently developed curriculum learning methods in terms of efficiency, early-stage convergence, and final performance, and this is shown in training several state-of-the-art DNNs on 11 modern datasets.", "output": [ "New understanding of training dynamics and metrics of memorization hardness lead to efficient and provable curriculum learning.", "This paper formulates DIH as a curriculum leaning problem that can more effectively utilize the data to train DNNs, and derives theory on the approximation bound." ] }, { "id": "task668-0001e964a7d04ea88b45c61052b4cca0", "input": "Synthesizing user-intended programs from a small number of input-output exam-\n ples is a challenging problem with several important applications like spreadsheet\n manipulation, data wrangling and code refactoring. Existing synthesis systems\n either completely rely on deductive logic techniques that are extensively hand-\n engineered or on purely statistical models that need massive amounts of data, and in\n general fail to provide real-time synthesis on challenging benchmarks. In this work,\n we propose Neural Guided Deductive Search (NGDS), a hybrid synthesis technique\n that combines the best of both symbolic logic techniques and statistical models.\n Thus, it produces programs that satisfy the provided specifications by construction\n and generalize well on unseen examples, similar to data-driven systems. Our\n technique effectively utilizes the deductive search framework to reduce the learning\n problem of the neural component to a simple supervised learning setup. Further,\n this allows us to both train on sparingly available real-world data and still leverage\n powerful recurrent neural network encoders. We demonstrate the effectiveness\n of our method by evaluating on real-world customer scenarios by synthesizing\n accurate programs with up to 12× speed-up compared to state-of-the-art systems.", "output": [ "We integrate symbolic (deductive) and statistical (neural-based) methods to enable real-time program synthesis with almost perfect generalization from 1 input-output example.", "The paper presents a branch-and-bound approach to learn good programs where an LSTM is used to predict which branches in the search tree should lead to good programs", "Proposes system that synthesizes programs from a single example that generalize better than prior state-of-the-art" ] }, { "id": "task668-9cf0ab55ae7a4a23bb85a19e184f6b99", "input": "We discuss the feasibility of the following learning problem: given unmatched samples from two domains and nothing else, learn a mapping between the two, which preserves semantics. Due to the lack of paired samples and without any definition of the semantic information, the problem might seem ill-posed. Specifically, in typical cases, it seems possible to build infinitely many alternative mappings from every target mapping. This apparent ambiguity stands in sharp contrast to the recent empirical success in solving this problem.\n\n We identify the abstract notion of aligning two domains in a semantic way with concrete terms of minimal relative complexity. A theoretical framework for measuring the complexity of compositions of functions is developed in order to show that it is reasonable to expect the minimal complexity mapping to be unique. The measured complexity used is directly related to the depth of the neural networks being learned and a semantically aligned mapping could then be captured simply by learning using architectures that are not much bigger than the minimal architecture.\n\n Various predictions are made based on the hypothesis that semantic alignment can be captured by the minimal mapping. These are verified extensively. In addition, a new mapping algorithm is proposed and shown to lead to better mapping results.", "output": [ "Our hypothesis is that given two domains, the lowest complexity mapping that has a low discrepancy approximates the target mapping.", "The paper addresses the problem of learning mappings between different domains without any supervision, stating three conjectures.", "Demonstrates that in unsupervised learning on unaligned data it is possible to learn the between domains mapping using GAN only without a reconstruction loss." ] }, { "id": "task668-c4c2775eecc3446db6ce132ff0d3a26a", "input": "A restricted Boltzmann machine (RBM) learns a probabilistic distribution over its input samples and has numerous uses like dimensionality reduction, classification and generative modeling. Conventional RBMs accept vectorized data that dismisses potentially important structural information in the original tensor (multi-way) input. Matrix-variate and tensor-variate RBMs, named MvRBM and TvRBM, have been proposed but are all restrictive by construction. This work presents the matrix product operator RBM (MPORBM) that utilizes a tensor network generalization of Mv/TvRBM, preserves input formats in both the visible and hidden layers, and results in higher expressive power. A novel training algorithm integrating contrastive divergence and an alternating optimization procedure is also developed.", "output": [ "Propose a general tensor-based RBM model which can compress the model greatly at the same keep a strong model expression capacity" ] }, { "id": "task668-a3bec69049bc417cba5d0350f91d0c33", "input": "The training of deep neural networks with Stochastic Gradient Descent (SGD) with a large learning rate or a small batch-size typically ends in flat regions of the weight space, as indicated by small eigenvalues of the Hessian of the training loss. This was found to correlate with a good final generalization performance. In this paper we extend previous work by investigating the curvature of the loss surface along the whole training trajectory, rather than only at the endpoint. We find that initially SGD visits increasingly sharp regions, reaching a maximum sharpness determined by both the learning rate and the batch-size of SGD. At this peak value SGD starts to fail to minimize the loss along directions in the loss surface corresponding to the largest curvature (sharpest directions). To further investigate the effect of these dynamics in the training process, we study a variant of SGD using a reduced learning rate along the sharpest directions which we show can improve training speed while finding both sharper and better generalizing solution, compared to vanilla SGD. Overall, our results show that the SGD dynamics in the subspace of the sharpest directions influence the regions that SGD steers to (where larger learning rate or smaller batch size result in wider regions visited), the overall training speed, and the generalization ability of the final model.", "output": [ "SGD is steered early on in training towards a region in which its step is too large compared to curvature, which impacts the rest of training. ", "Analyzes the relationship between the convergence/generalization and the update on largest eigenvectors of Hessian of the empirical losses of DNNs.", "This work studies the relationship between the SGD step size and the curvature of the loss surface" ] }, { "id": "task668-81e1664500c74fe5982f8aacbaa1220c", "input": "We propose a method to efficiently learn diverse strategies in reinforcement learning for query reformulation in the tasks of document retrieval and question answering. In the proposed framework an agent consists of multiple specialized sub-agents and a meta-agent that learns to aggregate the answers from sub-agents to produce a final answer. Sub-agents are trained on disjoint partitions of the training data, while the meta-agent is trained on the full training set. Our method makes learning faster, because it is highly parallelizable, and has better generalization performance than strong baselines, such as an ensemble of agents trained on the full data. We show that the improved performance is due to the increased diversity of reformulation strategies.", "output": [ "Multiple diverse query reformulation agents trained with reinforcement learning to improve search engines.", "Parellelization of ensemble method in reinforement learning for query reformulation, speeding up training and improving the diversity of learnt freformulations", "The authors propose to train multiple distinct agents, each over a different subset of the training set.", "The authors propose an ensemble approach for query reformulation" ] }, { "id": "task668-c992b0ba054740eb8c9dc2913212d7c4", "input": "This is an empirical paper which constructs color invariant networks and evaluates their performances on a realistic data set. The paper studies the simplest possible case of color invariance: invariance under pixel-wise permutation of the color channels. Thus the network is aware not of the specific color object, but its colorfulness. The data set introduced in the paper consists of images showing crashed cars from which ten classes were extracted. An additional annotation was done which labeled whether the car shown was red or non-red. The networks were evaluated by their performance on the classification task. With the color annotation we altered the color ratios in the training data and analyzed the generalization capabilities of the networks on the unaltered test data. We further split the test data in red and non-red cars and did a similar evaluation. It is shown in the paper that an pixel-wise ordering of the rgb-values of the images performs better or at least similarly for small deviations from the true color ratios. The limits of these networks are also discussed.", "output": [ "We construct and evaluate color invariant neural nets on a novel realistic data set", "Proposes a method to make neural networks for image recognition color invariant and evaluates it on the cifar 10 dataset.", "The authors investigate a modified input layer that results in color invariant networks, and show that certain color invariant input layers can improve accuracy for test-images from a different color distribution than the training images.", "The authors test a CNN on images with color channels modified to be invariant to permutations, with performance not degraded by too much. " ] }, { "id": "task668-a65a7242419a4a9da3cf239a811ac948", "input": "The increasing demand for neural networks (NNs) being employed on embedded devices has led to plenty of research investigating methods for training low precision NNs. While most methods involve a quantization step, we propose a principled Bayesian approach where we first infer a distribution over a discrete weight space from which we subsequently derive hardware-friendly low precision NNs. To this end, we introduce a probabilistic forward pass to approximate the intractable variational objective that allows us to optimize over discrete-valued weight distributions for NNs with sign activation functions. In our experiments, we show that our model achieves state of the art performance on several real world data sets. In addition, the resulting models exhibit a substantial amount of sparsity that can be utilized to further reduce the computational costs for inference.", "output": [ "Variational Inference for infering a discrete distribution from which a low-precision neural network is derived" ] }, { "id": "task668-86e97638938e4c8aa159b59e37426142", "input": "Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL. However, the theoretical understanding of such methods has been rather limited. This paper introduces a novel algorithmic framework for designing and analyzing model-based RL algorithms with theoretical guarantees. We design a meta-algorithm with a theoretical guarantee of monotone improvement to a local maximum of the expected reward. The meta-algorithm iteratively builds a lower bound of the expected reward based on the estimated dynamical model and sample trajectories, and then maximizes the lower bound jointly over the policy and the model. The framework extends the optimism-in-face-of-uncertainty principle to non-linear dynamical models in a way that requires no explicit uncertainty quantification. Instantiating our framework with simplification gives a variant of model-based RL algorithms Stochastic Lower Bounds Optimization (SLBO). Experiments demonstrate that SLBO achieves the state-of-the-art performance when only 1M or fewer samples are permitted on a range of continuous control benchmark tasks.", "output": [ "We design model-based reinforcement learning algorithms with theoretical guarantees and achieve state-of-the-art results on Mujuco benchmark tasks when one million or fewer samples are permitted.", "The paper proposed a framework to design model-based RL algorithms based on OFU that achieves SOTA performance on MuJoCo tasks." ] }, { "id": "task668-8b9a878f531c453b8324761ae99ec065", "input": "Parameters are one of the most critical components of machine learning models. As datasets and learning domains change, it is often necessary and time-consuming to re-learn entire models. Rather than re-learning the parameters from scratch, replacing learning with optimization, we propose a framework building upon the theory of \\emph{optimal transport} to adapt model parameters by discovering correspondences between models and data, significantly amortizing the training cost. We demonstrate our idea on the challenging problem of creating probabilistic spatial representations for autonomous robots. Although recent mapping techniques have facilitated robust occupancy mapping, learning all spatially-diverse parameters in such approximate Bayesian models demand considerable computational time, discouraging them to be used in real-world robotic mapping. Considering the fact that the geometric features a robot would observe with its sensors are similar across various environments, in this paper, we demonstrate how to re-use parameters and hyperparameters learned in different domains. This adaptation is computationally more efficient than variational inference and Monte Carlo techniques. A series of experiments conducted on realistic settings verified the possibility of transferring thousands of such parameters with a negligible time and memory cost, enabling large-scale mapping in urban environments.", "output": [ "We present a method of adapting hyperparameters of probabilistic models using optimal transport with applications in robotics" ] }, { "id": "task668-34d7d6b1cce240a6880d0da6c9710c5f", "input": "Over the last few years exciting work in deep generative models has produced models able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models are able to generate molecules with desirable properties, their utility in practice is limited due to the difficulty in knowing how to synthesize these molecules. We therefore propose a new molecule generation model, mirroring a more realistic real-world process, where reactants are selected and combined to form more complex molecules. More specifically, our generative model proposes a bag of initial reactants (selected from a pool of commercially-available molecules) and uses a reaction model to predict how they react together to generate new molecules. Modeling the entire process of constructing a molecule during generation offers a number of advantages. First, we show that such a model has the ability to generate a wide, diverse set of valid and unique molecules due to the useful inductive biases of modeling reactions. Second, modeling synthesis routes rather than final molecules offers practical advantages to chemists who are not only interested in new molecules but also suggestions on stable and safe synthetic routes. Third, we demonstrate the capabilities of our model to also solve one-step retrosynthesis problems, predicting a set of reactants that can produce a target product.", "output": [ "A deep generative model for organic molecules that first generates reactant building blocks before combining these using a reaction predictor.", "A molecular generative model that generates molecules via a two-step process that provides synthesis routes of the generated molecules, allowing users to examine the synthetic accessibility of generated compounds." ] }, { "id": "task668-6d73339aa6cf457ca51b19f5a957d586", "input": "We prove bounds on the generalization error of convolutional networks.\n The bounds are in terms of the training loss, the number of\n parameters, the Lipschitz constant of the loss and the distance from\n the weights to the initial weights. They are independent of the\n number of pixels in the input, and the height and width of hidden\n feature maps. We present experiments with CIFAR-10, along with varying\n hyperparameters of a deep convolutional network, comparing our bounds\n with practical generalization gaps.", "output": [ "We prove generalization bounds for convolutional neural networks that take account of weight-tying", "Studies the generalization power of CNNs and improves the upper bounds of generalization errors, showing correlation between the generalization error of learned CNNs and the upper bound's dominant term.", "This paper presents a generalization bound for convolutional neural networks based on the number of parameters, the Lipschitz constant, and the distance of the final weights from initialization." ] }, { "id": "task668-68d80d528f80426baf56e5f570af3406", "input": "Although there are more than 65,000 languages in the world, the pronunciations of many phonemes sound similar across the languages. When people learn a foreign language, their pronunciation often reflect their native language's characteristics. That motivates us to investigate how the speech synthesis network learns the pronunciation when multi-lingual dataset is given. In this study, we train the speech synthesis network bilingually in English and Korean, and analyze how the network learns the relations of phoneme pronunciation between the languages. Our experimental result shows that the learned phoneme embedding vectors are located closer if their pronunciations are similar across the languages. Based on the result, we also show that it is possible to train networks that synthesize English speaker's Korean speech and vice versa. In another experiment, we train the network with limited amount of English dataset and large Korean dataset, and analyze the required amount of dataset to train a resource-poor language with the help of resource-rich languages.", "output": [ "Learned phoneme embeddings of multilingual neural speech synthesis network could represent relations of phoneme pronunciation between the languages." ] }, { "id": "task668-adac683482a94eb99bbc26a727e5476a", "input": "Reconstruction of few-view x-ray Computed Tomography (CT) data is a highly ill-posed problem. It is often used in applications that require low radiation dose in clinical CT, rapid industrial scanning, or fixed-gantry CT. Existing analytic or iterative algorithms generally produce poorly reconstructed images, severely deteriorated by artifacts and noise, especially when the number of x-ray projections is considerably low. This paper presents a deep network-driven approach to address extreme few-view CT by incorporating convolutional neural network-based inference into state-of-the-art iterative reconstruction. The proposed method interprets few-view sinogram data using attention-based deep networks to infer the reconstructed image. The predicted image is then used as prior knowledge in the iterative algorithm for final reconstruction. We demonstrate effectiveness of the proposed approach by performing reconstruction experiments on a chest CT dataset.", "output": [ "We present a CNN inference-based reconstruction algorithm to address extremely few-view CT. " ] }, { "id": "task668-989e2abd1c4646fbae3c0ccd284c4efa", "input": "RNNs have been shown to be excellent models for sequential data and in particular for session-based user behavior. The use of RNNs provides impressive performance benefits over classical methods in session-based recommendations. In this work we introduce a novel ranking loss function tailored for RNNs in recommendation settings. The better performance of such loss over alternatives, along with further tricks and improvements described in this work, allow to achieve an overall improvement of up to 35% in terms of MRR and Recall@20 over previous session-based RNN solutions and up to 51% over classical collaborative filtering approaches. Unlike data augmentation-based improvements, our method does not increase training times significantly.", "output": [ "Improving session-based recommendations with RNNs (GRU4Rec) by 35% using newly designed loss functions and sampling.", "This paper analyzes existing loss functions for session-based recommendations and proposes two novel losses functions which add a weighting to existing ranking-based loss functions", "Presents modifications on top of earlier work for session-based recommendation using RNN by weighting negative examples by their \"relevance\"", "This paper discusses the issues for optimizing the loss functions in GRU4Rec, proposes tricks for optimizing, and suggests an enhanced version." ] }, { "id": "task668-6f9c23b354d9410c9fe3bf6ccb07d7f0", "input": "The ability to generalize quickly from few observations is crucial for intelligent systems. In this paper we introduce APL, an algorithm that approximates probability distributions by remembering the most surprising observations it has encountered. These past observations are recalled from an external memory module and processed by a decoder network that can combine information from different memory slots to generalize beyond direct recall. We show this algorithm can perform as well as state of the art baselines on few-shot classification benchmarks with a smaller memory footprint. In addition, its memory compression allows it to scale to thousands of unknown labels. Finally, we introduce a meta-learning reasoning task which is more challenging than direct classification. In this setting, APL is able to generalize with fewer than one example per class via deductive reasoning.", "output": [ "We introduce a model which generalizes quickly from few observations by storing surprising information and attending over the most relevant data at each time point." ] }, { "id": "task668-dcadd61cb4354e9f9526b6c2db48401d", "input": "We investigate the methods by which a Reservoir Computing Network (RCN) learns concepts such as 'similar' and 'different' between pairs of images using a small training dataset and generalizes these concepts to previously unseen types of data. Specifically, we show that an RCN trained to identify relationships between image-pairs drawn from a subset of digits from the MNIST database or the depth maps of subset of visual scenes from a moving camera generalizes the learned transformations to images of digits unseen during training or depth maps of different visual scenes. We infer, using Principal Component Analysis, that the high dimensional reservoir states generated from an input image pair with a specific transformation converge over time to a unique relationship. Thus, as opposed to training the entire high dimensional reservoir state, the reservoir only needs to train on these unique relationships, allowing the reservoir to perform well with very few training examples. Thus, generalization of learning to unseen images is interpretable in terms of clustering of the reservoir state onto the attractor corresponding to the transformation in reservoir space. We find that RCNs can identify and generalize linear and non-linear transformations, and combinations of transformations, naturally and be a robust and effective image classifier. Additionally, RCNs perform significantly better than state of the art neural network classification techniques such as deep Siamese Neural Networks (SNNs) in generalization tasks both on the MNIST dataset and more complex depth maps of visual scenes from a moving camera. This work helps bridge the gap between explainable machine learning and biological learning through analogies using small datasets, and points to new directions in the investigation of learning processes.", "output": [ "Generalization of the relationships learnt between pairs of images using a small training data to previously unseen types of images using an explainable dynamical systems model, Reservoir Computing, and a biologically plausible learning technique based on analogies.", "Claims results of \"combining transformations\" in the context of RC by using an echo-state network with standard tanh acctivations with the difference that recurrent weights are not trained", "Novel method of classifying different distorions of MNIST data", "The paper uses an echo state network to learn to classify image transformations between pairs of images into one of fives classes." ] }, { "id": "task668-b64e3b85f096422ab088d4eac19911bf", "input": "Training an agent to solve control tasks directly from high-dimensional images with model-free reinforcement learning (RL) has proven difficult. The agent needs to learn a latent representation together with a control policy to perform the task. Fitting a high-capacity encoder using a scarce reward signal is not only extremely sample inefficient, but also prone to suboptimal convergence. Two ways to improve sample efficiency are to learn a good feature representation and use off-policy algorithms. We dissect various approaches of learning good latent features, and conclude that the image reconstruction loss is the essential ingredient that enables efficient and stable representation learning in image-based RL. Following these findings, we devise an off-policy actor-critic algorithm with an auxiliary decoder that trains end-to-end and matches state-of-the-art performance across both model-free and model-based algorithms on many challenging control tasks. We release our code to encourage future research on image-based RL.", "output": [ "We design a simple and efficient model-free off-policy method for image-based reinforcement learning that matches the state-of-the-art model-based methods in sample efficiency" ] }, { "id": "task668-fe77e9ff9d76474a8e34536fe06a2b52", "input": "We present a personalized recommender system using neural network for recommending\n products, such as eBooks, audio-books, Mobile Apps, Video and Music.\n It produces recommendations based on customer’s implicit feedback history such\n as purchases, listens or watches. Our key contribution is to formulate recommendation\n problem as a model that encodes historical behavior to predict the future\n behavior using soft data split, combining predictor and auto-encoder models. We\n introduce convolutional layer for learning the importance (time decay) of the purchases\n depending on their purchase date and demonstrate that the shape of the time\n decay function can be well approximated by a parametrical function. We present\n offline experimental results showing that neural networks with two hidden layers\n can capture seasonality changes, and at the same time outperform other modeling\n techniques, including our recommender in production. Most importantly, we\n demonstrate that our model can be scaled to all digital categories, and we observe\n significant improvements in an online A/B test. We also discuss key enhancements\n to the neural network model and describe our production pipeline. Finally\n we open-sourced our deep learning library which supports multi-gpu model parallel\n training. This is an important feature in building neural network based recommenders\n with large dimensionality of input and output data.", "output": [ "Improving recommendations using time sensitive modeling with neural networks in multiple product categories on a retail website", "The paper proposes a new neural network based method for recommendation.", "The authors describe a procedure of building their production recommender system from scratch and integrate time decay of purchases into the learning framework." ] }, { "id": "task668-5b5685140e2f4f9394f2dab821b47990", "input": "While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that number of instances per task and class is fixed. Due to such restriction, they learn to equally utilize the meta-knowledge across all the tasks, even when the number of instances per task and class largely varies. Moreover, they do not consider distributional difference in unseen tasks, on which the meta-knowledge may have less usefulness depending on the task relatedness. To overcome these limitations, we propose a novel meta-learning model that adaptively balances the effect of the meta-learning and task-specific learning within each task. Through the learning of the balancing variables, we can decide whether to obtain a solution by relying on the meta-knowledge or task-specific learning. We formulate this objective into a Bayesian inference framework and tackle it using variational inference. We validate our Bayesian Task-Adaptive Meta-Learning (Bayesian TAML) on two realistic task- and class-imbalanced datasets, on which it significantly outperforms existing meta-learning approaches. Further ablation study confirms the effectiveness of each balancing component and the Bayesian learning framework.", "output": [ "A novel meta-learning model that adaptively balances the effect of the meta-learning and task-specific learning, and also class-specific learning within each task." ] }, { "id": "task668-52f08e2a4a0148bba6c62a5a21f5d530", "input": "This work presents a method for active anomaly detection which can be built upon existing deep learning solutions for unsupervised anomaly detection. We show that a prior needs to be assumed on what the anomalies are, in order to have performance guarantees in unsupervised anomaly detection. We argue that active anomaly detection has, in practice, the same cost of unsupervised anomaly detection but with the possibility of much better results. To solve this problem, we present a new layer that can be attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active method, presenting results on both synthetic and real anomaly detection datasets.", "output": [ "A method for active anomaly detection. We present a new layer that can be attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active method." ] }, { "id": "task668-ac435aefa96948978cbda97691296f7e", "input": "Active matter consists of active agents which transform energy extracted from surroundings into momentum, producing a variety of collective phenomena. A model, synthetic active system composed of microtubule polymers driven by protein motors spontaneously forms a liquid-crystalline nematic phase. Extensile stress created by the protein motors precipitates continuous buckling and folding of the microtubules creating motile topological defects and turbulent fluid flows. Defect motion is determined by the rheological properties of the material; however, these remain largely unquantified. Measuring defects dynamics can yield fundamental insights into active nematics, a class of materials that include bacterial films and animal cells. Current methods for defect detection lack robustness and precision, and require fine-tuning for datasets with different visual quality. In this study, we applied Deep Learning to train a defect detector to automatically analyze microscopy videos of the microtubule active nematic. Experimental results indicate that our method is robust and accurate. It is expected to significantly increase the amount of video data that can be processed.", "output": [ "An interesting application of CNN in soft condensed matter physics experiments.", "The authors demonstrate that a deep learning approach offers improvement to both the identification accuracy and rate at which defects can be identified of nematic liquid crystals.", "Apply a well known neural model (YOLO) to detect bounding boxes of objects in images." ] }, { "id": "task668-3944927d680b45a8b8c83896fbe052d0", "input": "We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series. The model is inspired by standard autoregressive (AR) models and gating mechanisms used in recurrent neural networks. It involves an AR-like weighting system, where the final predictor is obtained as a weighted sum of adjusted regressors, while the weights are data-dependent functions learnt through a convolutional network. The architecture was designed for applications on asynchronous time series and is evaluated on such datasets: a hedge fund proprietary dataset of over 2 million quotes for a credit derivative index, an artificially generated noisy autoregressive series and household electricity consumption dataset. The pro-posed architecture achieves promising results as compared to convolutional and recurrent neural networks. The code for the numerical experiments and the architecture implementation will be shared online to make the research reproducible.", "output": [ "Convolutional architecture for learning data-dependent weights for autoregressive forecasting of time series." ] }, { "id": "task668-42b0a25ed88c48dc920b918c2e0ab54a", "input": "Reinforcement learning (RL) with value-based methods (e.g., Q-learning) has shown success in a variety of domains such as\n games and recommender systems (RSs). When the action space is finite, these algorithms implicitly finds a policy by learning the optimal value function, which are often very efficient. \n However, one major challenge of extending Q-learning to tackle continuous-action RL problems is that obtaining optimal Bellman backup requires solving a continuous action-maximization (max-Q) problem. While it is common to restrict the parameterization of the Q-function to be concave in actions to simplify the max-Q problem, such a restriction might lead to performance degradation. Alternatively, when the Q-function is parameterized with a generic feed-forward neural network (NN), the max-Q problem can be NP-hard. In this work, we propose the CAQL method which minimizes the Bellman residual using Q-learning with one of several plug-and-play action optimizers. In particular, leveraging the strides of optimization theories in deep NN, we show that max-Q problem can be solved optimally with mixed-integer programming (MIP)---when the Q-function has sufficient representation power, this MIP-based optimization induces better policies and is more robust than counterparts, e.g., CEM or GA, that approximate the max-Q solution. To speed up training of CAQL, we develop three techniques, namely (i) dynamic tolerance, (ii) dual filtering, and (iii) clustering.\n To speed up inference of CAQL, we introduce the action function that concurrently learns the optimal policy.\n To demonstrate the efficiency of CAQL we compare it with state-of-the-art RL algorithms on benchmark continuous control problems that have different degrees of action constraints and show that CAQL significantly outperforms policy-based methods in heavily constrained environments.", "output": [ "A general framework of value-based reinforcement learning for continuous control" ] }, { "id": "task668-85655f6338e74463904889d44eda4e52", "input": "Word embedding is a useful approach to capture co-occurrence structures in a large corpus of text. In addition to the text data itself, we often have additional covariates associated with individual documents in the corpus---e.g. the demographic of the author, time and venue of publication, etc.---and we would like the embedding to naturally capture the information of the covariates. In this paper, we propose a new tensor decomposition model for word embeddings with covariates. Our model jointly learns a \\emph{base} embedding for all the words as well as a weighted diagonal transformation to model how each covariate modifies the base embedding. To obtain the specific embedding for a particular author or venue, for example, we can then simply multiply the base embedding by the transformation matrix associated with that time or venue. The main advantages of our approach is data efficiency and interpretability of the covariate transformation matrix. Our experiments demonstrate that our joint model learns substantially better embeddings conditioned on each covariate compared to the standard approach of learning a separate embedding for each covariate using only the relevant subset of data. Furthermore, our model encourages the embeddings to be ``topic-aligned'' in the sense that the dimensions have specific independent meanings. This allows our covariate-specific embeddings to be compared by topic, enabling downstream differential analysis. We empirically evaluate the benefits of our algorithm on several datasets, and demonstrate how it can be used to address many natural questions about the effects of covariates.", "output": [ "Using the same embedding across covariates doesn't make sense, we show that a tensor decomposition algorithm learns sparse covariate-specific embeddings and naturally separable topics jointly and data-efficiently." ] }, { "id": "task668-b35f3313a98c4319b090bd7924b124f7", "input": "Deep neural networks (DNNs) although achieving human-level performance in many domains, have very large model size that hinders their broader applications on edge computing devices. Extensive research work have been conducted on DNN model compression or pruning. However, most of the previous work took heuristic approaches. This work proposes a progressive weight pruning approach based on ADMM (Alternating Direction Method of Multipliers), a powerful technique to deal with non-convex optimization problems with potentially combinatorial constraints. Motivated by dynamic programming, the proposed method reaches extremely high pruning rate by using partial prunings with moderate pruning rates. Therefore, it resolves the accuracy degradation and long convergence time problems when pursuing extremely high pruning ratios. It achieves up to 34× pruning rate for ImageNet dataset and 167× pruning rate for MNIST dataset, significantly higher than those reached by the literature work. Under the same number of epochs, the proposed method also achieves faster convergence and higher compression rates. The codes and pruned DNN models are released in the anonymous link bit.ly/2zxdlss.", "output": [ "We implement a DNN weight pruning approach that achieves the highest pruning rates.", "This paper focuses on weight pruning for neural network compression, achiving 30x compression rate for AlexNet and VGG for ImageNet.", "A progressive pruning technique which imposes structural sparsity constraint on the weight parameter and rewrites the optimization as an ADMM framework, achieving higher accurancy than projected gradient descent." ] }, { "id": "task668-a5a8cc37588c4c10908a64be6252c198", "input": "Sequential learning, also called lifelong learning, studies the problem of learning tasks in a sequence with access restricted to only the data of the current task. In this paper we look at a scenario with fixed model capacity, and postulate that the learning process should not be selfish, i.e. it should account for future tasks to be added and thus leave enough capacity for them. To achieve Selfless Sequential Learning we study different regularization strategies and activation functions. We find that\n imposing sparsity at the level of the representation (i.e. neuron activations) is more beneficial for sequential learning than encouraging parameter sparsity. In particular, we propose a novel regularizer, that encourages representation sparsity by means of neural inhibition. It results in few active neurons which in turn leaves more free neurons to be utilized by upcoming tasks. As neural inhibition over an entire layer can be too drastic, especially for complex tasks requiring strong representations,\n our regularizer only inhibits other neurons in a local neighbourhood, inspired by lateral inhibition processes in the brain. We combine our novel regularizer with state-of-the-art lifelong learning methods that penalize changes to important previously learned parts of the network. We show that our new regularizer leads to increased sparsity which translates in consistent performance improvement on diverse datasets.", "output": [ "A regularization strategy for improving the performance of sequential learning", "A novel, regularization based approach to the sequential learning problem using a fixed size model that adds extra terms to the loss, encouraging representation sparsity and combating catastrophic forgetting.", "This paper deals with the problem of catastrophic forgetting in lifelong learning by proposing regularized learning strategies" ] }, { "id": "task668-67a6acc5aba34a82a3372e31e8aed3f0", "input": "Designing a metric manually for unsupervised sequence generation tasks, such as text generation, is essentially difficult. In a such situation, learning a metric of a sequence from data is one possible solution. The previous study, SeqGAN, proposed the framework for unsupervised sequence generation, in which a metric is learned from data, and a generator is optimized with regard to the learned metric with policy gradient, inspired by generative adversarial nets (GANs) and reinforcement learning. In this paper, we make two proposals to learn better metric than SeqGAN's: partial reward function and expert-based reward function training. The partial reward function is a reward function for a partial sequence of a certain length. SeqGAN employs a reward function for completed sequence only. By combining long-scale and short-scale partial reward functions, we expect a learned metric to be able to evaluate a partial correctness as well as a coherence of a sequence, as a whole. In expert-based reward function training, a reward function is trained to discriminate between an expert (or true) sequence and a fake sequence that is produced by editing an expert sequence. Expert-based reward function training is not a kind of GAN frameworks. This makes the optimization of the generator easier. We examine the effect of the partial reward function and expert-based reward function training on synthetic data and real text data, and show improvements over SeqGAN and the model trained with MLE. Specifically, whereas SeqGAN gains 0.42 improvement of NLL over MLE on synthetic data, our best model gains 3.02 improvement, and whereas SeqGAN gains 0.029 improvement of BLEU over MLE, our best model gains 0.250 improvement.", "output": [ "This paper aims to learn a better metric for unsupervised learning, such as text generation, and shows a significant improvement over SeqGAN.", "Describes an approach to generating time sequences by learning state-action values, where the state is the sequence generated so far, and the action is the choice of the next value. ", "This paper considers the problem of improving sequence generation by learning better metrics, specifically the exposure bias problem" ] }, { "id": "task668-ec930f6502474234a1222d8427cd11d3", "input": "One of the challenges in the study of generative adversarial networks is the instability of its training. \n In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator.\n Our new normalization technique is computationally light and easy to incorporate into existing implementations. \n We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.", "output": [ "We propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator of GANs.", "This paper uses spectral regularization to normalize GAN objectives, and the ensuing GAN, called SN-GAN, essentially ensures the Lipschitz property of the discriminator.", "This paper proposes\"spectral normalization\", moving a nice step forward in improving the training of GANs." ] }, { "id": "task668-b6c9c6552cc14b83aa215b346e2acfb3", "input": "This paper presents a generic framework to tackle the crucial class mismatch problem in unsupervised domain adaptation (UDA) for multi-class distributions. Previous adversarial learning methods condition domain alignment only on pseudo labels, but noisy and inaccurate pseudo labels may perturb the multi-class distribution embedded in probabilistic predictions, hence bringing insufficient alleviation to the latent mismatch problem. Compared with pseudo labels, class prototypes are more accurate and reliable since they summarize over all the instances and are able to represent the inherent semantic distribution shared across domains. Therefore, we propose a novel Prototype-Assisted Adversarial Learning (PAAL) scheme, which incorporates instance probabilistic predictions and class prototypes together to provide reliable indicators for adversarial domain alignment. With the PAAL scheme, we align both the instance feature representations and class prototype representations to alleviate the mismatch among semantically different classes. Also, we exploit the class prototypes as proxy to minimize the within-class variance in the target domain to mitigate the mismatch among semantically similar classes. With these novelties, we constitute a Prototype-Assisted Conditional Domain Adaptation (PACDA) framework which well tackles the class mismatch problem. We demonstrate the good performance and generalization ability of the PAAL scheme and also PACDA framework on two UDA tasks, i.e., object recognition (Office-Home,ImageCLEF-DA, andOffice) and synthetic-to-real semantic segmentation (GTA5→CityscapesandSynthia→Cityscapes).", "output": [ "We propose a reliable conditional adversarial learning scheme along with a simple, generic yet effective framework for UDA tasks." ] }, { "id": "task668-652c14e5322c4028bc3dfc4ba2344112", "input": "The successful application of flexible, general learning algorithms to real-world robotics applications is often limited by their poor data-efficiency. To address the challenge, domains with more than one dominant task of interest encourage the sharing of information across tasks to limit required experiment time. To this end, we investigate compositional inductive biases in the form of hierarchical policies as a mechanism for knowledge transfer across tasks in reinforcement learning (RL). We demonstrate that this type of hierarchy enables positive transfer while mitigating negative interference. Furthermore, we demonstrate the benefits of additional incentives to efficiently decompose task solutions. Our experiments show that these incentives are naturally given in multitask learning and can be easily introduced for single objectives. We design an RL algorithm that enables stable and fast learning of structured policies and the effective reuse of both behavior components and transition data across tasks in an off-policy setting. Finally, we evaluate our algorithm in simulated environments as well as physical robot experiments and demonstrate substantial improvements in data data-efficiency over competitive baselines.", "output": [ "We develop a hierarchical, actor-critic algorithm for compositional transfer by sharing policy components and demonstrate component specialization and related direct benefits in multitask domains as well as its adaptation for single tasks.", "A combination of different learning techniques for acquiring structure and learning with asymmetric data, used to train an HRL policy.", "The authors introduce a hierarchical policy structure for use in both single task and multitask reinforcement learning, and assess the structure's usefulness on complex robotic tasks." ] }, { "id": "task668-d627a17343614408b2d908fcbacb8799", "input": "Understanding procedural language requires anticipating the causal effects of actions, even when they are not explicitly stated. In this work, we introduce Neural Process Networks to understand procedural text through (neural) simulation of action dynamics. Our model complements existing memory architectures with dynamic entity tracking by explicitly modeling actions as state transformers. The model updates the states of the entities by executing learned action operators. Empirical results demonstrate that our proposed model can reason about the unstated causal effects of actions, allowing it to provide more accurate contextual information for understanding and generating procedural text, all while offering more interpretable internal representations than existing alternatives.", "output": [ "We propose a new recurrent memory architecture that can track common sense state changes of entities by simulating the causal effects of actions." ] }, { "id": "task668-e4fb51c2b45f468db4d89d094f07cf20", "input": "Fine-tuning language models, such as BERT, on domain specific corpora has proven to be valuable in domains like scientific papers and biomedical text. In this paper, we show that fine-tuning BERT on legal documents similarly provides valuable improvements on NLP tasks in the legal domain. Demonstrating this outcome is significant for analyzing commercial agreements, because obtaining large legal corpora is challenging due to their confidential nature. As such, we show that having access to large legal corpora is a competitive advantage for commercial applications, and academic research on analyzing contracts.", "output": [ "Fine-tuning BERT on legal corpora provides marginal, but valuable, improvements on NLP tasks in the legal domain." ] }, { "id": "task668-20e0a747464e48fea2c32dbe9556f964", "input": "The goal of survival clustering is to map subjects (e.g., users in a social network, patients in a medical study) to $K$ clusters ranging from low-risk to high-risk. Existing survival methods assume the presence of clear \\textit{end-of-life} signals or introduce them artificially using a pre-defined timeout. In this paper, we forego this assumption and introduce a loss function that differentiates between the empirical lifetime distributions of the clusters using a modified Kuiper statistic. We learn a deep neural network by optimizing this loss, that performs a soft clustering of users into survival groups. We apply our method to a social network dataset with over 1M subjects, and show significant improvement in C-index compared to alternatives.", "output": [ "The goal of survival clustering is to map subjects into clusters. Without end-of-life signals, this is a challenging task. To address this task we propose a new loss function by modifying the Kuiper statistics." ] }, { "id": "task668-49eb3ef77e72460a9e02123af61b8373", "input": "Recent work on encoder-decoder models for sequence-to-sequence mapping has shown that integrating both temporal and spatial attentional mechanisms into neural networks increases the performance of the system substantially. We report on a new modular network architecture that applies an attentional mechanism not on temporal and spatial regions of the input, but on sensor selection for multi-sensor setups. This network called the sensor transformation attention network (STAN) is evaluated in scenarios which include the presence of natural noise or synthetic dynamic noise. We demonstrate how the attentional signal responds dynamically to changing noise levels and sensor-specific noise, leading to reduced word error rates (WERs) on both audio and visual tasks using TIDIGITS and GRID; and also on CHiME-3, a multi-microphone real-world noisy dataset. The improvement grows as more channels are corrupted as demonstrated on the CHiME-3 dataset. Moreover, the proposed STAN architecture naturally introduces a number of advantages including ease of removing sensors from existing architectures, attentional interpretability, and increased robustness to a variety of noise environments.", "output": [ "We introduce a modular multi-sensor network architecture with an attentional mechanism that enables dynamic sensor selection on real-world noisy data from CHiME-3.", "A generic neural architecture able to learn the attention that must be payed to different input channels depending on the relative quality of each sensor with respect to the others.", " Considers the use of attention for sensor or channel selection with results on TIDIGITS and GRID showing a benefit of attention over concatentation of features." ] }, { "id": "task668-9642bee7529d467597cc07ad03420064", "input": "Building chatbots that can accomplish goals such as booking a flight ticket is an unsolved problem in natural language understanding. Much progress has been made to build conversation models using techniques such as sequence2sequence modeling. One challenge in applying such techniques to building goal-oriented conversation models is that maximum likelihood-based models are not optimized toward accomplishing goals. Recently, many methods have been proposed to address this issue by optimizing a reward that contains task status or outcome. However, adding the reward optimization on the fly usually provides little guidance for language construction and the conversation model soon becomes decoupled from the language model. In this paper, we propose a new setting in goal-oriented dialogue system to tighten the gap between these two aspects by enforcing model level information isolation on individual models between two agents. Language construction now becomes an important part in reward optimization since it is the only way information can be exchanged. We experimented our models using self-play and results showed that our method not only beat the baseline sequence2sequence model in rewards but can also generate human-readable meaningful conversations of comparable quality.", "output": [ "A Goal-oriented Neural Conversation Model by Self-Play", "A self-play model for goal oriented dialog generation, aiming to enforce a stronger coupling between the task reward and the language model.", "This paper describes a method for improving a goal oriented dialogue system using selfplay. " ] }, { "id": "task668-f920adb15748490cb04f0460564e2c30", "input": "Power-efficient CNN Domain Specific Accelerator (CNN-DSA) chips are currently available for wide use in mobile devices. These chips are mainly used in computer vision applications. However, the recent work of Super Characters method for text classification and sentiment analysis tasks using two-dimensional CNN models has also achieved state-of-the-art results through the method of transfer learning from vision to text. In this paper, we implemented the text classification and sentiment analysis applications on mobile devices using CNN-DSA chips. Compact network representations using one-bit and three-bits precision for coefficients and five-bits for activations are used in the CNN-DSA chip with power consumption less than 300mW. For edge devices under memory and compute constraints, the network is further compressed by approximating the external Fully Connected (FC) layers within the CNN-DSA chip. At the workshop, we have two system demonstrations for NLP tasks. The first demo classifies the input English Wikipedia sentence into one of the 14 classes. The second demo classifies the Chinese online-shopping review into positive or negative.", "output": [ "Deploy text classification and sentiment analysis applications for English and Chinese on a 300mW CNN accelerator chip for on-device application scenarios." ] }, { "id": "task668-c6346b0ce14b4957a44c00be7dab7713", "input": "Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference speed and reduce memory usage compared to general linear maps. However, choosing which of the myriad structured transformations to use (and its associated parameterization) is a laborious task that requires trading off speed, space, and accuracy. We consider a different approach: we introduce a family of matrices called kaleidoscope matrices (K-matrices) that provably capture any structured matrix with near-optimal space (parameter) and time (arithmetic operation) complexity. We empirically validate that K-matrices can be automatically learned within end-to-end pipelines to replace hand-crafted procedures, in order to improve model quality. For example, replacing channel shuffles in ShuffleNet improves classification accuracy on ImageNet by up to 5%. Learnable K-matrices can also simplify hand-engineered pipelines---we replace filter bank feature computation in speech data preprocessing with a kaleidoscope layer, resulting in only 0.4% loss in accuracy on the TIMIT speech recognition task. K-matrices can also capture latent structure in models: for a challenging permuted image classification task, adding a K-matrix to a standard convolutional architecture can enable learning the latent permutation and improve accuracy by over 8 points. We provide a practically efficient implementation of our approach, and use K-matrices in a Transformer network to attain 36% faster end-to-end inference speed on a language translation task.", "output": [ "We propose a differentiable family of \"kaleidoscope matrices,\" prove that all structured matrices can be represented in this form, and use them to replace hand-crafted linear maps in deep learning models." ] }, { "id": "task668-7a8e0d0fb315460dacf1cf9e20d52d63", "input": "The problem of exploration in reinforcement learning is well-understood in the tabular case and many sample-efficient algorithms are known. Nevertheless, it is often unclear how the algorithms in the tabular setting can be extended to tasks with large state-spaces where generalization is required. Recent promising developments generally depend on problem-specific density models or handcrafted features. In this paper we introduce a simple approach for exploration that allows us to develop theoretically justified algorithms in the tabular case but that also give us intuitions for new algorithms applicable to settings where function approximation is required. Our approach and its underlying theory is based on the substochastic successor representation, a concept we develop here. While the traditional successor representation is a representation that defines state generalization by the similarity of successor states, the substochastic successor representation is also able to implicitly count the number of times each state (or feature) has been observed. This extension connects two until now disjoint areas of research. We show in traditional tabular domains (RiverSwim and SixArms) that our algorithm empirically performs as well as other sample-efficient algorithms. We then describe a deep reinforcement learning algorithm inspired by these ideas and show that it matches the performance of recent pseudo-count-based methods in hard exploration Atari 2600 games.", "output": [ "We propose the idea of using the norm of the successor representation an exploration bonus in reinforcement learning. In hard exploration Atari games, our the deep RL algorithm matches the performance of recent pseudo-count-based methods." ] }, { "id": "task668-7f13f4c4f3e04cafaec6173de76fd280", "input": "Existing approaches to neural machine translation condition each output word on previously generated outputs. We introduce a model that avoids this autoregressive property and produces its outputs in parallel, allowing an order of magnitude lower latency during inference. Through knowledge distillation, the use of input token fertilities as a latent variable, and policy gradient fine-tuning, we achieve this at a cost of as little as 2.0 BLEU points relative to the autoregressive Transformer network used as a teacher. We demonstrate substantial cumulative improvements associated with each of the three aspects of our training strategy, and validate our approach on IWSLT 2016 English–German and two WMT language pairs. By sampling fertilities in parallel at inference time, our non-autoregressive model achieves near-state-of-the-art performance of 29.8 BLEU on WMT 2016 English–Romanian.", "output": [ "We introduce the first NMT model with fully parallel decoding, reducing inference latency by 10x.", "This work proposes non-autoregressive decoder for the encoder-decoder framework in which the decision of generating a word does not depends on the prior decision of generated words", "This paper describes an approach to decode non-autoregressively for neural machine translation with the possibility of more parallel decoding which can result in a significant speed-up.", "Proposes the introduction of a set of latent variables to represent the fertility of each source word to make the target sentence generation non-autoregressive" ] }, { "id": "task668-882d39b859664ed2b1bf9d19c54d6255", "input": "The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address this “weight transport problem” (Grossberg, 1987), two biologically-plausible algorithms, proposed by Liao et al. (2016) and Lillicrap et al. (2016), relax BP’s weight symmetry requirements and demonstrate comparable learning capabilities to that of BP on small datasets. However, a recent study by Bartunov et al. (2018) finds that although feedback alignment (FA) and some variants of target-propagation (TP) perform well on MNIST and CIFAR, they perform significantly worse than BP on ImageNet. Here, we additionally evaluate the sign-symmetry (SS) algorithm (Liao et al., 2016), which differs from both BP and FA in that the feedback and feedforward weights do not share magnitudes but share signs. We examined the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures (ResNet-18 and AlexNet for ImageNet; RetinaNet for MS COCO). Surprisingly, networks trained with sign-symmetry can attain classification performance approaching that of BP-trained networks. These results complement the study by Bartunov et al. (2018) and establish a new benchmark for future biologically-plausible learning algorithms on more difficult datasets and more complex architectures.", "output": [ "Biologically plausible learning algorithms, particularly sign-symmetry, work well on ImageNet" ] }, { "id": "task668-347b104056ba4fe5a8d58e7fd48ac192", "input": "Learning rules for neural networks necessarily include some form of regularization. Most regularization techniques are conceptualized and implemented in the space of parameters. However, it is also possible to regularize in the space of functions. Here, we propose to measure networks in an $L^2$ Hilbert space, and test a learning rule that regularizes the distance a network can travel through $L^2$-space each update. This approach is inspired by the slow movement of gradient descent through parameter space as well as by the natural gradient, which can be derived from a regularization term upon functional change. The resulting learning rule, which we call Hilbert-constrained gradient descent (HCGD), is thus closely related to the natural gradient but regularizes a different and more calculable metric over the space of functions. Experiments show that the HCGD is efficient and leads to considerably better generalization.", "output": [ "It's important to consider optimization in function space, not just parameter space. We introduce a learning rule that reduces distance traveled in function space, just like SGD limits distance traveled in parameter space." ] }, { "id": "task668-7046ff32a59449368375e7161cf8df8d", "input": "We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, thus our model is an attractive candidate for applications where the encoding and decoding speed is critical. Additionally, this allows us to only sample autoregressively in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity.", "output": [ "scale and enhance VQ-VAE with powerful priors to generate near realistic images." ] }, { "id": "task668-6a20505adf624021bd59b739d17ed7bb", "input": "We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an ``incomplete'' signal such as a low-resolution image, a surface normal map, or edges. Current state-of-the-art deep generative models designed for such conditional image synthesis lack two important things: (1) they are unable to generate a large set of diverse outputs, due to the mode collapse problem. (2) they are not interpretable, making it difficult to control the synthesized output. We demonstrate that NN approaches potentially address such limitations, but suffer in accuracy on small datasets. We design a simple pipeline that combines the best of both worlds: the first stage uses a convolutional neural network (CNN) to map the input to a (overly-smoothed) image, and the second stage uses a pixel-wise nearest neighbor method to map the smoothed output to multiple high-quality, high-frequency outputs in a controllable manner. Importantly, pixel-wise matching allows our method to compose novel high-frequency content by cutting-and-pasting pixels from different training exemplars. We demonstrate our approach for various input modalities, and for various domains ranging from human faces, pets, shoes, and handbags.", "output": [ "Pixel-wise nearest neighbors used for generating multiple images from incomplete priors such as a low-res images, surface normals, edges etc." ] }, { "id": "task668-a433dbf50f7149b780dd58bf3bb9fc32", "input": "Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train. One common way to tackle this issue has been to propose new formulations of the GAN objective. Yet, surprisingly few studies have looked at optimization methods designed for this adversarial training. In this work, we cast GAN optimization problems in the general variational inequality framework. Tapping into the mathematical programming literature, we counter some common misconceptions about the difficulties of saddle point optimization and propose to extend methods designed for variational inequalities to the training of GANs. We apply averaging, extrapolation and a computationally cheaper variant that we call extrapolation from the past to the stochastic gradient method (SGD) and Adam.", "output": [ "We cast GANs in the variational inequality framework and import techniques from this literature to optimize GANs better; we give algorithmic extensions and empirically test their performance for training GANs." ] }, { "id": "task668-d1c34eb1bbac4918be291e5dbe4037da", "input": "We propose a Warped Residual Network (WarpNet) using a parallelizable warp operator for forward and backward propagation to distant layers that trains faster than the original residual neural network. We apply a perturbation theory on residual networks and decouple the interactions between residual units. The resulting warp operator is a first order approximation of the output over multiple layers. The first order perturbation theory exhibits properties such as binomial path lengths and exponential gradient scaling found experimentally by Veit et al (2016). \n We demonstrate through an extensive performance study that the proposed network achieves comparable predictive performance to the original residual network with the same number of parameters, while achieving a significant speed-up on the total training time. As WarpNet performs model parallelism in residual network training in which weights are distributed over different GPUs, it offers speed-up and capability to train larger networks compared to original residual networks.", "output": [ "We propose the Warped Residual Network using a parallelizable warp operator for forward and backward propagation to distant layers that trains faster than the original residual neural network. " ] }, { "id": "task668-ab6b94872c5e42a29682d9968782139f", "input": "The problem of visual metamerism is defined as finding a family of perceptually\n indistinguishable, yet physically different images. In this paper, we propose our\n NeuroFovea metamer model, a foveated generative model that is based on a mixture\n of peripheral representations and style transfer forward-pass algorithms. Our\n gradient-descent free model is parametrized by a foveated VGG19 encoder-decoder\n which allows us to encode images in high dimensional space and interpolate\n between the content and texture information with adaptive instance normalization\n anywhere in the visual field. Our contributions include: 1) A framework for\ncomputing metamers that resembles a noisy communication system via a foveated\nfeed-forward encoder-decoder network – We observe that metamerism arises as a\nbyproduct of noisy perturbations that partially lie in the perceptual null space; 2)\nA perceptual optimization scheme as a solution to the hyperparametric nature of\nour metamer model that requires tuning of the image-texture tradeoff coefficients\neverywhere in the visual field which are a consequence of internal noise; 3) An\n ABX psychophysical evaluation of our metamers where we also find that the rate\n of growth of the receptive fields in our model match V1 for reference metamers\n and V2 between synthesized samples. Our model also renders metamers at roughly\n a second, presenting a ×1000 speed-up compared to the previous work, which now\n allows for tractable data-driven metamer experiments.", "output": [ "We introduce a novel feed-forward framework to generate visual metamers", "Proposes a NeuroFovea model for generation of point-of-fixation metamers by using a style transfer approach via and Encoder-Decoder style architecture", "An analysis of metamerism and a model capable of rapidly producing metamers of value for experimental psychophysics and other domains.", "The paper proposes a fast method for generating visual metamers – physically different images that cannot be told apart from an original – via foveated, fast, arbitrary style transfer" ] }, { "id": "task668-bf4ff12059654c91bd9fcca89af6d589", "input": "Spoken term detection (STD) is the task of determining whether and where a given word or phrase appears in a given segment of speech. Algorithms for STD are often aimed at maximizing the gap between the scores of positive and negative examples. As such they are focused on ensuring that utterances where the term appears are ranked higher than utterances where the term does not appear. However, they do not determine a detection threshold between the two. In this paper, we propose a new approach for setting an absolute detection threshold for all terms by introducing a new calibrated loss function. The advantage of minimizing this loss function during training is that it aims at maximizing not only the relative ranking scores, but also adjusts the system to use a fixed threshold and thus enhances system robustness and maximizes the detection accuracy rates. We use the new loss function in the structured prediction setting and extend the discriminative keyword spotting algorithm for learning the spoken term detector with a single threshold for all terms. We further demonstrate the effectiveness of the new loss function by applying it on a deep neural Siamese network in a weakly supervised setting for template-based spoken term detection, again with a single fixed threshold. Experiments with the TIMIT, WSJ and Switchboard corpora showed that our approach not only improved the accuracy rates when a fixed threshold was used but also obtained higher Area Under Curve (AUC).", "output": [ "Spoken Term Detection, using structured prediction and deep networks, implementing a new loss function that both maximizes AUC and ranks according to a predefined threshold." ] }, { "id": "task668-3641f786beac4a9187a17a070203af08", "input": "Network embedding (NE) methods aim to learn low-dimensional representations of network nodes as vectors, typically in Euclidean space. These representations are then used for a variety of downstream prediction tasks. Link prediction is one of the most popular choices for assessing the performance of NE methods. However, the complexity of link prediction requires a carefully designed evaluation pipeline to provide consistent, reproducible and comparable results. We argue this has not been considered sufficiently in recent works. The main goal of this paper is to overcome difficulties associated with evaluation pipelines and reproducibility of results. We introduce EvalNE, an evaluation framework to transparently assess and compare the performance of NE methods on link prediction. EvalNE provides automation and abstraction for tasks such as hyper-parameter tuning, model validation, edge sampling, computation of edge embeddings and model validation. The framework integrates efficient procedures for edge and non-edge sampling and can be used to easily evaluate any off-the-shelf embedding method. The framework is freely available as a Python toolbox. Finally, demonstrating the usefulness of EvalNE in practice, we conduct an empirical study in which we try to replicate and analyse experimental sections of several influential papers.", "output": [ "In this paper we introduce EvalNE, a Python toolbox for automating the evaluation of network embedding methods on link prediction and ensuring the reproducibility of results." ] }, { "id": "task668-ed721c572c104b82ba324566ac2a7848", "input": "Are neural networks biased toward simple functions?\n Does depth always help learn more complex features?\n Is training the last layer of a network as good as training all layers?\n These questions seem unrelated at face value, but in this work we give all of them a common treatment from the spectral perspective.\n We will study the spectra of the *Conjugate Kernel, CK,* (also called the *Neural Network-Gaussian Process Kernel*), and the *Neural Tangent Kernel, NTK*.\n Roughly, the CK and the NTK tell us respectively ``\"what a network looks like at initialization\" and \"``what a network looks like during and after training.\"\n Their spectra then encode valuable information about the initial distribution and the training and generalization properties of neural networks.\n By analyzing the eigenvalues, we lend novel insights into the questions put forth at the beginning, and we verify these insights by extensive experiments of neural networks.\n We believe the computational tools we develop here for analyzing the spectra of CK and NTK serve as a solid foundation for future studies of deep neural networks.\n We have open-sourced the code for it and for generating the plots in this paper at github.com/jxVmnLgedVwv6mNcGCBy/NNspectra.", "output": [ "Eigenvalues of Conjugate (aka NNGP) and Neural Tangent Kernel can be computed in closed form over the Boolean cube and reveal the effects of hyperparameters on neural network inductive bias, training, and generalization.", "This paper gives a spectral analysis on neural networks' conjugate kernel and neural tangent kernel on boolean cube to resolve why deep networks are biased towards simple functions." ] }, { "id": "task668-c9ec414dacd04d0daecdc6c00fc797ec", "input": "Simultaneous machine translation models start generating a target sequence before they have encoded or read the source sequence. Recent approach for this task either apply a fixed policy on transformer, or a learnable monotonic attention on a weaker recurrent neural network based structure. In this paper, we propose a new attention mechanism, Monotonic Multihead Attention (MMA), which introduced the monotonic attention mechanism to multihead attention. We also introduced two novel interpretable approaches for latency control that are specifically designed for multiple attentions. We apply MMA to the simultaneous machine translation task and demonstrate better latency-quality tradeoffs compared to MILk, the previous state-of-the-art approach. Code will be released upon publication.\n", "output": [ "Make the transformer streamable with monotonic attention." ] }, { "id": "task668-909580735b694123ab3c0bb90f0291df", "input": "\\emph{Truncated Backpropagation Through Time} (truncated BPTT, \\cite{jaeger2002tutorial}) is a widespread method for learning recurrent computational graphs. Truncated BPTT keeps the computational benefits of \\emph{Backpropagation Through Time} (BPTT \\cite{werbos:bptt}) while relieving the need for a complete backtrack through the whole data sequence at every step. However, truncation favors short-term dependencies: the gradient estimate of truncated BPTT is biased, so that it does not benefit from the convergence guarantees from stochastic gradient theory. We introduce \\emph{Anticipated Reweighted Truncated Backpropagation} (ARTBP), an algorithm that keeps the computational benefits of truncated BPTT, while providing unbiasedness. ARTBP works by using variable truncation lengths together with carefully chosen compensation factors in the backpropagation equation. We check the viability of ARTBP on two tasks. First, a simple synthetic task where careful balancing of temporal dependencies at different scales is needed: truncated BPTT displays unreliable performance, and in worst case scenarios, divergence, while ARTBP converges reliably. Second, on Penn Treebank character-level language modelling \\cite{ptb_proc}, ARTBP slightly outperforms truncated BPTT.\n", "output": [ "Provides an unbiased version of truncated backpropagation by sampling truncation lengths and reweighting accordingly.", "Proposes stochastic determination methods for truncation points in backpropagation through time.", "A new approximation to backpropagation through time to overcome the computational and memory loads that arise when having to learn from long sequences." ] }, { "id": "task668-d4b311b4a5bf4e168709f56950128643", "input": "Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a novel, systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We identify the atomic building blocks of existing methods, and decouple the assumptions they enforce from the mathematical tools they rely on. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.", "output": [ "Systematic categorization of regularization methods for deep learning, revealing their similarities.", "Attempts to build a taxonomy for regularization techniques employed in deep learning." ] }, { "id": "task668-b038ffe008f940c889ecfd1d262e0af1", "input": "Predictive coding theories suggest that the brain learns by predicting observations at various levels of abstraction. One of the most basic prediction tasks is view prediction: how would a given scene look from an alternative viewpoint? Humans excel at this task. Our ability to imagine and fill in missing visual information is tightly coupled with perception: we feel as if we see the world in 3 dimensions, while in fact, information from only the front surface of the world hits our (2D) retinas. This paper explores the connection between view-predictive representation learning and its role in the development of 3D visual recognition. We propose inverse graphics networks, which take as input 2.5D video streams captured by a moving camera, and map to stable 3D feature maps of the scene, by disentangling the scene content from the motion of the camera. The model can also project its 3D feature maps to novel viewpoints, to predict and match against target views. We propose contrastive prediction losses that can handle stochasticity of the visual input and can scale view-predictive learning to more photorealistic scenes than those considered in previous works. We show that the proposed model learns 3D visual representations useful for (1) semi-supervised learning of 3D object detectors, and (2) unsupervised learning of 3D moving object detectors, by estimating motion of the inferred 3D feature maps in videos of dynamic scenes. To the best of our knowledge, this is the first work that empirically shows view prediction to be a useful and scalable self-supervised task beneficial to 3D object detection. ", "output": [ "We show that with the right loss and architecture, view-predictive learning improves 3D object detection" ] }, { "id": "task668-7f4ee92b02d0480bbdf243659fdc4bc0", "input": "This paper fosters the idea that deep learning methods can be sided to classical\n visual odometry pipelines to improve their accuracy and to produce uncertainty\n models to their estimations. We show that the biases inherent to the visual odom-\n etry process can be faithfully learnt and compensated for, and that a learning ar-\n chitecture associated to a probabilistic loss function can jointly estimate a full\n covariance matrix of the residual errors, defining a heteroscedastic error model.\n Experiments on autonomous driving image sequences and micro aerial vehicles\n camera acquisitions assess the possibility to concurrently improve visual odome-\n try and estimate an error associated to its outputs.", "output": [ "This paper discusses different methods of pairing VO with deep learning and proposes a simultaneous prediction of corrections and uncertainty." ] }, { "id": "task668-a995834a52274033a24bdbb9f139e54b", "input": "As machine learning becomes ubiquitous, deployed systems need to be as accu- rate as they can. As a result, machine learning service providers have a surging need for useful, additional training data that benefits training, without giving up all the details about the trained program. At the same time, data owners would like to trade their data for its value, without having to first give away the data itself be- fore receiving compensation. It is difficult for data providers and model providers to agree on a fair price without first revealing the data or the trained model to the other side. Escrow systems only complicate this further, adding an additional layer of trust required of both parties. Currently, data owners and model owners don’t have a fair pricing system that eliminates the need to trust a third party and training the model on the data, which 1) takes a long time to complete, 2) does not guarantee that useful data is paid valuably and that useless data isn’t, without trusting in the third party with both the model and the data. Existing improve- ments to secure the transaction focus heavily on encrypting or approximating the data, such as training on encrypted data, and variants of federated learning. As powerful as the methods appear to be, we show them to be impractical in our use case with real world assumptions for preserving privacy for the data owners when facing black-box models. Thus, a fair pricing scheme that does not rely on secure data encryption and obfuscation is needed before the exchange of data. This pa- per proposes a novel method for fair pricing using data-model efficacy techniques such as influence functions, model extraction, and model compression methods, thus enabling secure data transactions. We successfully show that without running the data through the model, one can approximate the value of the data; that is, if the data turns out redundant, the pricing is minimal, and if the data leads to proper improvement, its value is properly assessed, without placing strong assumptions on the nature of the model. Future work will be focused on establishing a system with stronger transactional security against adversarial attacks that will reveal details about the model or the data to the other party.", "output": [ "Facing complex, black-box models, encrypting the data is not as usable as approximating the model and using it to price a potential transaction." ] }, { "id": "task668-806a729cd6e24fc0b7572f723157b121", "input": "We point out important problems with the common practice of using the best single model performance for comparing deep learning architectures, and we propose a method that corrects these flaws. Each time a model is trained, one gets a different result due to random factors in the training process, which include random parameter initialization and random data shuffling. Reporting the best single model performance does not appropriately address this stochasticity. We propose a normalized expected best-out-of-n performance (Boo_n) as a way to correct these problems.", "output": [ "We point out important problems with the common practice of using the best single model performance for comparing deep learning architectures, and we propose a method that corrects these flaws." ] }, { "id": "task668-ffbf36dfd9a344b9995600ec22970948", "input": "We present Predicted Variables, an approach to making machine learning (ML) a first class citizen in programming languages.\n There is a growing divide in approaches to building systems: using human experts (e.g. programming) on the one hand, and using behavior learned from data (e.g. ML) on the other hand. PVars aim to make using ML in programming easier by hybridizing the two. We leverage the existing concept of variables and create a new type, a predicted variable. PVars are akin to native variables with one important distinction: PVars determine their value using ML when evaluated. We describe PVars and their interface, how they can be used in programming, and demonstrate the feasibility of our approach on three algorithmic problems: binary search, QuickSort, and caches.\n We show experimentally that PVars are able to improve over the commonly used heuristics and lead to a better performance than the original algorithms.\n As opposed to previous work applying ML to algorithmic problems, PVars have the advantage that they can be used within the existing frameworks and do not require the existing domain knowledge to be replaced. PVars allow for a seamless integration of ML into existing systems and algorithms.\n Our PVars implementation currently relies on standard Reinforcement Learning (RL) methods. To learn faster, PVars use the heuristic function, which they are replacing, as an initial function. We show that PVars quickly pick up the behavior of the initial function and then improve performance beyond that without ever performing substantially worse -- allowing for a safe deployment in critical applications.", "output": [ "We present Predicted Variables, an approach to making machine learning a first class citizen in programming languages." ] }, { "id": "task668-92789248ada84716839cb398165dbb57", "input": "We present Optimal Completion Distillation (OCD), a training procedure for optimizing sequence to sequence models based on edit distance. OCD is efficient, has no hyper-parameters of its own, and does not require pre-training or joint optimization with conditional log-likelihood. Given a partial sequence generated by the model, we first identify the set of optimal suffixes that minimize the total edit distance, using an efficient dynamic programming algorithm. Then, for each position of the generated sequence, we use a target distribution which puts equal probability on the first token of all the optimal suffixes. OCD achieves the state-of-the-art performance on end-to-end speech recognition, on both Wall Street Journal and Librispeech datasets, achieving $9.3\\%$ WER and $4.5\\%$ WER, respectively.", "output": [ "Optimal Completion Distillation (OCD) is a training procedure for optimizing sequence to sequence models based on edit distance which achieves state-of-the-art on end-to-end Speech Recognition tasks.", "Alternative approach to training seq2seq models using a dynamic program to compute optimal continuations of predicted prefixes", "A training algorithm for auto-regressive models that does not require any MLE pre-training and can directly optimize from the sampling.", "The paper considers a shortcoming of sequence to sequence models trained using maximum likelihood estimation and propose an approach based on edit distances and the implicit use of given label sequences during training" ] }, { "id": "task668-978d41d822744c7c84dcc80b4aeaec6b", "input": "The vast majority of natural sensory data is temporally redundant. For instance, video frames or audio samples which are sampled at nearby points in time tend to have similar values. Typically, deep learning algorithms take no advantage of this redundancy to reduce computations. This can be an obscene waste of energy. We present a variant on backpropagation for neural networks in which computation scales with the rate of change of the data - not the rate at which we process the data. We do this by implementing a form of Predictive Coding wherein neurons communicate a combination of their state, and their temporal change in state, and quantize this signal using Sigma-Delta modulation. Intriguingly, this simple communication rule give rise to units that resemble biologically-inspired leaky integrate-and-fire neurons, and to a spike-timing-dependent weight-update similar to Spike-Timing Dependent Plasticity (STDP), a synaptic learning rule observed in the brain. We demonstrate that on MNIST, on a temporal variant of MNIST, and on Youtube-BB, a dataset with videos in the wild, our algorithm performs about as well as a standard deep network trained with backpropagation, despite only communicating discrete values between layers. ", "output": [ "An algorithm for training neural networks efficiently on temporally redundant data.", "The paper describes a neural coding scheme for spike based learning in deep neural networks", "\nThis paper presents a method for spike based learning that aims at reducing the needed computation during learning and testing when classifying temporal redundant data.", "This paper applies a predictive coding version of the Sigma-Delta encoding scheme to reduce a computational load on a deep learning network, combining the three components in a way not seen previously." ] }, { "id": "task668-765d198bfcc54a01be5e3fb4ad70cad4", "input": "In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions. We investigate the viability of two discrete GAN training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST) and demonstrate that SCST shows more stable gradient behavior and improved results over Gumbel ST.", "output": [ "Image captioning as a conditional GAN training with novel architectures, also study two discrete GAN training methods. ", "An improved GAN model for image captioning that proposes a context-aware LSTM captioner, introduces a stronger co-attentive discriminator with better performance, and uses SCST for GAN training." ] }, { "id": "task668-49b690d72aae49fc8ecd1d362af22798", "input": "In many environments only a tiny subset of all states yield high reward. In these cases, few of the interactions with the environment provide a relevant learning signal. Hence, we may want to preferentially train on those high-reward states and the probable trajectories leading to them. \n To this end, we advocate for the use of a \\textit{backtracking model} that predicts the preceding states that terminate at a given high-reward state. We can train a model which, starting from a high value state (or one that is estimated to have high value), predicts and samples which (state, action)-tuples may have led to that high value state. These traces of (state, action) pairs, which we refer to as Recall Traces, sampled from this backtracking model starting from a high value state, are informative as they terminate in good states, and hence we can use these traces to improve a policy. We provide a variational interpretation for this idea and a practical algorithm in which the backtracking model samples from an approximate posterior distribution over trajectories which lead to large rewards. Our method improves the sample efficiency of both on- and off-policy RL algorithms across several environments and tasks. ", "output": [ "A backward model of previous (state, action) given the next state, i.e. P(s_t, a_t | s_{t+1}), can be used to simulate additional trajectories terminating at states of interest! Improves RL learning efficiency." ] }, { "id": "task668-31f82e5451d14e56838afa5b15dfa112", "input": "In a time where neural networks are increasingly adopted in sensitive applications, algorithmic bias has emerged as an issue with moral implications. While there are myriad ways that a system may be compromised by bias, systematically isolating and evaluating existing systems on such scenarios is non-trivial, i.e., bias may be subtle, natural and inherently difficult to quantify. To this end, this paper proposes the first systematic study of benchmarking state-of-the-art neural models against biased scenarios. More concretely, we postulate that the bias annotator problem can be approximated with neural models, i.e., we propose generative models of latent bias to deliberately and unfairly associate latent features to a specific class. All in all, our framework provides a new way for principled quantification and evaluation of models against biased datasets. Consequently, we find that state-of-the-art NLP models (e.g., BERT, RoBERTa, XLNET) are readily compromised by biased data.", "output": [ "We propose a neural bias annotator to benchmark models on their robustness to biased text datasets.", "A method to generate biased datasets for NLP, relying on a conditional adversarially regularized autoencoder (CARA)." ] }, { "id": "task668-8f3b96ebd6654a8ea6e29f477eded5f8", "input": "Learning word representations from large available corpora relies on the distributional hypothesis that words present in similar contexts tend to have similar meanings. Recent work has shown that word representations learnt in this manner lack sentiment information which, fortunately, can be leveraged using external knowledge. Our work addresses the question: can affect lexica improve the word representations learnt from a corpus? In this work, we propose techniques to incorporate affect lexica, which capture fine-grained information about a word's psycholinguistic and emotional orientation, into the training process of Word2Vec SkipGram, Word2Vec CBOW and GloVe methods using a joint learning approach. We use affect scores from Warriner's affect lexicon to regularize the vector representations learnt from an unlabelled corpus. Our proposed method outperforms previously proposed methods on standard tasks for word similarity detection, outlier detection and sentiment detection. We also demonstrate the usefulness of our approach for a new task related to the prediction of formality, frustration and politeness in corporate communication.", "output": [ "Enriching word embeddings with affect information improves their performance on sentiment prediction tasks.", "Proposes to use affect lexica to improve word embeddings to outperform the standard Word2vec and Glove.", "This paper proposes integrating information from a semantic resource quantifying the affect of words into a text-based word embedding algorithm to make language models more reflective of semantic and pragmatic phenomena.", "This paper introduces modifications the word2vec and GloVe loss functions to incorporate affect lexica to facilitate the learning of affect-sensitive word embeddings." ] }, { "id": "task668-f9cea66d5b6a48e5b201d81e04316bde", "input": "Overfitting is an ubiquitous problem in neural network training and usually mitigated using a holdout data set.\n Here we challenge this rationale and investigate criteria for overfitting without using a holdout data set.\n Specifically, we train a model for a fixed number of epochs multiple times with varying fractions of randomized labels and for a range of regularization strengths. \n A properly trained model should not be able to attain an accuracy greater than the fraction of properly labeled data points. Otherwise the model overfits. \n We introduce two criteria for detecting overfitting and one to detect underfitting. We analyze early stopping, the regularization factor, and network depth.\n In safety critical applications we are interested in models and parameter settings which perform well and are not likely to overfit. The methods of this paper allow characterizing and identifying such models.", "output": [ "We introduce and analyze several criteria for detecting overfitting." ] }, { "id": "task668-5c29958d38b748e196948b0c2ee2f435", "input": "We propose a general deep reinforcement learning method and apply it to robot manipulation tasks. Our approach leverages demonstration data to assist a reinforcement learning agent in learning to solve a wide range of tasks, mainly previously unsolved. We train visuomotor policies end-to-end to learn a direct mapping from RGB camera inputs to joint velocities. Our experiments indicate that our reinforcement and imitation approach can solve contact-rich robot manipulation tasks that neither the state-of-the-art reinforcement nor imitation learning method can solve alone. We also illustrate that these policies achieved zero-shot sim2real transfer by training with large visual and dynamics variations.", "output": [ "combine reinforcement learning and imitation learning to solve complex robot manipulation tasks from pixels" ] }, { "id": "task668-a6a7f72eb99749a68cb1d3d955d710de", "input": "Deep neural networks can learn meaningful representations of data. However, these representations are hard to interpret. For example, visualizing a latent layer is generally only possible for at most three dimensions. Neural networks are able to learn and benefit from much higher dimensional representations but these are not visually interpretable because nodes have arbitrary ordering within a layer. Here, we utilize the ability of the human observer to identify patterns in structured representations to visualize higher dimensions. To do so, we propose a class of regularizations we call \\textit{Graph Spectral Regularizations} that impose graph-structure on latent layers. This is achieved by treating activations as signals on a predefined graph and constraining those activations using graph filters, such as low pass and wavelet-like filters. This framework allows for any kind of graph as well as filter to achieve a wide range of structured regularizations depending on the inference needs of the data. First, we show a synthetic example that the graph-structured layer can reveal topological features of the data. Next, we show that a smoothing regularization can impose semantically consistent ordering of nodes when applied to capsule nets. Further, we show that the graph-structured layer, using wavelet-like spatially localized filters, can form localized receptive fields for improved image and biomedical data interpretation. In other words, the mapping between latent layer, neurons and the output space becomes clear due to the localization of the activations. Finally, we show that when structured as a grid, the representations create coherent images that allow for image-processing techniques such as convolutions.", "output": [ "Imposing graph structure on neural network layers for improved visual interpretability.", "A novel regularizer to impose graph structure upon hidden layers of a Neural Network to improve the interpretability of hidden representations.", "Highlights the contribution of graph spectral regularizer to the interpretability of neural networks." ] }, { "id": "task668-176cb16cab7d434b999e3756844695be", "input": "Deep Learning (DL) algorithms based on Generative Adversarial Network (GAN) have demonstrated great potentials in computer vision tasks such as image restoration. Despite the rapid development of image restoration algorithms using DL and GANs, image restoration for specific scenarios, such as medical image enhancement and super-resolved identity recognition, are still facing challenges. How to ensure visually realistic restoration while avoiding hallucination or mode- collapse? How to make sure the visually plausible results do not contain hallucinated features jeopardizing downstream tasks such as pathology identification and subject identification?\n Here we propose to resolve these challenges by coupling the GAN based image restoration framework with another task-specific network. With medical imaging restoration as an example, the proposed model conducts additional pathology recognition/classification task to ensure the preservation of detailed structures that are important to this task. Validated on multiple medical datasets, we demonstrate the proposed method leads to improved deep learning based image restoration while preserving the detailed structure and diagnostic features. Additionally, the trained task network show potentials to achieve super-human level performance in identifying pathology and diagnosis.\n Further validation on super-resolved identity recognition tasks also show that the proposed method can be generalized for diverse image restoration tasks.", "output": [ "Couple the GAN based image restoration framework with another task-specific network to generate realistic image while preserving task-specific features.", "A novel method of Task-GAN of image coupling that couples GAN and a task-specific network, which alleviates to avoid hallucination or mode collapse.", "The authors propose to augment GAN-based image restoration with another task-specific branch, such as classification tasks, for further improvement." ] }, { "id": "task668-d4914804bc4c427aba89928006ca4339", "input": "Deep neural networks trained on a wide range of datasets demonstrate impressive transferability. Deep features appear general in that they are applicable to many datasets and tasks. Such property is in prevalent use in real-world applications. A neural network pretrained on large datasets, such as ImageNet, can significantly boost generalization and accelerate training if fine-tuned to a smaller target dataset. Despite its pervasiveness, few effort has been devoted to uncovering the reason of transferability in deep feature representations. This paper tries to understand transferability from the perspectives of improved generalization, optimization and the feasibility of transferability. We demonstrate that 1) Transferred models tend to find flatter minima, since their weight matrices stay close to the original flat region of pretrained parameters when transferred to a similar target dataset; 2) Transferred representations make the loss landscape more favorable with improved Lipschitzness, which accelerates and stabilizes training substantially. The improvement largely attributes to the fact that the principal component of gradient is suppressed in the pretrained parameters, thus stabilizing the magnitude of gradient in back-propagation. 3) The feasibility of transferability is related to the similarity of both input and label. And a surprising discovery is that the feasibility is also impacted by the training stages in that the transferability first increases during training, and then declines. We further provide a theoretical analysis to verify our observations.", "output": [ "Understand transferability from the perspectives of improved generalization, optimization and the feasibility of transferability." ] }, { "id": "task668-965a4172636b47a88472d53e24795fa8", "input": "We investigate methods to efficiently learn diverse strategies in reinforcement learning for a generative structured prediction problem: query reformulation. In the proposed framework an agent consists of multiple specialized sub-agents and a meta-agent that learns to aggregate the answers from sub-agents to produce a final answer. Sub-agents are trained on disjoint partitions of the training data, while the meta-agent is trained on the full training set. Our method makes learning faster, because it is highly parallelizable, and has better generalization performance than strong baselines, such as\n an ensemble of agents trained on the full data. We evaluate on the tasks of document retrieval and question answering. The\n improved performance seems due to the increased diversity of reformulation strategies. This suggests that multi-agent, hierarchical approaches might play an important role in structured prediction tasks of this kind. However, we also find that it is not obvious how to characterize diversity in this context, and a first attempt based on clustering did not produce good results. Furthermore, reinforcement learning for the reformulation task is hard in high-performance regimes. At best, it only marginally improves over the state of the art, which highlights the complexity of training models in this framework for end-to-end language understanding problems.", "output": [ "We use reinforcement learning for query reformulation on two tasks and surprisingly find that when training multiple agents diversity of the reformulations is more important than specialisation." ] }, { "id": "task668-b552c6c0c5bc44b2a8da045cccaf19de", "input": "Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks. In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings. IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues. Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases. Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.", "output": [ "We introduce IC3Net, a single network which can be used to train agents in cooperative, competitive and mixed scenarios. We also show that agents can learn when to communicate using our model.", "Author proposes a new architecture for multi-agent reinforcement learning that uses several LSTM controllers with tied weights that transmit a continuous vector to each other", "The authors propose an interesting gating scheme allowing agents to communicate in an multi-agent RL setting. " ] }, { "id": "task668-83052b2efc2a4cac9f2263e2c88ec8b3", "input": "Deep neural networks are widely used in various domains, but the prohibitive computational complexity prevents their deployment on mobile devices. Numerous model compression algorithms have been proposed, however, it is often difficult and time-consuming to choose proper hyper-parameters to obtain an efficient compressed model. In this paper, we propose an automated framework for model compression and acceleration, namely PocketFlow. This is an easy-to-use toolkit that integrates a series of model compression algorithms and embeds a hyper-parameter optimization module to automatically search for the optimal combination of hyper-parameters. Furthermore, the compressed model can be converted into the TensorFlow Lite format and easily deployed on mobile devices to speed-up the inference. PocketFlow is now open-source and publicly available at https://github.com/Tencent/PocketFlow.", "output": [ "We propose PocketFlow, an automated framework for model compression and acceleration, to facilitate deep learning models' deployment on mobile devices." ] }, { "id": "task668-7a54ad1fd72f4bb7b7a360ca30177fe5", "input": "It is usually hard for a learning system to predict correctly on the rare events, and there is no exception for segmentation algorithms. Therefore, we hope to build an alarm system to set off alarms when the segmentation result is possibly unsatisfactory. One plausible solution is to project the segmentation results into a low dimensional feature space, and then learn classifiers/regressors in the feature space to predict the qualities of segmentation results. In this paper, we form the feature space using shape feature which is a strong prior information shared among different data, so it is capable to predict the qualities of segmentation results given different segmentation algorithms on different datasets. The shape feature of a segmentation result is captured using the value of loss function when the segmentation result is tested using a Variational Auto-Encoder(VAE). The VAE is trained using only the ground truth masks, therefore the bad segmentation results with bad shapes become the rare events for VAE and will result in large loss value. By utilizing this fact, the VAE is able to detect all kinds of shapes that are out of the distribution of normal shapes in ground truth (GT). Finally, we learn the representation in the one-dimensional feature space to predict the qualities of segmentation results. We evaluate our alarm system on several recent segmentation algorithms for the medical segmentation task. The segmentation algorithms perform differently on different datasets, but our system consistently provides reliable prediction on the qualities of segmentation results.\n", "output": [ "We use VAE to capture the shape feature for automatic segmentation evaluation" ] }, { "id": "task668-800a73715a6d418fa3265e4f58faf612", "input": "This paper aims to address the limitations of mutual information estimators based on variational optimization. By redefining the cost using generalized functions from nonextensive statistical mechanics we raise the upper bound of previous estimators and enable the control of the bias variance trade off. Variational based estimators outperform previous methods especially in high dependence high dimensional scenarios found in machine learning setups. Despite their performance, these estimators either exhibit a high variance or are upper bounded by log(batch size). Our approach inspired by nonextensive statistical mechanics uses different generalizations for the logarithm and the exponential in the partition function. This enables the estimator to capture changes in mutual information over a wider range of dimensions and correlations of the input variables whereas previous estimators saturate them.", "output": [ "Mutual information estimator based nonextensive statistical mechanics", "This paper tries to establish novel variational lower bounds for mutual information by introducing parameter q and defining q-algebra, showing that the lower bounds have smaller variance and achieves high values." ] }, { "id": "task668-8f0d50f7a0a742718a180bf98da18563", "input": "Clustering is the central task in unsupervised learning and data mining. k-means is one of the most widely used clustering algorithms. Unfortunately, it is generally non-trivial to extend k-means to cluster data points beyond Gaussian distribution, particularly, the clusters with non-convex shapes (Beliakov & King, 2006). To this end, we, for the first time, introduce Extreme Value Theory (EVT) to improve the clustering ability of k-means. Particularly, the Euclidean space was transformed into a novel probability space denoted as extreme value space by EVT. We thus propose a novel algorithm called Extreme Value k-means (EV k-means), including GEV k-means and GPD k-means. In addition, we also introduce the tricks to accelerate Euclidean distance computation in improving the computational efficiency of classical k-means. Furthermore, our EV k-means is extended to an online version, i.e., online Extreme Value k-means, in utilizing the Mini Batch k-means to cluster streaming data. Extensive experiments are conducted to validate our EV k-means and online EV k-means on synthetic datasets and real datasets. Experimental results show that our algorithms significantly outperform competitors in most cases.", "output": [ "This paper introduces Extreme Value Theory into k-means to measure similarity and proposes a novel algorithm called Extreme Value k-means for clustering." ] }, { "id": "task668-043f6127bbb6404491ae75122b02c4c0", "input": "We consider the problem of using variational latent-variable models for data compression. For such models to produce a compressed binary sequence, which is the universal data representation in a digital world, the latent representation needs to be subjected to entropy coding. Range coding as an entropy coding technique is optimal, but it can fail catastrophically if the computation of the prior differs even slightly between the sending and the receiving side. Unfortunately, this is a common scenario when floating point math is used and the sender and receiver operate on different hardware or software platforms, as numerical round-off is often platform dependent. We propose using integer networks as a universal solution to this problem, and demonstrate that they enable reliable cross-platform encoding and decoding of images using variational models.", "output": [ "We train variational models with quantized networks for computational determinism. This enables using them for cross-platform data compression." ] }, { "id": "task668-e13566ff6f314aa9acf19113666d82de", "input": "The Hamiltonian formalism plays a central role in classical and quantum physics. Hamiltonians are the main tool for modelling the continuous time evolution of systems with conserved quantities, and they come equipped with many useful properties, like time reversibility and smooth interpolation in time. These properties are important for many machine learning problems - from sequence prediction to reinforcement learning and density modelling - but are not typically provided out of the box by standard tools such as recurrent neural networks. In this paper, we introduce the Hamiltonian Generative Network (HGN), the first approach capable of consistently learning Hamiltonian dynamics from high-dimensional observations (such as images) without restrictive domain assumptions. Once trained, we can use HGN to sample new trajectories, perform rollouts both forward and backward in time, and even speed up or slow down the learned dynamics. We demonstrate how a simple modification of the network architecture turns HGN into a powerful normalising flow model, called Neural Hamiltonian Flow (NHF), that uses Hamiltonian dynamics to model expressive densities. Hence, we hope that our work serves as a first practical demonstration of the value that the Hamiltonian formalism can bring to machine learning. More results and video evaluations are available at: http://tiny.cc/hgn", "output": [ "We introduce a class of generative models that reliably learn Hamiltonian dynamics from high-dimensional observations. The learnt Hamiltonian can be applied to sequence modeling or as a normalising flow." ] }, { "id": "task668-fddd9e9f663049e4afb5a08e4f693fce", "input": "Recent powerful pre-trained language models have achieved remarkable performance on most of the popular datasets for reading comprehension. It is time to introduce more challenging datasets to push the development of this field towards more comprehensive reasoning of text. In this paper, we introduce a new Reading Comprehension dataset requiring logical reasoning (ReClor) extracted from standardized graduate admission examinations. As earlier studies suggest, human-annotated datasets usually contain biases, which are often exploited by models to achieve high accuracy without truly understanding the text. In order to comprehensively evaluate the logical reasoning ability of models on ReClor, we propose to identify biased data points and separate them into EASY set while the rest as HARD set. Empirical results show that the state-of-the-art models have an outstanding ability to capture biases contained in the dataset with high accuracy on EASY set. However, they struggle on HARD set with poor performance near that of random guess, indicating more research is needed to essentially enhance the logical reasoning ability of current models.", "output": [ "We introduce ReClor, a reading comprehension dataset requiring logical reasoning, and find that current state-of-the-art models struggle with real logical reasoning with poor performance near that of random guess." ] }, { "id": "task668-a3bb5b4d4185423893ff4beddb6e583b", "input": "Adversarial training is one of the strongest defenses against adversarial attacks, but it requires adversarial examples to be generated for every mini-batch during optimization. The expense of producing these examples during training often precludes adversarial training from use on complex image datasets. \n In this study, we explore the mechanisms by which adversarial training improves classifier robustness, and show that these mechanisms can be effectively mimicked using simple regularization methods, including label smoothing and logit squeezing. \n Remarkably, using these simple regularization methods in combination with Gaussian noise injection, we are able to achieve strong adversarial robustness -- often exceeding that of adversarial training -- using no adversarial examples.", "output": [ "Achieving strong adversarial robustness comparable to adversarial training without training on adversarial examples" ] }, { "id": "task668-a87ea19b0cfa470ea5de7c35ee7b695d", "input": "Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments. These exploration algorithms have been shown to allow real world robots to acquire skills such as tool use in high-dimensional continuous state and action spaces. However, they have so far assumed that self-generated goals are sampled in a specifically engineered feature space, limiting their autonomy. In this work, we propose an approach using deep representation learning algorithms to learn an adequate goal space. This is a developmental 2-stage approach: first, in a perceptual learning stage, deep learning algorithms use passive raw sensor observations of world changes to learn a corresponding latent space; then goal exploration happens in a second stage by sampling goals in this latent space. We present experiments with a simulated robot arm interacting with an object, and we show that exploration algorithms using such learned representations can closely match, and even sometimes improve, the performance obtained using engineered representations.", "output": [ "We propose a novel Intrinsically Motivated Goal Exploration architecture with unsupervised learning of goal space representations, and evaluate how various implementations enable the discovery of a diversity of policies." ] }, { "id": "task668-4ace0f75ce6d4be3907fbf7d080b29d3", "input": "Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents. This paper proposes to add such inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated. Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.", "output": [ "We introduce a new inductive bias that integrates tree structures in recurrent neural networks.", "This paper proposes ON-LSTM, a new RNN unit that integrates the latent tree structure into recurrent models and that has good results on language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference." ] }, { "id": "task668-dc0e2e71882b43bea815a80b91506e61", "input": "Humans can learn task-agnostic priors from interactive experience and utilize the priors for novel tasks without any finetuning. In this paper, we propose Scoring-Aggregating-Planning (SAP), a framework that can learn task-agnostic semantics and dynamics priors from arbitrary quality interactions as well as the corresponding sparse rewards and then plan on unseen tasks in zero-shot condition. The framework finds a neural score function for local regional state and action pairs that can be aggregated to approximate the quality of a full trajectory; moreover, a dynamics model that is learned with self-supervision can be incorporated for planning. Many of previous works that leverage interactive data for policy learning either need massive on-policy environmental interactions or assume access to expert data while we can achieve a similar goal with pure off-policy imperfect data. Instantiating our framework results in a generalizable policy to unseen tasks. Experiments demonstrate that the proposed method can outperform baseline methods on a wide range of applications including gridworld, robotics tasks and video games.", "output": [ "We learn dense scores and dynamics model as priors from exploration data and use them to induce a good policy in new tasks in zero-shot condition.", "This paper discusses zero shot generalization into new environments, and proposes an approach with results on Grid-World, Super Mario Bros, and 3D Robotics.", "A method aiming to learn task-agnostic priors for zero-shot generalization, with the idea to employ a modeling approach on top of the model-based RL framework." ] }, { "id": "task668-4496f033341a4231947f5c1b778ee93c", "input": "In multiagent systems (MASs), each agent makes individual decisions but all of them contribute globally to the system evolution. Learning in MASs is difficult since each agent's selection of actions must take place in the presence of other co-learning agents. Moreover, the environmental stochasticity and uncertainties increase exponentially with the increase in the number of agents. Previous works borrow various multiagent coordination mechanisms into deep learning architecture to facilitate multiagent coordination. However, none of them explicitly consider action semantics between agents that different actions have different influences on other agents. In this paper, we propose a novel network architecture, named Action Semantics Network (ASN), that explicitly represents such action semantics between agents. ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them. ASN can be easily combined with existing deep reinforcement learning (DRL) algorithms to boost their performance. Experimental results on StarCraft II micromanagement and Neural MMO show ASN significantly improves the performance of state-of-the-art DRL approaches compared with several network architectures.", "output": [ "Our proposed ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them." ] }, { "id": "task668-f5a34da1f7414169a5915e4630cf24bd", "input": "Binarized Neural Networks (BNNs) have recently attracted significant interest due to their computational efficiency. Concurrently, it has been shown that neural networks may be overly sensitive to ``attacks\" -- tiny adversarial changes in the input -- which may be detrimental to their use in safety-critical domains. Designing attack algorithms that effectively fool trained models is a key step towards learning robust neural networks.\n The discrete, non-differentiable nature of BNNs, which distinguishes them from their full-precision counterparts, poses a challenge to gradient-based attacks. In this work, we study the problem of attacking a BNN through the lens of combinatorial and integer optimization. We propose a Mixed Integer Linear Programming (MILP) formulation of the problem. While exact and flexible, the MILP quickly becomes intractable as the network and perturbation space grow. To address this issue, we propose IProp, a decomposition-based algorithm that solves a sequence of much smaller MILP problems. Experimentally, we evaluate both proposed methods against the standard gradient-based attack (PGD) on MNIST and Fashion-MNIST, and show that IProp performs favorably compared to PGD, while scaling beyond the limits of the MILP.", "output": [ "Gradient-based attacks on binarized neural networks are not effective due to the non-differentiability of such networks; Our IPROP algorithm solves this problem using integer optimization", "Proposes a new target propagation style algorithm to generate strong adversarial attacks on binarized neural networks.", "This paper proposed a new attack algorithm based on MILP on binary neural networks.", "This paper presents an algorithm to find adversarial attacks to binary neural networks which iteratively finds desired representations layer by layer from the top to the input and is more efficient than solving the full mixed integer linear programming (MILP) solver." ] }, { "id": "task668-4bc4067a49e543cc88b997e4303526eb", "input": "We propose an interactive classification approach for natural language queries. Instead of classifying given the natural language query only, we ask the user for additional information using a sequence of binary and multiple-choice questions. At each turn, we use a policy controller to decide if to present a question or pro-vide the user the final answer, and select the best question to ask by maximizing the system information gain. Our formulation enables bootstrapping the system without any interaction data, instead relying on non-interactive crowdsourcing an-notation tasks. Our evaluation shows the interaction helps the system increase its accuracy and handle ambiguous queries, while our approach effectively balances the number of questions and the final accuracy.", "output": [ "We propose an interactive approach for classifying natural language queries by asking users for additional information using information gain and a reinforcement learning policy controller." ] }, { "id": "task668-f44df75217844a02934f9eb936361814", "input": "Conducting reinforcement-learning experiments can be a complex and timely process. A full experimental pipeline will typically consist of a simulation of an environment, an implementation of one or many learning algorithms, a variety of additional components designed to facilitate the agent-environment interplay, and any requisite analysis, plotting, and logging thereof. In light of this complexity, this paper introduces simple rl, a new open source library for carrying out reinforcement learning experiments in Python 2 and 3 with a focus on simplicity. The goal of simple_rl is to support seamless, reproducible methods for running reinforcement learning experiments. This paper gives an overview of the core design philosophy of the package, how it differs from existing libraries, and showcases its central features.", "output": [ "This paper introduces and motivates simple_rl, a new open source library for carrying out reinforcement learning experiments in Python 2 and 3 with a focus on simplicity." ] }, { "id": "task668-7cd4954bdce3426cafcb2457e4ca5f9e", "input": "The assumption that data samples are independently identically distributed is the backbone of many learning algorithms. Nevertheless, datasets often exhibit rich structures in practice, and we argue that there exist some unknown orders within the data instances. Aiming to find such orders, we introduce a novel Generative Markov Network (GMN) which we use to extract the order of data instances automatically. Specifically, we assume that the instances are sampled from a Markov chain. Our goal is to learn the transitional operator of the chain as well as the generation order by maximizing the generation probability under all possible data permutations. One of our key ideas is to use neural networks as a soft lookup table for approximating the possibly huge, but discrete transition matrix. This strategy allows us to amortize the space complexity with a single model and make the transitional operator generalizable to unseen instances. To ensure the learned Markov chain is ergodic, we propose a greedy batch-wise permutation scheme that allows fast training. Empirically, we evaluate the learned Markov chain by showing that GMNs are able to discover orders among data instances and also perform comparably well to state-of-the-art methods on the one-shot recognition benchmark task.", "output": [ "Propose to observe implicit orders in datasets in a generative model viewpoint.", "The authors deal with the problem of implicit ordering in a dataset and the challenge of recovering it and propose to learn a distance-metric-free model that assumes a Markov chain as the generative mechanism of the data ", "The paper proposes “Generative Markov Networks” - a deep-learning-based approach to modeling sequences and discovering order in datasets.", "Proposes learning the order of an unordered data sample by learning a Markov chain." ] }, { "id": "task668-c83fd3d81afe4c41a18f8970ccb8e8c8", "input": "Deep neural networks have been recently demonstrated to be vulnerable to backdoor attacks. Specifically, by altering a small set of training examples, an adversary is able to install a backdoor that can be used during inference to fully control the model’s behavior. While the attack is very powerful, it crucially relies on the adversary being able to introduce arbitrary, often clearly mislabeled, inputs to the training set and can thus be detected even by fairly rudimentary data filtering. In this paper, we introduce a new approach to executing backdoor attacks, utilizing adversarial examples and GAN-generated data. The key feature is that the resulting poisoned inputs appear to be consistent with their label and thus seem benign even upon human inspection.", "output": [ "We show how to successfully perform backdoor attacks without changing training labels." ] }, { "id": "task668-383ad659e3294ca9813d31dc44cf8d9f", "input": "Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, learning an appropriate hierarchical decomposition of a domain into subtasks remains a substantial challenge. We present a novel algorithm for subtask discovery, based on the recently introduced multitask linearly-solvable Markov decision process (MLMDP) framework. The MLMDP can perform never-before-seen tasks by representing them as a linear combination of a previously learned basis set of tasks. In this setting, the subtask discovery problem can naturally be posed as finding an optimal low-rank approximation of the set of tasks the agent will face in a domain. We use non-negative matrix factorization to discover this minimal basis set of tasks, and show that the technique learns intuitive decompositions in a variety of domains. Our method has several qualitatively desirable features: it is not limited to learning subtasks with single goal states, instead learning distributed patterns of preferred states; it learns qualitatively different hierarchical decompositions in the same domain depending on the ensemble of tasks the agent will face; and it may be straightforwardly iterated to obtain deeper hierarchical decompositions.", "output": [ "We present a novel algorithm for hierarchical subtask discovery which leverages the multitask linear Markov decision process framework." ] }, { "id": "task668-15b32de1e8e64c27bb35b9279e70aeab", "input": "State-of-the-art results in imitation learning are currently held by adversarial methods that iteratively estimate the divergence between student and expert policies and then minimize this divergence to bring the imitation policy closer to expert behavior. Analogous techniques for imitation learning from observations alone (without expert action labels), however, have not enjoyed the same ubiquitous successes. \n Recent work in adversarial methods for generative models has shown that the measure used to judge the discrepancy between real and synthetic samples is an algorithmic design choice, and that different choices can result in significant differences in model performance. Choices including Wasserstein distance and various $f$-divergences have already been explored in the adversarial networks literature, while more recently the latter class has been investigated for imitation learning. Unfortunately, we find that in practice this existing imitation-learning framework for using $f$-divergences suffers from numerical instabilities stemming from the combination of function approximation and policy-gradient reinforcement learning. In this work, we alleviate these challenges and offer a reparameterization of adversarial imitation learning as $f$-divergence minimization before further extending the framework to handle the problem of imitation from observations only. Empirically, we demonstrate that our design choices for coupling imitation learning and $f$-divergences are critical to recovering successful imitation policies. Moreover, we find that with the appropriate choice of $f$-divergence, we can obtain imitation-from-observation algorithms that outperform baseline approaches and more closely match expert performance in continous-control tasks with low-dimensional observation spaces. With high-dimensional observations, we still observe a significant gap with and without action labels, offering an interesting avenue for future work.", "output": [ "The overall goal of this work is to enable sample-efficient imitation from expert demonstrations, both with and without the provision of expert action labels, through the use of f-divergences." ] }, { "id": "task668-6ad064cc06e24acbb468f41431ff462d", "input": "We address the challenging problem of deep representation learning--the efficient adaption of a pre-trained deep network to different tasks. Specifically, we propose to explore gradient-based features. These features are gradients of the model parameters with respect to a task-specific loss given an input sample. Our key innovation is the design of a linear model that incorporates both gradient features and the activation of the network. We show that our model provides a local linear approximation to a underlying deep model, and discuss important theoretical insight. Moreover, we present an efficient algorithm for the training and inference of our model without computing the actual gradients. Our method is evaluated across a number of representation learning tasks on several datasets and using different network architectures. We demonstrate strong results in all settings. And our results are well-aligned with our theoretical insight.", "output": [ "Given a pre-trained model, we explored the per-sample gradients of the model parameters relative to a task-specific loss, and constructed a linear model that combines gradients of model parameters and the activation of the model.", "This paper proposes to use the gradients of specific layers of convolutional networks as features in a linearized model for transfer learning and fast adaptation." ] }, { "id": "task668-d47aa56f2db24be38c385c71fd7a90ba", "input": "Unsupervised embedding learning aims to extract good representations from data without the use of human-annotated labels. Such techniques are apparently in the limelight because of the challenges in collecting massive-scale labels required for supervised learning. This paper proposes a comprehensive approach, called Super-AND, which is based on the Anchor Neighbourhood Discovery model. Multiple losses defined in Super-AND make similar samples gather even within a low-density space and keep features invariant against augmentation. As a result, our model outperforms existing approaches in various benchmark datasets and achieves an accuracy of 89.2% in CIFAR-10 with the Resnet18 backbone network, a 2.9% gain over the state-of-the-art.", "output": [ "We proposed a comprehensive approach for unsupervised embedding learning on the basis of AND algorithm." ] }, { "id": "task668-a2d12e66b65f4a5595e1ad8fea907e5b", "input": "We propose Pure CapsNets (P-CapsNets) without routing procedures. Specifically, we make three modifications to CapsNets. First, we remove routing procedures from CapsNets based on the observation that the coupling coefficients can be learned implicitly. Second, we replace the convolutional layers in CapsNets to improve efficiency. Third, we package the capsules into rank-3 tensors to further improve efficiency. The experiment shows that P-CapsNets achieve better performance than CapsNets with varied routine procedures by using significantly fewer parameters on MNIST&CIFAR10. The high efficiency of P-CapsNets is even comparable to some deep compressing models. For example, we achieve more than 99% percent accuracy on MNIST by using only 3888 parameters. We visualize the capsules as well as the corresponding correlation matrix to show a possible way of initializing CapsNets in the future. We also explore the adversarial robustness of P-CapsNets compared to CNNs.", "output": [ "Routing procedures are not necessary for CapsNets" ] }, { "id": "task668-9179b2ba748d4da9a8d11a1cf0a298e4", "input": "Unsupervised domain adaptation has received significant attention in recent years. Most of existing works tackle the closed-set scenario, assuming that the source and target domains share the exactly same categories. In practice, nevertheless, a target domain often contains samples of classes unseen in source domain (i.e., unknown class). The extension of domain adaptation from closed-set to such open-set situation is not trivial since the target samples in unknown class are not expected to align with the source. In this paper, we address this problem by augmenting the state-of-the-art domain adaptation technique, Self-Ensembling, with category-agnostic clusters in target domain. Specifically, we present Self-Ensembling with Category-agnostic Clusters (SE-CC) --- a novel architecture that steers domain adaptation with the additional guidance of category-agnostic clusters that are specific to target domain. These clustering information provides domain-specific visual cues, facilitating the generalization of Self-Ensembling for both closed-set and open-set scenarios. Technically, clustering is firstly performed over all the unlabeled target samples to obtain the category-agnostic clusters, which reveal the underlying data space structure peculiar to target domain. A clustering branch is capitalized on to ensure that the learnt representation preserves such underlying structure by matching the estimated assignment distribution over clusters to the inherent cluster distribution for each target sample. Furthermore, SE-CC enhances the learnt representation with mutual information maximization. Extensive experiments are conducted on Office and VisDA datasets for both open-set and closed-set domain adaptation, and superior results are reported when comparing to the state-of-the-art approaches.", "output": [ "We present a new design, i.e., Self-Ensembling with Category-agnostic Clusters, for both closed-set and open-set domain adaptation.", "A new approach to open set domain adaptation, where the source domain categories are contained in the target domain categories in order to filter out outlier categories and enable adaptation within the shared classes." ] }, { "id": "task668-83024a52355a43dda9c4d1a958c05689", "input": "Generative Adversarial Networks (GANs) can achieve state-of-the-art sample quality in generative modelling tasks but suffer from the mode collapse problem. Variational Autoencoders (VAE) on the other hand explicitly maximize a reconstruction-based data log-likelihood forcing it to cover all modes, but suffer from poorer sample quality. Recent works have proposed hybrid VAE-GAN frameworks which integrate a GAN-based synthetic likelihood to the VAE objective to address both the mode collapse and sample quality issues, with limited success. This is because the VAE objective forces a trade-off between the data log-likelihood and divergence to the latent prior. The synthetic likelihood ratio term also shows instability during training. We propose a novel objective with a ``\"Best-of-Many-Samples\" reconstruction cost and a stable direct estimate of the synthetic likelihood. This enables our hybrid VAE-GAN framework to achieve high data log-likelihood and low divergence to the latent prior at the same time and shows significant improvement over both hybrid VAE-GANS and plain GANs in mode coverage and quality.", "output": [ "We propose a new objective for training hybrid VAE-GANs which lead to significant improvement in mode coverage and quality." ] }, { "id": "task668-e2ba232f20414dacb82b1f9a0d884b66", "input": "As distributed approaches to natural language semantics have developed and diversified, embedders for linguistic units larger than words (e.g., sentences) have come to play an increasingly important role. To date, such embedders have been evaluated using benchmark tasks (e.g., GLUE) and linguistic probes. We propose a comparative approach, nearest neighbor overlap (N2O), that quantifies similarity between embedders in a task-agnostic manner. N2O requires only a collection of examples and is simple to understand: two embedders are more similar if, for the same set of inputs, there is greater overlap between the inputs' nearest neighbors. We use N2O to compare 21 sentence embedders and show the effects of different design choices and architectures.", "output": [ "We propose nearest neighbor overlap, a procedure which quantifies similarity between embedders in a task-agnostic manner, and use it to compare 21 sentence embedders." ] }, { "id": "task668-65c4a5062a4b42aaa2e27d349e0b4d08", "input": "Deep neural networks are known to be annotation-hungry. Numerous efforts have been devoted to reducing the annotation cost when learning with deep networks. Two prominent directions include learning with noisy labels and semi-supervised learning by exploiting unlabeled data. In this work, we propose DivideMix, a novel framework for learning with noisy labels by leveraging semi-supervised learning techniques. In particular, DivideMix models the per-sample loss distribution with a mixture model to dynamically divide the training data into a labeled set with clean samples and an unlabeled set with noisy samples, and trains the model on both the labeled and unlabeled data in a semi-supervised manner. To avoid confirmation bias, we simultaneously train two diverged networks where each network uses the dataset division from the other network. During the semi-supervised training phase, we improve the MixMatch strategy by performing label co-refinement and label co-guessing on labeled and unlabeled samples, respectively. Experiments on multiple benchmark datasets demonstrate substantial improvements over state-of-the-art methods. Code is available at https://github.com/LiJunnan1992/DivideMix .", "output": [ "We propose a novel semi-supervised learning approach with SOTA performance on combating learning with noisy labels." ] }, { "id": "task668-90a116b0c7f3448c8ba97a4ece865892", "input": "Genetic algorithms have been widely used in many practical optimization problems.\n Inspired by natural selection, operators, including mutation, crossover\n and selection, provide effective heuristics for search and black-box optimization.\n However, they have not been shown useful for deep reinforcement learning, possibly\n due to the catastrophic consequence of parameter crossovers of neural networks.\n Here, we present Genetic Policy Optimization (GPO), a new genetic algorithm\n for sample-efficient deep policy optimization. GPO uses imitation learning\n for policy crossover in the state space and applies policy gradient methods for mutation.\n Our experiments on MuJoCo tasks show that GPO as a genetic algorithm\n is able to provide superior performance over the state-of-the-art policy gradient\n methods and achieves comparable or higher sample efficiency.", "output": [ "Genetic algorithms based approach for optimizing deep neural network policies", "The authors present an algorithm for training ensembles of policy networks that regularly mixes different policies in the ensemble together.", "This paper proposes a genetic algorithm inspired policy optimization method, which mimics the mutation and the crossover operators over policy networks." ] }, { "id": "task668-c634be1b8ecb455fb24a9ca18c62389e", "input": "This paper revisits the problem of sequence modeling using convolutional \n architectures. Although both convolutional and recurrent architectures have a\nlong history in sequence prediction, the current \"default\" mindset in much of\n the deep learning community is that generic sequence modeling is best handled\n using recurrent networks. The goal of this paper is to question this assumption . \nSpecifically, we consider a simple generic temporal convolution network (TCN ),\nwhich adopts features from modern ConvNet architectures such as a dilations and \n residual connections. We show that on a variety of sequence modeling tasks ,\nincluding many frequently used as benchmarks for evaluating recurrent networks ,\nthe TCN outperforms baseline RNN methods (LSTMs, GRUs, and vanilla RNNs) and\n sometimes even highly specialized approaches. We further show that the\n potential \"infinite memory\" advantage that RNNs have over TCNs is largely\n absent in practice: TCNs indeed exhibit longer effective history sizes than their \n recurrent counterparts. As a whole, we argue that it may be time to (re)consider \n ConvNets as the default \"go to\" architecture for sequence modeling.", "output": [ "We argue that convolutional networks should be considered the default starting point for sequence modeling tasks." ] }, { "id": "task668-2fadf7b72ae74a2384e1c74c8282bcc6", "input": "Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings. We present an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism which selects relevant information for each agent at every timestep. This attention mechanism enables more effective and scalable learning in complex multi-agent environments, when compared to recent approaches. Our approach is applicable not only to cooperative settings with shared rewards, but also individualized reward settings, including adversarial settings, and it makes no assumptions about the action spaces of the agents. As such, it is flexible enough to be applied to most multi-agent learning problems", "output": [ "We propose an approach to learn decentralized policies in multi-agent settings using attention-based critics and demonstrate promising results in environments with complex interactions." ] }, { "id": "task668-5ac2440cff4a4a7a9fa0469ae5e81a6b", "input": "Recently, researchers have discovered that the state-of-the-art object classifiers can be fooled easily by small perturbations in the input unnoticeable to human eyes. It is known that an attacker can generate strong adversarial examples if she knows the classifier parameters . Conversely, a defender can robustify the classifier by retraining if she has the adversarial examples . \nThe cat-and-mouse game nature of attacks and defenses raises the question of the presence of equilibria in the dynamics .\nIn this paper, we present a neural-network based attack class to approximate a larger but intractable class of attacks, and \n formulate the attacker-defender interaction as a zero-sum leader-follower game. We present sensitivity-penalized optimization algorithms to find minimax solutions, which are the best worst-case defenses against whitebox attacks. Advantages of the learning-based attacks and defenses compared to gradient-based attacks and defenses are demonstrated with MNIST and CIFAR-10.", "output": [ "A game-theoretic solution to adversarial attacks and defenses." ] }, { "id": "task668-524aa6f7fe1e408892219246ec097224", "input": "We consider the problem of uncertainty estimation in the context of (non-Bayesian) deep neural classification. In this context, all known methods are based on extracting uncertainty signals from a trained network optimized to solve the classification problem at hand. We demonstrate that such techniques tend to introduce biased estimates for instances whose predictions are supposed to be highly confident. We argue that this deficiency is an artifact of the dynamics of training with SGD-like optimizers, and it has some properties similar to overfitting. Based on this observation, we develop an uncertainty estimation algorithm that selectively estimates the uncertainty of highly confident points, using earlier snapshots of the trained model, before their estimates are jittered (and way before they are ready for actual classification). We present extensive experiments indicating that the proposed algorithm provides uncertainty estimates that are consistently better than all known methods.", "output": [ "We use snapshots from the training process to improve any uncertainty estimation method of a DNN classifier." ] }, { "id": "task668-4492497b890043e8acf51e360f36b56c", "input": "To understand how object vision develops in infancy and childhood, it will be necessary to develop testable computational models. Deep neural networks (DNNs) have proven valuable as models of adult vision, but it is not yet clear if they have any value as models of development. As a first model, we measured learning in a DNN designed to mimic the architecture and representational geometry of the visual system (CORnet). We quantified the development of explicit object representations at each level of this network through training by freezing the convolutional layers and training an additional linear decoding layer. We evaluate decoding accuracy on the whole ImageNet validation set, and also for individual visual classes. CORnet, however, uses supervised training and because infants have only extremely impoverished access to labels they must instead learn in an unsupervised manner. We therefore also measured learning in a state-of-the-art unsupervised network (DeepCluster). CORnet and DeepCluster differ in both supervision and in the convolutional networks at their heart, thus to isolate the effect of supervision, we ran a control experiment in which we trained the convolutional network from DeepCluster (an AlexNet variant) in a supervised manner. We make predictions on how learning should develop across brain regions in infants. In all three networks, we also tested for a relationship in the order in which infants and machines acquire visual classes, and found only evidence for a counter-intuitive relationship. We discuss the potential reasons for this.", "output": [ "Unsupervised networks learn from bottom up; machines and infants acquire visual classes in different orders" ] }, { "id": "task668-5310325f68914fcf8d3075173e787df2", "input": "A distinct commonality between HMMs and RNNs is that they both learn hidden representations for sequential data. In addition, it has been noted that the backward computation of the Baum-Welch algorithm for HMMs is a special case of the back-propagation algorithm used for neural networks (Eisner (2016)). Do these observations suggest that, despite their many apparent differences, HMMs are a special case of RNNs? In this paper, we show that that is indeed the case, and investigate a series of architectural transformations between HMMs and RNNs, both through theoretical derivations and empirical hybridization. In particular, we investigate three key design factors—independence assumptions between the hidden states and the observation, the placement of softmaxes, and the use of non-linearities—in order to pin down their empirical effects. We present a comprehensive empirical study to provide insights into the interplay between expressivity and interpretability in this model family with respect to language modeling and parts-of-speech induction.", "output": [ "Are HMMs a special case of RNNs? We investigate a series of architectural transformations between HMMs and RNNs, both through theoretical derivations and empirical hybridization and provide new insights.", "This paper explores if HMMs are a special case of RNNs using language modeling and POS tagging" ] }, { "id": "task668-4e94d59c11c741f0829938ca1ab312fd", "input": "We introduce a “learning-based” algorithm for the low-rank decomposition problem: given an $n \\times d$ matrix $A$, and a parameter $k$, compute a rank-$k$ matrix $A'$ that minimizes the approximation loss $||A- A'||_F$. The algorithm uses a training set of input matrices in order to optimize its performance. Specifically, some of the most efficient approximate algorithms for computing low-rank approximations proceed by computing a projection $SA$, where $S$ is a sparse random $m \\times n$ “sketching matrix”, and then performing the singular value decomposition of $SA$. We show how to replace the random matrix $S$ with a “learned” matrix of the same sparsity to reduce the error.\n\n Our experiments show that, for multiple types of data sets, a learned sketch matrix can substantially reduce the approximation loss compared to a random matrix $S$, sometimes by one order of magnitude. We also study mixed matrices where only some of the rows are trained and the remaining ones are random, and show that matrices still offer improved performance while retaining worst-case guarantees.", "output": [ "Learning-based algorithms can improve upon the performance of classical algorithms for the low-rank approximation problem while retaining the worst-case guarantee." ] }, { "id": "task668-9917dfacbb2c4f8bbf7ebf71844db826", "input": "The existence of adversarial examples, or intentional mis-predictions constructed from small changes to correctly predicted examples, is one of the most significant challenges in neural network research today. Ironically, many new defenses are based on a simple observation - the adversarial inputs themselves are not robust and small perturbations to the attacking input often recover the desired prediction. While the intuition is somewhat clear, a detailed understanding of this phenomenon is missing from the research literature. This paper presents a comprehensive experimental analysis of when and why perturbation defenses work and potential mechanisms that could explain their effectiveness (or ineffectiveness) in different settings.", "output": [ "We identify a family of defense techniques and show that both deterministic lossy compression and randomized perturbations to the input lead to similar gains in robustness.", "This paper discusses ways of destabilizing a given adversarial attack, what makes adversarial images non-robust, and if it's possible for attackers to use a universal model of perturbations to make their adversarial examples robust against such perturbations.", "The paper studies the robustness of adversarial attacks to transformations of their input." ] }, { "id": "task668-5495b51f92f8424a910004fb7096aaf0", "input": "Recent improvements in large-scale language models have driven progress on automatic generation of syntactically and semantically consistent text for many real-world applications. Many of these advances leverage the availability of large corpora. While training on such corpora encourages the model to understand long-range dependencies in text, it can also result in the models internalizing the social biases present in the corpora. This paper aims to quantify and reduce biases exhibited by language models. Given a conditioning context (e.g. a writing prompt) and a language model, we analyze if (and how) the sentiment of the generated text is affected by changes in values of sensitive attributes (e.g. country names, occupations, genders, etc.) in the conditioning context, a.k.a. counterfactual evaluation. We quantify these biases by adapting individual and group fairness metrics from the fair machine learning literature. Extensive evaluation on two different corpora (news articles and Wikipedia) shows that state-of-the-art Transformer-based language models exhibit biases learned from data. We propose embedding-similarity and sentiment-similarity regularization methods that improve both individual and group fairness metrics without sacrificing perplexity and semantic similarity---a positive step toward development and deployment of fairer language models for real-world applications.", "output": [ "We reduce sentiment biases based on counterfactual evaluation of text generation using language models.", "This paper measures sentiment bias in language models as reflected by text generated by the models, and adds other objective terms to the usual language modeling objective to reduce bias.", "This paper proposes to evaluate bias in pre-trained language models by using a fixed sentiment system and tests several different prefix templates.", "A method based on semantic simiilarity and a method based on sentiment similarity to debias the neural language models trained from large datasets." ] }, { "id": "task668-c2865738bdef4e43b460f87ccb9c291a", "input": "Given the fast development of analysis techniques for NLP and speech\n processing systems, few systematic studies have been conducted to\n compare the strengths and weaknesses of each method. As a step in\n this direction we study the case of representations of phonology in\n neural network models of spoken language. We use two commonly applied\n analytical techniques, diagnostic classifiers and representational\n similarity analysis, to quantify to what extent neural activation\n patterns encode phonemes and phoneme sequences. We manipulate two\n factors that can affect the outcome of analysis. First, we investigate\n the role of learning by comparing neural activations extracted from\n trained versus randomly-initialized models. Second, we examine the\n temporal scope of the activations by probing both local activations\n corresponding to a few milliseconds of the speech signal, and global\n activations pooled over the whole utterance. We conclude that\n reporting analysis results with randomly initialized models is\n crucial, and that global-scope methods tend to yield more consistent\n and interpretable results and we recommend their use as a complement\n to local-scope diagnostic methods.", "output": [ "We study representations of phonology in neural network models of spoken language with several variants of analytical techniques." ] }, { "id": "task668-a6eddae3a953417e864fea110ef3f719", "input": "Recent studies have shown the vulnerability of reinforcement learning (RL) models in noisy settings. The sources of noises differ across scenarios. For instance, in practice, the observed reward channel is often subject to noise (e.g., when observed rewards are collected through sensors), and thus observed rewards may not be credible as a result. Also, in applications such as robotics, a deep reinforcement learning (DRL) algorithm can be manipulated to produce arbitrary errors. In this paper, we consider noisy RL problems where observed rewards by RL agents are generated with a reward confusion matrix. We call such observed rewards as perturbed rewards. We develop an unbiased reward estimator aided robust RL framework that enables RL agents to learn in noisy environments while observing only perturbed rewards. Our framework draws upon approaches for supervised learning with noisy data. The core ideas of our solution include estimating a reward confusion matrix and defining a set of unbiased surrogate rewards. We prove the convergence and sample complexity of our approach. Extensive experiments on different DRL platforms show that policies based on our estimated surrogate reward can achieve higher expected rewards, and converge faster than existing baselines. For instance, the state-of-the-art PPO algorithm is able to obtain 67.5% and 46.7% improvements in average on five Atari games, when the error rates are 10% and 30% respectively.", "output": [ "A new approach for learning with noisy rewards in reinforcement learning" ] }, { "id": "task668-5fa55e79358241a5b180850186b3d95f", "input": "The convergence rate and final performance of common deep learning models have significantly benefited from recently proposed heuristics such as learning rate schedules, knowledge distillation, skip connections and normalization layers. In the absence of theoretical underpinnings, controlled experiments aimed at explaining the efficacy of these strategies can aid our understanding of deep learning landscapes and the training dynamics. Existing approaches for empirical analysis rely on tools of linear interpolation and visualizations with dimensionality reduction, each with their limitations. Instead, we revisit the empirical analysis of heuristics through the lens of recently proposed methods for loss surface and representation analysis, viz. mode connectivity and canonical correlation analysis (CCA), and hypothesize reasons why the heuristics succeed. In particular, we explore knowledge distillation and learning rate heuristics of (cosine) restarts and warmup using mode connectivity and CCA. Our empirical analysis suggests that : (a) the reasons often quoted for the success of cosine annealing are not evidenced in practice ; (b) that the effect of learning rate warmup is to prevent the deeper layers from creating training instability; and (c) that the latent knowledge shared by the teacher is primarily disbursed in the deeper layers.", "output": [ "We use empirical tools of mode connectivity and SVCCA to investigate neural network training heuristics of learning rate restarts, warmup and knowledge distillation." ] }, { "id": "task668-186419565aef472e8eccc3c4c85a9150", "input": "Catastrophic forgetting in neural networks is one of the most well-known problems in continual learning. Previous attempts on addressing the problem focus on preventing important weights from changing. Such methods often require task boundaries to learn effectively and do not support backward transfer learning. In this paper, we propose a meta-learning algorithm which learns to reconstruct the gradients of old tasks w.r.t. the current parameters and combines these reconstructed gradients with the current gradient to enable continual learning and backward transfer learning from the current task to previous tasks. Experiments on standard continual learning benchmarks show that our algorithm can effectively prevent catastrophic forgetting and supports backward transfer learning.\n", "output": [ "We propose a meta learning algorithm for continual learning which can effectively prevent catastrophic forgetting problem and support backward transfer learning." ] }, { "id": "task668-b9c1738581de4ee2aec8aa1cb1df1eff", "input": "Deep neural networks with millions of parameters may suffer from poor generalizations due to overfitting. To mitigate the issue, we propose a new regularization method that penalizes the predictive distribution between similar samples. In particular, we distill the predictive distribution between different samples of the same label and augmented samples of the same source during training. In other words, we regularize the dark knowledge (i.e., the knowledge on wrong predictions) of a single network, i.e., a self-knowledge distillation technique, to force it output more meaningful predictions. We demonstrate the effectiveness of the proposed method via experiments on various image classification tasks: it improves not only the generalization ability, but also the calibration accuracy of modern neural networks.", "output": [ "We propose a new regularization technique based on the knowledge distillation." ] }, { "id": "task668-4f3f783a89da4c8e81421e3bdf992e13", "input": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick\", allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.3 and Frechet Inception Distance (FID) of 9.6, improving over the previous best IS of 52.52 and FID of 18.65.", "output": [ "GANs benefit from scaling up." ] }, { "id": "task668-633f19badae94ea5a12b1a4203a4afda", "input": "This paper extends the proof of density of neural networks in the space of continuous (or even measurable) functions on Euclidean spaces to functions on compact sets of probability measures.\n By doing so the work parallels a more then a decade old results on mean-map embedding of probability measures in reproducing kernel Hilbert spaces. \n The work has wide practical consequences for multi-instance learning, where it theoretically justifies some recently proposed constructions.\n The result is then extended to Cartesian products, yielding universal approximation theorem for tree-structured domains, which naturally occur in data-exchange formats like JSON, XML, YAML, AVRO, and ProtoBuffer. This has important practical implications, as it enables to automatically create an architecture of neural networks for processing structured data (AutoML paradigms), as demonstrated by an accompanied library for JSON format.", "output": [ "This paper extends the proof of density of neural networks in the space of continuous (or even measurable) functions on Euclidean spaces to functions on compact sets of probability measures. ", "This paper investigates the approximation properties of a family of neural networks designed to address multi-instance learning problems, and shows that results for standard one layer architectures extend to these models.", "This paper generalizes the universal approximation theorem to real functions on the space of measures." ] }, { "id": "task668-a2a8f8f30bcc45e4aff36aed6cc48856", "input": "Measuring visual (dis)similarity between two or more instances within a data distribution is a fundamental task in many applications, specially in image retrieval. Theoretically, non-metric distances are able to generate a more complex and accurate similarity model than metric distances, provided that the non-linear data distribution is precisely captured by the similarity model. In this work, we analyze a simple approach for deep learning networks to be used as an approximation of non-metric similarity functions and we study how these models generalize across different image retrieval datasets.", "output": [ "Similarity network to learn a non-metric visual similarity estimation between a pair of images", "The authors propose learning similarity measure for visual similarity and obtain by this an improvement in very well-known datasets of Oxford and Paris for image retrival.", "The paper argues that it is more suitable to use non-metric distances instead of metric distances." ] }, { "id": "task668-50f4acc5dd9c4bdda832734b186ffc84", "input": "The use of deep learning for a wide range of data problems has increased the need for understanding and diagnosing these models, and deep learning interpretation techniques have become an essential tool for data analysts. Although numerous model interpretation methods have been proposed in recent years, most of these procedures are based on heuristics with little or no theoretical guarantees. In this work, we propose a statistical framework for saliency estimation for black box computer vision models. We build a model-agnostic estimation procedure that is statistically consistent and passes the saliency checks of Adebayo et al. (2018). Our method requires solving a linear program, whose solution can be efficiently computed in polynomial time. Through our theoretical analysis, we establish an upper bound on the number of model evaluations needed to recover the region of importance with high probability, and build a new perturbation scheme for estimation of local gradients that is shown to be more efficient than the commonly used random perturbation schemes. Validity of the new method is demonstrated through sensitivity analysis.\n", "output": [ "We propose a statistical framework and a theoretically consistent procedure for saliency estimation." ] }, { "id": "task668-233f0c4204644ea2aeb70538bae55642", "input": "Convolution is an efficient technique to obtain abstract feature representations using hierarchical layers in deep networks. Although performing convolution in Euclidean geometries is fairly straightforward, its extension to other topological spaces---such as a sphere S^2 or a unit ball B^3---entails unique challenges. In this work, we propose a novel `\"volumetric convolution\" operation that can effectively convolve arbitrary functions in B^3. We develop a theoretical framework for \"volumetric convolution\" based on Zernike polynomials and efficiently implement it as a differentiable and an easily pluggable layer for deep networks. Furthermore, our formulation leads to derivation of a novel formula to measure the symmetry of a function in B^3 around an arbitrary axis, that is useful in 3D shape analysis tasks. We demonstrate the efficacy of proposed volumetric convolution operation on a possible use-case i.e., 3D object recognition task.", "output": [ "A novel convolution operator for automatic representation learning inside unit ball", "This work is related to the recent spherical CNN and SE(n) equivariant network papers and extends previous ideas to volumetric data in the unit ball.", "Proposes using volumetric convolutions on convolutions networks in order to learn unit ball and discusses methodology and results of process." ] }, { "id": "task668-733ebd583ccd4579975342553257fa14", "input": "We introduce bio-inspired artificial neural networks consisting of neurons that are additionally characterized by spatial positions. To simulate properties of biological systems we add the costs penalizing long connections and the proximity of neurons in a two-dimensional space. Our experiments show that in the case where the network performs two different tasks, the neurons naturally split into clusters, where each cluster is responsible for processing a different task. This behavior not only corresponds to the biological systems, but also allows for further insight into interpretability or continual learning.", "output": [ "Bio-inspired artificial neural networks, consisting of neurons positioned in a two-dimensional space, are capable of forming independent groups for performing different tasks." ] }, { "id": "task668-725e8a8eaa6f4120a49060fb57de8a9e", "input": "Neural embeddings have been used with great success in Natural Language Processing (NLP) where they provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks. The success of neural embeddings has prompted significant amounts of research into applications in domains other than language. One such domain is graph-structured data, where embeddings of vertices can be learned that encapsulate vertex similarity and improve performance on tasks including edge prediction and vertex labelling. For both NLP and graph-based tasks, embeddings in high-dimensional Euclidean spaces have been learned.\n However, recent work has shown that the appropriate isometric space for embedding complex networks is not the flat Euclidean space, but a negatively curved hyperbolic space. We present a new concept that exploits these recent insights and propose learning neural embeddings of graphs in hyperbolic space. We provide experimental evidence that hyperbolic embeddings significantly outperform Euclidean embeddings on vertex classification tasks for several real-world public datasets.", "output": [ "We learn neural embeddings of graphs in hyperbolic instead of Euclidean space" ] }, { "id": "task668-60a571200a4d4e81a7e18d652c703c79", "input": "Recent approaches have successfully demonstrated the benefits of learning the parameters of shallow networks in hyperbolic space. We extend this line of work by imposing hyperbolic geometry on the embeddings used to compute the ubiquitous attention mechanisms for different neural networks architectures. By only changing the geometry of embedding of object representations, we can use the embedding space more efficiently without increasing the number of parameters of the model. Mainly as the number of objects grows exponentially for any semantic distance from the query, hyperbolic geometry --as opposed to Euclidean geometry-- can encode those objects without having any interference. Our method shows improvements in generalization on neural machine translation on WMT'14 (English to German), learning on graphs (both on synthetic and real-world graph tasks) and visual question answering (CLEVR) tasks while keeping the neural representations compact.", "output": [ "We propose to incorporate inductive biases and operations coming from hyperbolic geometry to improve the attention mechanism of the neural networks.", "This paper replaces the dot-product similarity used in attention mechanisms with the negative hyperbolic distance, and applies it to the existing Transformer model, graph attention networks, and Relation Networks", "The authors propose a novel approach to improve relational-attention by changing the matching and aggregation functions to use hyperbolic geometric. " ] }, { "id": "task668-7b716468c77a41bbad3af41ead1721cc", "input": "Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images. However, their application in the audio domain has received limited attention,\nand autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech. To address this paucity, we introduce GAN-TTS, a Generative Adversarial Network for Text-to-Speech.\n Our architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes. The discriminators analyse the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced. To measure the performance of GAN-TTS, we employ both subjective human evaluation (MOS - Mean Opinion Score), as well as novel quantitative metrics (Fréchet DeepSpeech Distance and Kernel DeepSpeech Distance), which we find to be well correlated with MOS. We show that GAN-TTS is capable of generating high-fidelity speech with naturalness comparable to the state-of-the-art models, and unlike autoregressive models, it is highly parallelisable thanks to an efficient feed-forward generator. Listen to GAN-TTS reading this abstract at http://tiny.cc/gantts.", "output": [ "We introduce GAN-TTS, a Generative Adversarial Network for Text-to-Speech, which achieves Mean Opinion Score (MOS) 4.2.", "Solves the GAN challenge in raw waveform synthesis and begins to close the existing performance gap between autoregressive models and GANs for raw audios." ] }, { "id": "task668-6a57112bfba0472f96949491c7949a56", "input": "We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning. In such hierarchical structures, a higher-level controller solves tasks by iteratively communicating goals which a lower-level policy is trained to reach. Accordingly, the choice of representation -- the mapping of observation space to goal space -- is crucial. To study this problem, we develop a notion of sub-optimality of a representation, defined in terms of expected reward of the optimal hierarchical policy using this representation. We derive expressions which bound the sub-optimality and show how these expressions can be translated to representation learning objectives which may be optimized in practice. Results on a number of difficult continuous-control tasks show that our approach to representation learning yields qualitatively better representations as well as quantitatively better hierarchical policies, compared to existing methods.", "output": [ "We translate a bound on sub-optimality of representations to a practical training objective in the context of hierarchical reinforcement learning.", "The authors proposes a novel approach in learning a representation for HRL and state an intriguing connection between representation learning and bounding the sub-optimality which results in a gradient based algorithm", "This paper proposes a way to handle sub-optimality in the context of learning representations which refer to the sub-optimality of hierarchical polity with respect to the task reward." ] }, { "id": "task668-7401d918a24542558461272e00ae6407", "input": "Learning in recurrent neural networks (RNNs) is most often implemented by gradient descent using backpropagation through time (BPTT), but BPTT does not model accurately how the brain learns. Instead, many experimental results on synaptic plasticity can be summarized as three-factor learning rules involving eligibility traces of the local neural activity and a third factor. We present here eligibility propagation (e-prop), a new factorization of the loss gradients in RNNs that fits the framework of three factor learning rules when derived for biophysical spiking neuron models. When tested on the TIMIT speech recognition benchmark, it is competitive with BPTT both for training artificial LSTM networks and spiking RNNs. Further analysis suggests that the diversity of learning signals and the consideration of slow internal neural dynamics are decisive to the learning efficiency of e-prop.", "output": [ "We present eligibility propagation an alternative to BPTT that is compatible with experimental data on synaptic plasticity and competes with BPTT on machine learning benchmarks." ] }, { "id": "task668-b3dd162b586e4a00a92fba0378dc82db", "input": "Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure. Existing approaches that deal with the higher-order neighbors tend to take advantage of adjacency matrix power. In this paper, we assume a seemly trivial condition that the higher-order neighborhood information may be similar to that of the first-order neighbors. Accordingly, we present an unsupervised approach to describe such similarities and learn the weight matrices of higher-order neighbors automatically through Lasso that minimizes the feature loss between the first-order and higher-order neighbors, based on which we formulate the new convolutional filter for GCN to learn the better node representations. Our model, called higher-order weighted GCN (HWGCN), has achieved the state-of-the-art results on a number of node classification tasks over Cora, Citeseer and Pubmed datasets.", "output": [ "We propose HWGCN to mix the relevant neighborhood information at different orders to better learn node representations.", "The authors propose a variant of GCN, HWGCN, to consider convolution beyond 1-step neighbors, which is comparable to state-of-the-art methods." ] }, { "id": "task668-0d5bb807588c41f7bf1c571b4b23a4f4", "input": "The field of few-shot learning has recently seen substantial advancements. Most of these advancements came from casting few-shot learning as a meta-learning problem.Model Agnostic Meta Learning or MAML is currently one of the best approaches for few-shot learning via meta-learning. MAML is simple, elegant and very powerful, however, it has a variety of issues, such as being very sensitive to neural network architectures, often leading to instability during training, requiring arduous hyperparameter searches to stabilize training and achieve high generalization and being very computationally expensive at both training and inference times. In this paper, we propose various modifications to MAML that not only stabilize the system, but also substantially improve the generalization performance, convergence speed and computational overhead of MAML, which we call MAML++.", "output": [ "MAML is great, but it has many problems, we solve many of those problems and as a result we learn most hyper parameters end to end, speed-up training and inference and set a new SOTA in few-shot learning" ] }, { "id": "task668-352993fde4894407a15d395e9e02cc14", "input": "The neural linear model is a simple adaptive Bayesian linear regression method that has recently been used in a number of problems ranging from Bayesian optimization to reinforcement learning. Despite its apparent successes in these settings, to the best of our knowledge there has been no systematic exploration of its capabilities on simple regression tasks. In this work we characterize these on the UCI datasets, a popular benchmark for Bayesian regression models, as well as on the recently introduced ''gap'' datasets, which are better tests of out-of-distribution uncertainty. We demonstrate that the neural linear model is a simple method that shows competitive performance on these tasks.", "output": [ "We benchmark the neural linear model on the UCI and UCI \"gap\" datasets." ] }, { "id": "task668-53f7d7c898e446dea53ca6743073e47a", "input": "Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes. We argue that, in addition to the primary objective, training also using a complement objective that leverages information from the complement classes can be effective in improving model performance. This motivates us to study a new training paradigm that maximizes the likelihood of the ground-truth class while neutralizing the probabilities of the complement classes. We conduct extensive experiments on multiple tasks ranging from computer vision to natural language understanding. The experimental results confirm that, compared to the conventional training with just one primary objective, training also with the complement objective further improves the performance of the state-of-the-art models across all tasks. In addition to the accuracy improvement, we also show that models trained with both primary and complement objectives are more robust to single-step adversarial attacks.\n", "output": [ "We propose Complement Objective Training (COT), a new training paradigm that optimizes both the primary and complement objectives for effectively learning the parameters of neural networks.", "Considers augmenting the cross-entropy objective with \"complement\" objective maximization, which aims at neutralizing the predicted probabilities of classes other than the ground truth labels.", "The authors propose a secondary objective for softmax minimization based on evaluating the information gathered from the incorrect classes, leading to a new training approach.", "Deals with the training of neural networks for classification or sequence generation tasks using across-entropy loss" ] }, { "id": "task668-d3e3160ef7bb468ead4a3eec48124931", "input": "We describe a novel way of representing a symbolic knowledge base (KB) called a sparse-matrix reified KB. This representation enables neural modules that are fully differentiable, faithful to the original semantics of the KB, expressive enough to model multi-hop inferences, and scalable enough to use with realistically large KBs. The sparse-matrix reified KB can be distributed across multiple GPUs, can scale to tens of millions of entities and facts, and is orders of magnitude faster than naive sparse-matrix implementations. The reified KB enables very simple end-to-end architectures to obtain competitive performance on several benchmarks representing two families of tasks: KB completion, and learning semantic parsers from denotations.", "output": [ "A scalable differentiable neural module that implements reasoning on symbolic KBs." ] }, { "id": "task668-5db3343ed00e4298a1f17ae64766bef3", "input": "Recently, Generative Adversarial Network (GAN) and numbers of its variants have been widely used to solve the image-to-image translation problem and achieved extraordinary results in both a supervised and unsupervised manner. However, most GAN-based methods suffer from the imbalance problem between the generator and discriminator in practice. Namely, the relative model capacities of the generator and discriminator do not match, leading to mode collapse and/or diminished gradients. To tackle this problem, we propose a GuideGAN based on attention mechanism. More specifically, we arm the discriminator with an attention mechanism so not only it estimates the probability that its input is real, but also does it create an attention map that highlights the critical features for such prediction. This attention map then assists the generator to produce more plausible and realistic images. We extensively evaluate the proposed GuideGAN framework on a number of image transfer tasks. Both qualitative results and quantitative comparison demonstrate the superiority of our proposed approach.", "output": [ "A general method that improves the image translation performance of GAN framework by using an attention embedded discriminator", "A feedback mechanism in the GAN framework which improves the quality of generated images in image-to-image translation, and whose discriminator outputs a map indicating where the generator should focus to make its results more convincing.", "Proposal for a GAN with an attention-based discriminator for I2I translation which provides the probability of real/fake and an attention map which reflects salience for image generation." ] }, { "id": "task668-163eab5c5145463dbb1229c0d17071a9", "input": "We derive reverse-mode (or adjoint) automatic differentiation for solutions of stochastic differential equations (SDEs), allowing time-efficient and constant-memory computation of pathwise gradients, a continuous-time analogue of the reparameterization trick.\n Specifically, we construct a backward SDE whose solution is the gradient and provide conditions under which numerical solutions converge.\n We also combine our stochastic adjoint approach with a stochastic variational inference scheme for continuous-time SDE models, allowing us to learn distributions over functions using stochastic gradient descent.\n Our latent SDE model achieves competitive performance compared to existing approaches on time series modeling.\n", "output": [ "We present a constant memory gradient computation procedure through solutions of stochastic differential equations (SDEs) and apply the method for learning latent SDE models." ] }, { "id": "task668-96b43e325b9946ffbe77a9b20c0e6064", "input": "Large-scale pre-trained language model, such as BERT, has recently achieved great success in a wide range of language understanding tasks. However, it remains an open question how to utilize BERT for text generation tasks. In this paper, we present a novel approach to addressing this challenge in a generic sequence-to-sequence (Seq2Seq) setting. We first propose a new task, Conditional Masked Language Modeling (C-MLM), to enable fine-tuning of BERT on target text-generation dataset. The fine-tuned BERT (i.e., teacher) is then exploited as extra supervision to improve conventional Seq2Seq models (i.e., student) for text generation. By leveraging BERT's idiosyncratic bidirectional nature, distilling the knowledge learned from BERT can encourage auto-regressive Seq2Seq models to plan ahead, imposing global sequence-level supervision for coherent text generation. Experiments show that the proposed approach significantly outperforms strong baselines of Transformer on multiple text generation tasks, including machine translation (MT) and text summarization. Our proposed model also achieves new state-of-the-art results on the IWSLT German-English and English-Vietnamese MT datasets.", "output": [ "We propose a model-agnostic way to leverage BERT for text generation and achieve improvements over Transformer on 2 tasks over 4 datasets." ] }, { "id": "task668-d223a944152649879eb84e5d9cc27912", "input": "Deep generative models such as Generative Adversarial Networks (GANs) and\n Variational Auto-Encoders (VAEs) are important tools to capture and investigate\n the properties of complex empirical data. However, the complexity of their inner\n elements makes their functionment challenging to assess and modify. In this\n respect, these architectures behave as black box models. In order to better\n understand the function of such networks, we analyze their modularity based on\n the counterfactual manipulation of their internal variables. Our experiments on the\n generation of human faces with VAEs and GANs support that modularity between\n activation maps distributed over channels of generator architectures is achieved\n to some degree, can be used to better understand how these systems operate and allow meaningful transformations of the generated images without further training.\n erate and edit the content of generated images.", "output": [ "We investigate the modularity of deep generative models.", "The paper provides a way to investigate the modular structure of the deep generative model, with the key concept to distribute over channels of generator architectures." ] }, { "id": "task668-c6c6cbd2e6b14df5b3b538c6b6af2656", "input": "Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, for classifying a node these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood is hard to extend. In this paper, we use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank. We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP. Our model's training time is on par or faster and its number of parameters on par or lower than previous models. It leverages a large, adjustable neighborhood for classification and can be easily combined with any neural network. We show that this model outperforms several recently proposed methods for semi-supervised classification in the most thorough study done so far for GCN-like models. Our implementation is available online.", "output": [ "Personalized propagation of neural predictions (PPNP) improves graph neural networks by separating them into prediction and propagation via personalized PageRank." ] }, { "id": "task668-9bea63136ea24a85870388c7f685d717", "input": "Off-policy learning, the task of evaluating and improving policies using historic data collected from a logging policy, is important because on-policy evaluation is usually expensive and has adverse impacts. One of the major challenge of off-policy learning is to derive counterfactual estimators that also has low variance and thus low generalization error. \n In this work, inspired by learning bounds for importance sampling problems, we present a new counterfactual learning principle for off-policy learning with bandit feedbacks.Our method regularizes the generalization error by minimizing the distribution divergence between the logging policy and the new policy, and removes the need for iterating through all training samples to compute sample variance regularization in prior work. With neural network policies, our end-to-end training algorithms using variational divergence minimization showed significant improvement over conventional baseline algorithms and is also consistent with our theoretical results.", "output": [ "For off-policy learning with bandit feedbacks, we propose a new variance regularized counterfactual learning algorithm, which has both theoretical foundations and superior empirical performance." ] }, { "id": "task668-dbd368a400174db9ad534ed773f8dc86", "input": "This paper we present a defogger, a model that learns to predict future hidden information from partial observations. We formulate this model in the context of forward modeling and leverage spatial and sequential constraints and correlations via convolutional neural networks and long short-term memory networks, respectively. We evaluate our approach on a large dataset of human games of StarCraft: Brood War, a real-time strategy video game. Our models consistently beat strong rule-based baselines and qualitatively produce sensible future game states.", "output": [ "This paper presents a defogger, a model that learns to predict future hidden information from partial observations, applied to a StarCraft dataset." ] }, { "id": "task668-e4a4d5f69f11492facc59e568d8d4831", "input": "In classic papers, Zellner (1988, 2002) demonstrated that Bayesian inference could be derived as the solution to an information theoretic functional. Below we derive a generalized form of this functional as a variational lower bound of a predictive information bottleneck objective. This generalized functional encompasses most modern inference procedures and suggests novel ones.", "output": [ "Rederive a wide class of inference procedures from an global information bottleneck objective." ] }, { "id": "task668-d85bb32792fd41c38c00fc513c339e25", "input": "In this work we present a new agent architecture, called Reactor, which combines multiple algorithmic and architectural contributions to produce an agent with higher sample-efficiency than Prioritized Dueling DQN (Wang et al., 2016) and Categorical DQN (Bellemare et al., 2017), while giving better run-time performance than A3C (Mnih et al., 2016). Our first contribution is a new policy evaluation algorithm called Distributional Retrace, which brings multi-step off-policy updates to the distributional reinforcement learning setting. The same approach can be used to convert several classes of multi-step policy evaluation algorithms designed for expected value evaluation into distributional ones. Next, we introduce the β-leaveone-out policy gradient algorithm which improves the trade-off between variance and bias by using action values as a baseline. Our final algorithmic contribution is a new prioritized replay algorithm for sequences, which exploits the temporal locality of neighboring observations for more efficient replay prioritization. Using the Atari 2600 benchmarks, we show that each of these innovations contribute to both the sample efficiency and final agent performance. Finally, we demonstrate that Reactor reaches state-of-the-art performance after 200 million frames and less than a day of training.", "output": [ "Reactor combines multiple algorithmic and architectural contributions to produce an agent with higher sample-efficiency than Prioritized Dueling DQN while giving better run-time performance than A3C." ] }, { "id": "task668-eccf74c6534548688350e82fb5adf90a", "input": "One of the challenges in training generative models such as the variational auto encoder (VAE) is avoiding posterior collapse. When the generator has too much capacity, it is prone to ignoring latent code. This problem is exacerbated when the dataset is small, and the latent dimension is high. The root of the problem is the ELBO objective, specifically the Kullback–Leibler (KL) divergence term in objective function. This paper proposes a new objective function to replace the KL term with one that emulates the maximum mean discrepancy (MMD) objective. It also introduces a new technique, named latent clipping, that is used to control distance between samples in latent space. A probabilistic autoencoder model, named $\\mu$-VAE, is designed and trained on MNIST and MNIST Fashion datasets, using the new objective function and is shown to outperform models trained with ELBO and $\\beta$-VAE objective. The $\\mu$-VAE is less prone to posterior collapse, and can generate reconstructions and new samples in good quality. Latent representations learned by $\\mu$-VAE are shown to be good and can be used for downstream tasks such as classification. ", "output": [ "This paper proposes a new objective function to replace KL term with one that emulates maximum mean discrepancy (MMD) objective. " ] }, { "id": "task668-a60378784da9494faa9d589c5bbcc3ab", "input": "Numerous models for grounded language understanding have been recently proposed, including (i) generic models that can be easily adapted to any given task and (ii) intuitively appealing modular models that require background knowledge to be instantiated. We compare both types of models in how much they lend themselves to a particular form of systematic generalization. Using a synthetic VQA test, we evaluate which models are capable of reasoning about all possible object pairs after training on only a small subset of them. Our findings show that the generalization of modular models is much more systematic and that it is highly sensitive to the module layout, i.e. to how exactly the modules are connected. We furthermore investigate if modular models that generalize well could be made more end-to-end by learning their layout and parametrization. We find that end-to-end methods from prior work often learn inappropriate layouts or parametrizations that do not facilitate systematic generalization. Our results suggest that, in addition to modularity, systematic generalization in language understanding may require explicit regularizers or priors.\n", "output": [ "We show that modular structured models are the best in terms of systematic generalization and that their end-to-end versions don't generalize as well.", "This paper evaluates systemic generalization between modular neural networks and otherwise generic models via introduction of a new, spatial reasoning dataset", "A targeted empirical evaluation of generalization in models for visual reasoning, focused on the problem of recognizing (object, relation, object) triples in synthetic scenes featuring letters and numbers." ] }, { "id": "task668-e1a0de27ba01488081efdd4f06562786", "input": "Long Short-Term Memory (LSTM) units have the ability to memorise and use long-term dependencies between inputs to generate predictions on time series data. We introduce the concept of modifying the cell state (memory) of LSTMs using rotation matrices parametrised by a new set of trainable weights. This addition shows significant increases of performance on some of the tasks from the bAbI dataset.", "output": [ "Adding a new set of weights to the LSTM that rotate the cell memory improves performance on some bAbI tasks." ] }, { "id": "task668-4de875767f024a0a89e19b3928801794", "input": "The effectiveness of Convolutional Neural Networks stems in large part from their ability to exploit the translation invariance that is inherent in many learning problems. Recently, it was shown that CNNs can exploit other invariances, such as rotation invariance, by using group convolutions instead of planar convolutions. However, for reasons of performance and ease of implementation, it has been necessary to limit the group convolution to transformations that can be applied to the filters without interpolation. Thus, for images with square pixels, only integer translations, rotations by multiples of 90 degrees, and reflections are admissible.\n\n Whereas the square tiling provides a 4-fold rotational symmetry, a hexagonal tiling of the plane has a 6-fold rotational symmetry. In this paper we show how one can efficiently implement planar convolution and group convolution over hexagonal lattices, by re-using existing highly optimized convolution routines. We find that, due to the reduced anisotropy of hexagonal filters, planar HexaConv provides better accuracy than planar convolution with square filters, given a fixed parameter budget. Furthermore, we find that the increased degree of symmetry of the hexagonal grid increases the effectiveness of group convolutions, by allowing for more parameter sharing. We show that our method significantly outperforms conventional CNNs on the AID aerial scene classification dataset, even outperforming ImageNet pre-trained models.", "output": [ "We introduce G-HexaConv, a group equivariant convolutional neural network on hexagonal lattices." ] }, { "id": "task668-579c9c0e897a4afd80b377d31281dd66", "input": "Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose \"Active Neural Localizer\", a fully differentiable neural network that learns to localize efficiently. The proposed model incorporates ideas of traditional filtering-based localization methods, by using a structured belief of the state with multiplicative interactions to propagate belief, and combines it with a policy model to minimize the number of steps required for localization. Active Neural Localizer is trained end-to-end with reinforcement learning. We use a variety of simulation environments for our experiments which include random 2D mazes, random mazes in the Doom game engine and a photo-realistic environment in the Unreal game engine. The results on the 2D environments show the effectiveness of the learned policy in an idealistic setting while results on the 3D environments demonstrate the model's capability of learning the policy and perceptual model jointly from raw-pixel based RGB observations. We also show that a model trained on random textures in the Doom environment generalizes well to a photo-realistic office space environment in the Unreal engine.", "output": [ "\"Active Neural Localizer\", a fully differentiable neural network that learns to localize efficiently using deep reinforcement learning.", "This paper formulates the problem of localisation on a known map using a belief network as an RL problem where the agent's goal is to minimise the number of steps to localise itself.", "This is a clear and interesting paper that builds a parameterized network to select actions for a robot in a simulated environment" ] }, { "id": "task668-40b0faf239b246ddbbe16c3997f9d916", "input": "Standard image captioning tasks such as COCO and Flickr30k are factual, neutral in tone and (to a human) state the obvious (e.g., “a man playing a guitar”). While such tasks are useful to verify that a machine understands the content of an image, they are not engaging to humans as captions. With this in mind we define a new task, Personality-Captions, where the goal is to be as engaging to humans as possible by incorporating controllable style and personality traits. We collect and release a large dataset of 201,858 of such captions conditioned over 215 possible traits. We build models that combine existing work from (i) sentence representations (Mazaré et al., 2018) with Transformers trained on 1.7 billion dialogue examples; and (ii) image representations (Mahajan et al., 2018) with ResNets trained on 3.5 billion social media images. We obtain state-of-the-art performance on Flickr30k and COCO, and strong performance on our new task. Finally, online evaluations validate that our task and models are engaging to humans, with our best model close to human performance.", "output": [ "We develop engaging image captioning models conditioned on personality that are also state of the art on regular captioning tasks." ] }, { "id": "task668-7b9434b5142b417592442f185f6360d7", "input": "Most distributed machine learning (ML) systems store a copy of the model parameters locally on each machine to minimize network communication. In practice, in order to reduce synchronization waiting time, these copies of the model are not necessarily updated in lock-step, and can become stale. Despite much development in large-scale ML, the effect of staleness on the learning efficiency is inconclusive, mainly because it is challenging to control or monitor the staleness in complex distributed environments. In this work, we study the convergence behaviors of a wide array of ML models and algorithms under delayed updates. Our extensive experiments reveal the rich diversity of the effects of staleness on the convergence of ML algorithms and offer insights into seemingly contradictory reports in the literature. The empirical findings also inspire a new convergence analysis of SGD in non-convex optimization under staleness, matching the best-known convergence rate of O(1/\\sqrt{T}).", "output": [ "Empirical and theoretical study of the effects of staleness in non-synchronous execution on machine learning algorithms." ] }, { "id": "task668-4d701643f76f495cb56113860f4a4e49", "input": "Unsupervised learning of timeseries data is a challenging problem in machine learning. Here, \nwe propose a novel algorithm, Deep Temporal Clustering (DTC), a fully unsupervised method, to naturally integrate dimensionality reduction and temporal clustering into a single end to end learning framework. The algorithm starts with an initial cluster estimates using an autoencoder for dimensionality reduction and a novel temporal clustering layer for cluster assignment. Then it jointly optimizes the clustering objective and the dimensionality reduction objective. Based on requirement and application, the temporal clustering layer can be customized with any temporal similarity metric. Several similarity metrics are considered and compared. To gain insight into features that the network has learned for its clustering, we apply a visualization method that generates a heat map of regions of interest in the timeseries. The viability of the algorithm is demonstrated using timeseries data from diverse domains, ranging from earthquakes to sensor data from spacecraft. In each case, we show that our algorithm outperforms traditional methods. This performance is attributed to fully integrated temporal dimensionality reduction and clustering criterion.", "output": [ "A fully unsupervised method, to naturally integrate dimensionality reduction and temporal clustering into a single end to end learning framework.", "Proposes an algorithm that integrates autoencoder with time-series data clustering using a network structure that suits time-series data.", "An algorithm for jointly performing dimensionality reduction and temporal clustering in a deep learning context, utilizing an autoencoder and clustering objective.", "The authors proposed an unsupervised time series clustering methods built with deep neural networks and equipped with an encoder-decoder and a clustering mode to shorten the time series, extract local temporal features, and to get the encoded representations." ] }, { "id": "task668-0bba8ce9c6ec4d6586f97ec918bc1bab", "input": "This paper concerns dictionary learning, i.e., sparse coding, a fundamental representation learning problem. We show that a subgradient descent algorithm, with random initialization, can recover orthogonal dictionaries on a natural nonsmooth, nonconvex L1 minimization formulation of the problem, under mild statistical assumption on the data. This is in contrast to previous provable methods that require either expensive computation or delicate initialization schemes. Our analysis develops several tools for characterizing landscapes of nonsmooth functions, which might be of independent interest for provable training of deep networks with nonsmooth activations (e.g., ReLU), among other applications. Preliminary synthetic and real experiments corroborate our analysis and show that our algorithm works well empirically in recovering orthogonal dictionaries.", "output": [ "Efficient dictionary learning by L1 minimization via a novel analysis of the non-convex non-smooth geometry." ] }, { "id": "task668-55efb245b42f48c886905cfadc0e74e8", "input": "In order to alleviate the notorious mode collapse phenomenon in generative adversarial networks (GANs), we propose a novel training method of GANs in which certain fake samples can be reconsidered as real ones during the training process. This strategy can reduce the gradient value that generator receives in the region where gradient exploding happens. We show that the theoretical equilibrium between the generators and discriminations actually can be seldom realized in practice. And this results in an unbalanced generated distribution that deviates from the target one, when fake datepoints overfit to real ones, which explains the non-stability of GANs. We also prove that, by penalizing the difference between discriminator outputs and considering certain fake datapoints as real for adjacent real and fake sample pairs, gradient exploding can be alleviated. Accordingly, a modified GAN training method is proposed with a more stable training process and a better generalization. Experiments on different datasets verify our theoretical analysis.", "output": [ " We propose a novel GAN training method by considering certain fake samples as real to alleviate mode collapse and stabilize training process." ] }, { "id": "task668-b903cf3c2c45437f912b688f861957c6", "input": "In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real. The generator is trained to increase the probability that fake data is real. We argue that it should also simultaneously decrease the probability that real data is real because 1) this would account for a priori knowledge that half of the data in the mini-batch is fake, 2) this would be observed with divergence minimization, and 3) in optimal settings, SGAN would be equivalent to integral probability metric (IPM) GANs. \n\n We show that this property can be induced by using a relativistic discriminator which estimate the probability that the given real data is more realistic than a randomly sampled fake data. We also present a variant in which the discriminator estimate the probability that the given real data is more realistic than fake data, on average. We generalize both approaches to non-standard GAN loss functions and we refer to them respectively as Relativistic GANs (RGANs) and Relativistic average GANs (RaGANs). We show that IPM-based GANs are a subset of RGANs which use the identity function. \n\n Empirically, we observe that 1) RGANs and RaGANs are significantly more stable and generate higher quality data samples than their non-relativistic counterparts, 2) Standard RaGAN with gradient penalty generate data of better quality than WGAN-GP while only requiring a single discriminator update per generator update (reducing the time taken for reaching the state-of-the-art by 400%), and 3) RaGANs are able to generate plausible high resolutions images (256x256) from a very small sample (N=2011), while GAN and LSGAN cannot; these images are of significantly better quality than the ones generated by WGAN-GP and SGAN with spectral normalization.\n\n The code is freely available on https://github.com/AlexiaJM/RelativisticGAN.", "output": [ "Improving the quality and stability of GANs using a relativistic discriminator; IPM GANs (such as WGAN-GP) are a special case.", "The paper proposes a “relativistic discriminator”, whic helps in some settings, although a bit sensitive to hyperparameters, architectures, and datasets.", "In this work, the authors considers a variation of GAN by simultaneously decreasing the probability that real data is real for the generator." ] }, { "id": "task668-06a8e79c8eee4ecdaa841cdd0acc198a", "input": "Holistically exploring the perceptual and neural representations underlying animal communication has traditionally been very difficult because of the complexity of the underlying signal. We present here a novel set of techniques to project entire communicative repertoires into low dimensional spaces that can be systematically sampled from, exploring the relationship between perceptual representations, neural representations, and the latent representational spaces learned by machine learning algorithms. We showcase this method in one ongoing experiment studying sequential and temporal maintenance of context in songbird neural and perceptual representations of syllables. We further discuss how studying the neural mechanisms underlying the maintenance of the long-range information content present in birdsong can inform and be informed by machine sequence modeling.", "output": [ "We compare perceptual, neural, and modeled representations of animal communication using machine learning, behavior, and physiology. " ] }, { "id": "task668-1fa1c0d430e04196921ab1f4815774ba", "input": "Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and interpret the learned mechanisms that contribute to such effective behaviors and more critically, failure modes. In this work, we present a general method for visualizing an arbitrary neural network's inner mechanisms and their power and limitations. Our dataset-centric method produces visualizations of how a trained network attends to components of its inputs. The computed \"attention masks\" support improved interpretability by highlighting which input attributes are critical in determining output. We demonstrate the effectiveness of our framework on a variety of deep neural network architectures in domains from computer vision and natural language processing. The primary contribution of our approach is an interpretable visualization of attention that provides unique insights into the network's underlying decision-making process irrespective of the data modality.", "output": [ "We develop a technique to visualize attention mechanisms in arbitrary neural networks. ", "Proposes to learn a Latent Attention Network that can help to visualize the inner structure of a deep neural network.", "The authors of this paper propose a data-driven black-box visualization scheme. " ] }, { "id": "task668-d4d9dca583f640e983e79232dbf864e6", "input": "Standard deep learning systems require thousands or millions of examples to learn a concept, and cannot integrate new concepts easily. By contrast, humans have an incredible ability to do one-shot or few-shot learning. For instance, from just hearing a word used in a sentence, humans can infer a great deal about it, by leveraging what the syntax and semantics of the surrounding words tells us. Here, we draw inspiration from this to highlight a simple technique by which deep recurrent networks can similarly exploit their prior knowledge to learn a useful representation for a new word from little data. This could make natural language processing systems much more flexible, by allowing them to learn continually from the new words they encounter.", "output": [ "We highlight a technique by which natural language processing systems can learn a new word from context, allowing them to be much more flexible.", "A technique for exploiting prior knowledge to learn embedding representations for new words with minimal data." ] }, { "id": "task668-ba4ca89f6651426bbc8a6b95ebe49c5c", "input": "Convolutional neural networks (CNNs) are inherently equivariant to translation. Efforts to embed other forms of equivariance have concentrated solely on rotation. We expand the notion of equivariance in CNNs through the Polar Transformer Network (PTN). PTN combines ideas from the Spatial Transformer Network (STN) and canonical coordinate representations. The result is a network invariant to translation and equivariant to both rotation and scale. PTN is trained end-to-end and composed of three distinct stages: a polar origin predictor, the newly introduced polar transformer module and a classifier. PTN achieves state-of-the-art on rotated MNIST and the newly introduced SIM2MNIST dataset, an MNIST variation obtained by adding clutter and perturbing digits with translation, rotation and scaling. The ideas of PTN are extensible to 3D which we demonstrate through the Cylindrical Transformer Network.", "output": [ "We learn feature maps invariant to translation, and equivariant to rotation and scale." ] }, { "id": "task668-324e410452c34b61aff96bd8aa11720d", "input": "Much of the focus in the design of deep neural networks had been on improving accuracy, leading to more powerful yet highly complex network architectures that are difficult to deploy in practical scenarios. As a result, there has been a recent interest in the design of quantitative metrics for evaluating deep neural networks that accounts for more than just model accuracy as the sole indicator of network performance. In this study, we continue the conversation towards universal metrics for evaluating the performance of deep neural networks for practical on-device edge usage by introducing NetScore, a new metric designed specifically to provide a quantitative assessment of the balance between accuracy, computational complexity, and network architecture complexity of a deep neural network. In what is one of the largest comparative analysis between deep neural networks in literature, the NetScore metric, the top-1 accuracy metric, and the popular information density metric were compared across a diverse set of 60 different deep convolutional neural networks for image classification on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) dataset. The evaluation results across these three metrics for this diverse set of networks are presented in this study to act as a reference guide for practitioners in the field. ", "output": [ "We introduce NetScore, new metric designed to provide a quantitative assessment of the balance between accuracy, computational complexity, and network architecture complexity of a deep neural network." ] }, { "id": "task668-d96f19b6e4ed4fa8ae2a925101a165ba", "input": "While modern generative models are able to synthesize high-fidelity, visually appealing images, successfully generating examples that are useful for recognition tasks remains an elusive goal. To this end, our key insight is that the examples should be synthesized to recover classifier decision boundaries that would be learned from a large amount of real examples. More concretely, we treat a classifier trained on synthetic examples as ''student'' and a classifier trained on real examples as ''teacher''. By introducing knowledge distillation into a meta-learning framework, we encourage the generative model to produce examples in a way that enables the student classifier to mimic the behavior of the teacher. To mitigate the potential gap between student and teacher classifiers, we further propose to distill the knowledge in a progressive manner, either by gradually strengthening the teacher or weakening the student. We demonstrate the use of our model-agnostic distillation approach to deal with data scarcity, significantly improving few-shot learning performance on miniImageNet and ImageNet1K benchmarks.", "output": [ "This paper introduces progressive knowledge distillation for learning generative models that are recognition task oriented", "This paper demonstrates easy-to-hard curriculum learning to train a generative model to improve few-shot classification." ] }, { "id": "task668-496a204fd61e461eb0e0237f970e1403", "input": "Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance. At the same time, deep learning ensembles have provided state-of-the-art results in uncertainty estimation. In this work, we focus on in-domain uncertainty for image classification. We explore the standards for its quantification and point out pitfalls of existing metrics. Avoiding these pitfalls, we perform a broad study of different ensembling techniques. To provide more insight in the broad comparison, we introduce the deep ensemble equivalent (DEE) and show that many sophisticated ensembling techniques are equivalent to an ensemble of very few independently trained networks in terms of the test log-likelihood.", "output": [ "We highlight the problems with common metrics of in-domain uncertainty and perform a broad study of modern ensembling techniques." ] }, { "id": "task668-4f9a261435a44d91994820789e8811a2", "input": "Pruning neural networks for wiring length efficiency is considered. Three techniques are proposed and experimentally tested: distance-based regularization, nested-rank pruning, and layer-by-layer bipartite matching. The first two algorithms are used in the training and pruning phases, respectively, and the third is used in the arranging neurons phase. Experiments show that distance-based regularization with weight based pruning tends to perform the best, with or without layer-by-layer bipartite matching. These results suggest that these techniques may be useful in creating neural networks for implementation in widely deployed specialized circuits.", "output": [ "Three new algorithms with ablation studies to prune neural network to optimize for wiring length, as opposed to number of remaining weights." ] }, { "id": "task668-ce46ad0a839040feb6857bf9cb6bef2e", "input": "Deep neural networks are susceptible to adversarial attacks. In computer vision, well-crafted perturbations to images can cause neural networks to make mistakes such as confusing a cat with a computer. Previous adversarial attacks have been designed to degrade performance of models or cause machine learning models to produce specific outputs chosen ahead of time by the attacker. We introduce attacks that instead reprogram the target model to perform a task chosen by the attacker without the attacker needing to specify or compute the desired output for each test-time input. This attack finds a single adversarial perturbation, that can be added to all test-time inputs to a machine learning model in order to cause the model to perform a task chosen by the adversary—even if the model was not trained to do this task. These perturbations can thus be considered a program for the new task. We demonstrate adversarial reprogramming on six ImageNet classification models, repurposing these models to perform a counting task, as well as classification tasks: classification of MNIST and CIFAR-10 examples presented as inputs to the ImageNet model.", "output": [ "We introduce the first instance of adversarial attacks that reprogram the target model to perform a task chosen by the attacker---without the attacker needing to specify or compute the desired output for each test-time input.", "The authors present a novel adversarial attack scheme where a neural net is repurposed to accomplish a different task than the one it was originally trained on", "This paper proposed \"adversarial reprogramming\" of well-trained and fixed neural networks and show that adversarial reprogramming is less effective on untrained networks.", "The paper extends the idea of 'adversarial attacks' in supervised learning of NNs to a full repurposing of the solution of a trained net." ] }, { "id": "task668-38a05ea552b34d2780dd2e82f64212bf", "input": "We present Value Propagation (VProp), a set of parameter-efficient differentiable planning modules built on Value Iteration which can successfully be trained using reinforcement learning to solve unseen tasks, has the capability to generalize to larger map sizes, and can learn to navigate in dynamic environments. We show that the modules enable learning to plan when the environment also includes stochastic elements, providing a cost-efficient learning system to build low-level size-invariant planners for a variety of interactive navigation problems. We evaluate on static and dynamic configurations of MazeBase grid-worlds, with randomly generated environments of several different sizes, and on a StarCraft navigation scenario, with more complex dynamics, and pixels as input.", "output": [ "We present planners based on convnets that are sample-efficient and that generalize to larger instances of navigation and pathfinding problems.", "Proposes methods, which can be seen as modifications of Value Iteration Networks (VIN), with some improvements aimed at improving sample efficiency and generalization to large environment sizes.", "The paper presents an extension of the original value iteration networks (VIN) by considering a state-dependent transition function." ] }, { "id": "task668-470ae48808854b3389487d25195fa237", "input": "Deep Learning for Computer Vision depends mainly on the source of supervision. Photo-realistic simulators can generate large-scale automatically labeled synthetic data, but introduce a domain gap negatively impacting performance. We propose a new unsupervised domain adaptation algorithm, called SPIGAN, relying on Simulator Privileged Information (PI) and Generative Adversarial Networks (GAN). We use internal data from the simulator as PI during the training of a target task network. We experimentally evaluate our approach on semantic segmentation. We train the networks on real-world Cityscapes and Vistas datasets, using only unlabeled real-world images and synthetic labeled data with z-buffer (depth) PI from the SYNTHIA dataset. Our method improves over no adaptation and state-of-the-art unsupervised domain adaptation techniques.", "output": [ "An unsupervised sim-to-real domain adaptation method for semantic segmentation using privileged information from a simulator with GAN-based image translation." ] }, { "id": "task668-d5644f7fdae4472cbdc115952d78ecc4", "input": "We introduce the masked translation model (MTM) which combines encoding and decoding of sequences within the same model component. The MTM is based on the idea of masked language modeling and supports both autoregressive and non-autoregressive decoding strategies by simply changing the order of masking. In experiments on the WMT 2016 Romanian-English task, the MTM shows strong constant-time translation performance, beating all related approaches with comparable complexity. We also extensively compare various decoding strategies supported by the MTM, as well as several length modeling techniques and training settings.", "output": [ "We use a transformer encoder to do translation by training it in the style of a masked translation model." ] }, { "id": "task668-fab761bc58734093b5569c5e74b41e22", "input": "To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights. One common technique for quantizing neural networks is the straight-through gradient method, which enables back-propagation through the quantization mapping. Despite its empirical success, little is understood about why the straight-through gradient method works.\n Building upon a novel observation that the straight-through gradient method is in fact identical to the well-known Nesterov’s dual-averaging algorithm on a quantization constrained optimization problem, we propose a more principled alternative approach, called ProxQuant , that formulates quantized network training as a regularized learning problem instead and optimizes it via the prox-gradient method. ProxQuant does back-propagation on the underlying full-precision vector and applies an efficient prox-operator in between stochastic gradient steps to encourage quantizedness. For quantizing ResNets and LSTMs, ProxQuant outperforms state-of-the-art results on binary quantization and is on par with state-of-the-art on multi-bit quantization. We further perform theoretical analyses showing that ProxQuant converges to stationary points under mild smoothness assumptions, whereas variants such as lazy prox-gradient method can fail to converge in the same setting.", "output": [ "A principled framework for model quantization using the proximal gradient method, with empirical evaluation and theoretical convergence analyses.", "Proposes ProxQuant method to train neural networks with quantized weights.", "Proposes solving binary nets and its variants using proximal gradient descent." ] }, { "id": "task668-f5728268ceb843fb9d6a7245601c4b06", "input": "Autonomy and adaptation of machines requires that they be able to measure their own errors. We consider the advantages and limitations of such an approach when a machine has to measure the error in a regression task. How can a machine measure the error of regression sub-components when it does not have the ground truth for the correct predictions? A \n compressed sensing approach applied to the error signal of the regressors can recover their precision error without any ground truth. It allows for some regressors to be strongly correlated as long as not too many are so related.\n Its solutions, however, are not unique - a property of ground truth inference solutions. Adding $\\ell_1$--minimization\n as a condition can recover the correct solution in settings where error correction is possible. We briefly discuss the similarity of the mathematics of ground truth inference for regressors to that for classifiers.", "output": [ "A non-parametric method to measure the error moments of regressors without ground truth can be used with biased regressors" ] }, { "id": "task668-e06085ad147f4b6f873984b9366809de", "input": "We introduce a new method for training GANs by applying the Wasserstein-2 metric proximal on the generators. \n The approach is based on the gradient operator induced by optimal transport, which connects the geometry of sample space and parameter space in implicit deep generative models. From this theory, we obtain an easy-to-implement regularizer for the parameter updates. Our experiments demonstrate that this method improves the speed and stability in training GANs in terms of wall-clock time and Fr\\'echet Inception Distance (FID) learning curves.", "output": [ "We propose the Wasserstein proximal method for training GANs. ", "Proposes a new GAN procedure that takes into account points generated in the previous iteration and updates the generator to be carried out l times.", "Considers natural gradient learning in GAN learning, where the Riemannian structure induced by the Wasserstein-2 distance is employed.", "The paper intends to utilize natural gradient induced by Wasserstein-2 distance to train the generator in GAN and the authors propose the Wasserstein proximal operator as a regularization." ] }, { "id": "task668-6d5546e27fde422d9d5d99c9d4c6f0d3", "input": "We recently observed that convolutional filters initialized\n farthest apart from each other using offthe-\n shelf pre-computed Grassmannian subspace\n packing codebooks performed surprisingly well\n across many datasets. Through this short paper,\n we’d like to disseminate some initial results in this\n regard in the hope that we stimulate the curiosity\n of the deep-learning community towards considering\n classical Grassmannian subspace packing\n results as a source of new ideas for more efficient\n initialization strategies.", "output": [ "Initialize weights using off-the-shelf Grassmannian codebooks, get faster training and better accuracy" ] }, { "id": "task668-f20245fef9364f638f4d57d511df51b8", "input": "Generative adversarial networks (GANs) train implicit generative models through solving minimax problems. Such minimax problems are known as nonconvex- nonconcave, for which the dynamics of first-order methods are not well understood. In this paper, we consider GANs in the type of the integral probability metrics (IPMs) with the generator represented by an overparametrized neural network. When the discriminator is solved to approximate optimality in each iteration, we prove that stochastic gradient descent on a regularized IPM objective converges globally to a stationary point with a sublinear rate. Moreover, we prove that when the width of the generator network is sufficiently large and the discriminator function class has enough discriminative ability, the obtained stationary point corresponds to a generator that yields a distribution that is close to the distribution of the observed data in terms of the total variation. To the best of our knowledge, we seem to first establish both the global convergence and global optimality of training GANs when the generator is parametrized by a neural network.", "output": [ "We establish global convergence to optimality for IPM-based GANs where the generator is an overparametrized neural network. " ] }, { "id": "task668-5923f48876244344908db9f1b6b0d2f2", "input": "Generative models have proven to be an outstanding tool for representing high-dimensional probability distributions and generating realistic looking images. A fundamental characteristic of generative models is their ability to produce multi-modal outputs. However, while training, they are often susceptible to mode collapse, which means that the model is limited in mapping the input noise to only a few modes of the true data distribution. In this paper, we draw inspiration from Determinantal Point Process (DPP) to devise a generative model that alleviates mode collapse while producing higher quality samples. DPP is an elegant probabilistic measure used to model negative correlations within a subset and hence quantify its diversity. We use DPP kernel to model the diversity in real data as well as in synthetic data. Then, we devise a generation penalty term that encourages the generator to synthesize data with a similar diversity to real data. In contrast to previous state-of-the-art generative models that tend to use additional trainable parameters or complex training paradigms, our method does not change the original training scheme. Embedded in an adversarial training and variational autoencoder, our Generative DPP approach shows a consistent resistance to mode-collapse on a wide-variety of synthetic data and natural image datasets including MNIST, CIFAR10, and CelebA, while outperforming state-of-the-art methods for data-efficiency, convergence-time, and generation quality. Our code will be made publicly available.", "output": [ "The addition of a diversity criterion inspired from DPP in the GAN objective avoids mode collapse and leads to better generations. " ] }, { "id": "task668-9c61874d13db4f58982ab178fb724030", "input": "We study the problem of learning and optimizing through physical simulations via differentiable programming. We present DiffSim, a new differentiable programming language tailored for building high-performance differentiable physical simulations. We demonstrate the performance and productivity of our language in gradient-based learning and optimization tasks on 10 different physical simulators. For example, a differentiable elastic object simulator written in our language is 4.6x faster than the hand-engineered CUDA version yet runs as fast, and is 188x faster than TensorFlow. Using our differentiable programs, neural network controllers are typically optimized within only tens of iterations. Finally, we share the lessons learned from our experience developing these simulators, that is, differentiating physical simulators does not always yield useful gradients of the physical system being simulated. We systematically study the underlying reasons and propose solutions to improve gradient quality.", "output": [ "We study the problem of learning and optimizing through physical simulations via differentiable programming, using our proposed DiffSim programming language and compiler." ] }, { "id": "task668-8cf16c624d7d49fd975d30d13c11c449", "input": "Significant work has been dedicated to developing methods for communicating reasons for decision-making within au-\n tomated scheduling systems to human users. However, much less focus has been placed on communicating reasons for why\n scheduling systems are unable to arrive at a feasible solution when over-constrained. We investigate this problem in the\n context of task scheduling. We introduce the agent resource-constrained project scheduling problem (ARCPSP), an ex-\n tension of the resource-constrained project scheduling problem which includes a conception of agents that execute tasks\n in parallel. We outline a generic framework, based on efficiently enumerating minimal unsatisfiable sets (MUS) and\n maximal satisfiable sets (MSS), to produce small descriptions of the source of infeasibility. These descriptions are supple-\n mented with potential relaxations that would fix the infeasibility found within the problem instance. We illustrate how\n this method may be applied to the ARCPSP and demonstrate how to generate different types of explanations for an over-\n constrained instance of the ARCPSP.", "output": [ "We develop a framework for generating human-understandable explanations for why infeasibility is occurring in over-constrained instances of a class of resource-constrained scheduling problems." ] }, { "id": "task668-3efb787c827d477f87979f8cea91ad56", "input": "In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images. We propose Defense-GAN, a new framework leveraging the expressive capability of generative models to defend deep neural networks against such attacks. Defense-GAN is trained to model the distribution of unperturbed images. At inference time, it finds a close output to a given image which does not contain the adversarial changes. This output is then fed to the classifier. Our proposed method can be used with any classification model and does not modify the classifier structure or training procedure. It can also be used as a defense against any attack as it does not assume knowledge of the process for generating the adversarial examples. We empirically show that Defense-GAN is consistently effective against different attack methods and improves on existing defense strategies.", "output": [ "Defense-GAN uses a Generative Adversarial Network to defend against white-box and black-box attacks in classification models." ] }, { "id": "task668-5e7e03c96b8f45c6b906d5c084b81d25", "input": "Dynamical system models (including RNNs) often lack the ability to adapt the sequence generation or prediction to a given context, limiting their real-world application. In this paper we show that hierarchical multi-task dynamical systems (MTDSs) provide direct user control over sequence generation, via use of a latent code z that specifies the customization to the\n individual data sequence. This enables style transfer, interpolation and morphing within generated sequences. We show the MTDS can improve predictions via latent code interpolation, and avoid the long-term performance degradation of standard RNN approaches.", "output": [ "Tailoring predictions from sequence models (such as LDSs and RNNs) via an explicit latent code." ] }, { "id": "task668-25be054c3b3946b1b090414ffa4bc651", "input": "Recurrent neural networks (RNNs) can learn continuous vector representations of symbolic structures such as sequences and sentences; these representations often exhibit linear regularities (analogies). Such regularities motivate our hypothesis that RNNs that show such regularities implicitly compile symbolic structures into tensor product representations (TPRs; Smolensky, 1990), which additively combine tensor products of vectors representing roles (e.g., sequence positions) and vectors representing fillers (e.g., particular words) . To test this hypothesis, we introduce Tensor Product Decomposition Networks (TPDNs), which use TPRs to approximate existing vector representations . We demonstrate using synthetic data that TPDNs can successfully approximate linear and tree-based RNN autoencoder representations, suggesting that these representations exhibit interpretable compositional structure; we explore the settings that lead RNNs to induce such structure-sensitive representations . By contrast, further TPDN experiments show that the representations of four models trained to encode naturally-occurring sentences can be largely approximated with a bag of words, with only marginal improvements from more sophisticated structures . We conclude that TPDNs provide a powerful method for interpreting vector representations, and that standard RNNs can induce compositional sequence representations that are remarkably well approximated byTPRs; at the same time, existing training tasks for sentence representation learning may not be sufficient for inducing robust structural representations", "output": [ "RNNs implicitly implement tensor-product representations, a principled and interpretable method for representing symbolic structures in continuous space." ] }, { "id": "task668-b649f32960d841b8aeb66f22e7bf6229", "input": "We analyze the convergence of (stochastic) gradient descent algorithm for learning a convolutional filter with Rectified Linear Unit (ReLU) activation function. Our analysis does not rely on any specific form of the input distribution and our proofs only use the definition of ReLU, in contrast with previous works that are restricted to standard Gaussian input. We show that (stochastic) gradient descent with random initialization can learn the convolutional filter in polynomial time and the convergence rate depends on the smoothness of the input distribution and the closeness of patches. To the best of our knowledge, this is the first recovery guarantee of gradient-based algorithms for convolutional filter on non-Gaussian input distributions. Our theory also justifies the two-stage learning rate strategy in deep neural networks. While our focus is theoretical, we also present experiments that justify our theoretical findings.", "output": [ "We prove randomly initialized (stochastic) gradient descent learns a convolutional filter in polynomial time.", "Studies the problem of learning a single convolutional filter using SGD and shows that under certain conditions, SGD learns a single convolutional filter.", "This paper extends the Gaussian distribution assumption to a more general angular smoothness assumption, which covers a wider family of input distributions" ] }, { "id": "task668-9d4231b1106249b8ac72db1a6bd682f4", "input": "Reinforcement Learning (RL) can model complex behavior policies for goal-directed sequential decision making tasks. A hallmark of RL algorithms is Temporal Difference (TD) learning: value function for the current state is moved towards a bootstrapped target that is estimated using the next state's value function. lambda-returns define the target of the RL agent as a weighted combination of rewards estimated by using multiple many-step look-aheads. Although mathematically tractable, the use of exponentially decaying weighting of n-step returns based targets in lambda-returns is a rather ad-hoc design choice. Our major contribution is that we propose a generalization of lambda-returns called Confidence-based Autodidactic Returns (CAR), wherein the RL agent learns the weighting of the n-step returns in an end-to-end manner. In contrast to lambda-returns wherein the RL agent is restricted to use an exponentially decaying weighting scheme, CAR allows the agent to learn to decide how much it wants to weigh the n-step returns based targets. Our experiments, in addition to showing the efficacy of CAR, also empirically demonstrate that using sophisticated weighted mixtures of multi-step returns (like CAR and lambda-returns) considerably outperforms the use of n-step returns. We perform our experiments on the Asynchronous Advantage Actor Critic (A3C) algorithm in the Atari 2600 domain.", "output": [ "A novel way to generalize lambda-returns by allowing the RL agent to decide how much it wants to weigh each of the n-step returns.", "Extends the A3C algorithm with lambda returns, and proposes an approach for learning the weights of the returns.", "The authors present confidence-based autodidactic returns, a Deep learning RL method to adjust the weights of an eligibility vector in TD(lambda)-like value estimation to favour more stable estimates of the state." ] }, { "id": "task668-e9ca0a104c50436fbf100591af91eb80", "input": "We show that generating English Wikipedia articles can be approached as a multi-\n document summarization of source documents. We use extractive summarization\n to coarsely identify salient information and a neural abstractive model to generate\n the article. For the abstractive model, we introduce a decoder-only architecture\n that can scalably attend to very long sequences, much longer than typical encoder-\n decoder architectures used in sequence transduction. We show that this model can\n generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia\n articles. When given reference documents, we show it can extract relevant factual\n information as reflected in perplexity, ROUGE scores and human evaluations.", "output": [ "We generate Wikipedia articles abstractively conditioned on source document text." ] }, { "id": "task668-2bffdc4241264495a95ee4dca9ba214b", "input": "Formulating the reinforcement learning (RL) problem in the framework of probabilistic inference not only offers a new perspective about RL, but also yields practical algorithms that are more robust and easier to train. While this connection between RL and probabilistic inference has been extensively studied in the single-agent setting, it has not yet been fully understood in the multi-agent setup. In this paper, we pose the problem of multi-agent reinforcement learning as the problem of performing inference in a particular graphical model. We model the environment, as seen by each of the agents, using separate but related Markov decision processes. We derive a practical off-policy maximum-entropy actor-critic algorithm that we call Multi-agent Soft Actor-Critic (MA-SAC) for performing approximate inference in the proposed model using variational inference. MA-SAC can be employed in both cooperative and competitive settings. Through experiments, we demonstrate that MA-SAC outperforms a strong baseline on several multi-agent scenarios. While MA-SAC is one resultant multi-agent RL algorithm that can be derived from the proposed probabilistic framework, our work provides a unified view of maximum-entropy algorithms in the multi-agent setting.", "output": [ "A probabilistic framework for multi-agent reinforcement learning", "This paper proposes a new algorithm named Multi-Agent Soft Actor-Critic (MA-SAC) based on the off-policy maximum-entropy actor critic algorithm Soft Actor-Critic (SAC)" ] }, { "id": "task668-a9fbced7af9e411687aeceffe628aaa8", "input": "There is significant recent evidence in supervised learning that, in the over-parametrized setting, wider networks achieve better test error. In other words, the bias-variance tradeoff is not directly observable when increasing network width arbitrarily. We investigate whether a corresponding phenomenon is present in reinforcement learning. We experiment on four OpenAI Gym environments, increasing the width of the value and policy networks beyond their prescribed values. Our empirical results lend support to this hypothesis. However, tuning the hyperparameters of each network width separately remains as important future work in environments/algorithms where the optimal hyperparameters vary noticably across widths, confounding the results when the same hyperparameters are used for all widths.", "output": [ "Over-parametrization in width seems to help in deep reinforcement learning, just as it does in supervised learning." ] }, { "id": "task668-68238ad8d5dd497f84229ebb8b499dd1", "input": "We propose the Information Maximization Autoencoder (IMAE), an information theoretic approach to simultaneously learn continuous and discrete representations in an unsupervised setting. Unlike the Variational Autoencoder framework, IMAE starts from a stochastic encoder that seeks to map each input data to a hybrid discrete and continuous representation with the objective of maximizing the mutual information between the data and their representations. A decoder is included to approximate the posterior distribution of the data given their representations, where a high fidelity approximation can be achieved by leveraging the informative representations. \n We show that the proposed objective is theoretically valid and provides a principled framework for understanding the tradeoffs regarding informativeness of each representation factor, disentanglement of representations, and decoding quality.", "output": [ "Information theoretical approach for unsupervised learning of unsupervised learning of a hybrid of discrete and continuous representations, " ] }, { "id": "task668-124a3309606e4838a8544d3e8760c689", "input": "An open question in the Deep Learning community is why neural networks trained with Gradient Descent generalize well on real datasets even though they are capable of fitting random data. We propose an approach to answering this question based on a hypothesis about the dynamics of gradient descent that we call Coherent Gradients: Gradients from similar examples are similar and so the overall gradient is stronger in certain directions where these reinforce each other. Thus changes to the network parameters during training are biased towards those that (locally) simultaneously benefit many examples when such similarity exists. We support this hypothesis with heuristic arguments and perturbative experiments and outline how this can explain several common empirical observations about Deep Learning. Furthermore, our analysis is not just descriptive, but prescriptive. It suggests a natural modification to gradient descent that can greatly reduce overfitting.", "output": [ "We propose a hypothesis for why gradient descent generalizes based on how per-example gradients interact with each other." ] }, { "id": "task668-0b44235ae54749fd9a760c985658ccad", "input": "Distillation is a method to transfer knowledge from one model to another and often achieves higher accuracy with the same capacity. In this paper, we aim to provide a theoretical understanding on what mainly helps with the distillation. Our answer is \"early stopping\". Assuming that the teacher network is overparameterized, we argue that the teacher network is essentially harvesting dark knowledge from the data via early stopping. This can be justified by a new concept, Anisotropic In- formation Retrieval (AIR), which means that the neural network tends to fit the informative information first and the non-informative information (including noise) later. Motivated by the recent development on theoretically analyzing overparame- terized neural networks, we can characterize AIR by the eigenspace of the Neural Tangent Kernel(NTK). AIR facilities a new understanding of distillation. With that, we further utilize distillation to refine noisy labels. We propose a self-distillation al- gorithm to sequentially distill knowledge from the network in the previous training epoch to avoid memorizing the wrong labels. We also demonstrate, both theoret- ically and empirically, that self-distillation can benefit from more than just early stopping. Theoretically, we prove convergence of the proposed algorithm to the ground truth labels for randomly initialized overparameterized neural networks in terms of l2 distance, while the previous result was on convergence in 0-1 loss. The theoretical result ensures the learned neural network enjoy a margin on the training data which leads to better generalization. Empirically, we achieve better testing accuracy and entirely avoid early stopping which makes the algorithm more user-friendly.\n", "output": [ "theoretically understand the regularization effect of distillation. We show that early stopping is essential in this process. From this perspective, we developed a distillation method for learning with corrupted Label with theoretical guarantees." ] }, { "id": "task668-0dde52f0712e459981279b949a8c433e", "input": "In recent years, the efficiency and even the feasibility of traditional load-balancing policies are challenged by the rapid growth of cloud infrastructure with increasing levels of server heterogeneity and increasing size of cloud services and applications. In such many software-load-balancers heterogeneous systems, traditional solutions, such as JSQ, incur an increasing communication overhead, whereas low-communication alternatives, such as JSQ(d) and the recently proposed JIQ scheme are either unstable or provide poor performance.\n\n We argue that a better low-communication load balancing scheme can be established by allowing each dispatcher to have a different view of the system and keep using JSQ, rather than greedily trying to avoid starvation on a per-decision basis. \n accordingly, we introduce the Loosely-Shortest -Queue family of load balancing algorithms. Roughly speaking, in Loosely-shortest -Queue, each dispatcher keeps a different approximation of the server queue lengths and routes jobs to the shortest among them. Communication is used only to update the approximations and make sure that they are not too far from the real queue lengths in expectation. We formally establish the strong stability of any Loosely-Shortest -Queue policy and provide an easy-to-verify sufficient condition for verifying that a policy is Loosely-Shortest -Queue. We further demonstrate that the Loosely-Shortest -Queue approach allows constructing throughput optimal policies with an arbitrarily low communication budget.\n\n Finally, using extensive simulations that consider homogeneous, heterogeneous and highly skewed heterogeneous systems in scenarios with a single dispatcher as well as with multiple dispatchers, we show that the examined Loosely-Shortest -Queue example policies are always stable as dictated by theory. Moreover, it exhibits an appealing performance and significantly outperforms well-known low-communication policies, such as JSQ(d) and JIQ, while using a similar communication budget.", "output": [ "Scalable and low communication load balancing solution for heterogeneous-server multi-dispatcher systems with strong theoretical guarantees and promising empirical results. " ] }, { "id": "task668-cdd77fdf6ed84fb38761bc0c235cdad3", "input": "The quality of the representations achieved by embeddings is determined by how well the geometry of the embedding space matches the structure of the data.\n Euclidean space has been the workhorse for embeddings; recently hyperbolic and spherical spaces have gained popularity due to their ability to better embed new types of structured data---such as hierarchical data---but most data is not structured so uniformly.\n We address this problem by proposing learning embeddings in a product manifold combining multiple copies of these model spaces (spherical, hyperbolic, Euclidean), providing a space of heterogeneous curvature suitable for a wide variety of structures.\n We introduce a heuristic to estimate the sectional curvature of graph data and directly determine an appropriate signature---the number of component spaces and their dimensions---of the product manifold.\n Empirically, we jointly learn the curvature and the embedding in the product space via Riemannian optimization.\n We discuss how to define and compute intrinsic quantities such as means---a challenging notion for product manifolds---and provably learnable optimization functions.\n On a range of datasets and reconstruction tasks, our product space embeddings outperform single Euclidean or hyperbolic spaces used in previous works, reducing distortion by 32.55% on a Facebook social network dataset. We learn word embeddings and find that a product of hyperbolic spaces in 50 dimensions consistently improves on baseline Euclidean and hyperbolic embeddings, by 2.6\n points in Spearman rank correlation on similarity tasks\n and 3.4 points on analogy accuracy.\n", "output": [ "Product manifold embedding spaces with heterogenous curvature yield improved representations compared to traditional embedding spaces for a variety of structures.", "Proposes a dimensionality reduction method that embeds data into a product manifold of spherical, Euclidean, and hyperbolic manifolds. The algorithm is based on matching the geodesic distances on the product manifold to graph distances." ] }, { "id": "task668-7009880aa166460ab0c68b473cf381c9", "input": "In this work, we aim to solve data-driven optimization problems, where the goal is to find an input that maximizes an unknown score function given access to a dataset of input, score pairs. Inputs may lie on extremely thin manifolds in high-dimensional spaces, making the optimization prone to falling-off the manifold. Further, evaluating the unknown function may be expensive, so the algorithm should be able to exploit static, offline data. We propose model inversion networks (MINs) as an approach to solve such problems. Unlike prior work, MINs scale to extremely high-dimensional input spaces and can efficiently leverage offline logged datasets for optimization in both contextual and non-contextual settings. We show that MINs can also be extended to the active setting, commonly studied in prior work, via a simple, novel and effective scheme for active data collection. Our experiments show that MINs act as powerful optimizers on a range of contextual/non-contextual, static/active problems including optimization over images and protein designs and learning from logged bandit feedback.", "output": [ "We propose a novel approach to solve data-driven model-based optimization problems in both passive and active settings that can scale to high-dimensional input spaces." ] }, { "id": "task668-3ef77d6761b5459c9d328ed94898902e", "input": "Animals develop novel skills not only through the interaction with the environment but also from the influence of the others. In this work we model the social influence into the scheme of reinforcement learning, enabling the agents to learn both from the environment and from their peers. Specifically, we first define a metric to measure the distance between policies then quantitatively derive the definition of uniqueness. Unlike previous precarious joint optimization approaches, the social uniqueness motivation in our work is imposed as a constraint to encourage the agent to learn a policy different from the existing agents while still solve the primal task. The resulting algorithm, namely Interior Policy Differentiation (IPD), brings about performance improvement as well as a collection of policies that solve a given task with distinct behaviors", "output": [ "A new RL algorithm called Interior Policy Differentiation is proposed to learn a collection of diverse policies for a given primal task." ] }, { "id": "task668-fd617e4552c24b808e57cc03c9b2b4fc", "input": "Users have tremendous potential to aid in the construction and maintenance of knowledges bases (KBs) through the contribution of feedback that identifies incorrect and missing entity attributes and relations. However, as new data is added to the KB, the KB entities, which are constructed by running entity resolution (ER), can change, rendering the intended targets of user feedback unknown–a problem we term identity uncertainty. In this work, we present a framework for integrating user feedback into KBs in the presence of identity uncertainty. Our approach is based on having user feedback participate alongside mentions in ER. We propose a specific representation of user feedback as feedback mentions and introduce a new online algorithm for integrating these mentions into an existing KB. In experiments, we demonstrate that our proposed approach outperforms the baselines in 70% of experimental conditions.", "output": [ "This paper develops a framework for integrating user feedback under identity uncertainty in knowledge bases. " ] }, { "id": "task668-1deda61fe4de4f6d8926c71943c2a1de", "input": "We propose a new architecture for distributed image compression from a group of distributed data sources. The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy. The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources. Its compression capability is much better than the method of training codecs separately. Meanwhile, for 10 distributed sources, our distributed system remarkably performs within 2 dB peak signal-to-noise ratio (PSNR) of that of a single codec trained with all data sources. We experiment distributed sources with different correlations and show how our methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding (DSC). Our method is also shown to be robust to the lack of presence of encoded data from a number of distributed sources. Moreover, it is scalable in the sense that codes can be decoded simultaneously at more than one compression quality level. To the best of our knowledge, this is the first data-driven DSC framework for general distributed code design with deep learning.", "output": [ "We introduce a data-driven Distributed Source Coding framework based on Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC).", "The paper proposed a distributed recurrent auto-encoder for image compression that uses a ConvLSTM to learn binary codes that are constructed progressively from residuals of previously encoded information", "The authors propose a method to train image compression models on multiple sources, with a separate encoder on each source, and a shared decoder. " ] }, { "id": "task668-c6342169013942818ea9dd3a5261f766", "input": "Generative adversarial networks (GANs) learn to map samples from a noise distribution to a chosen data distribution. Recent work has demonstrated that GANs are consequently sensitive to, and limited by, the shape of the noise distribution. For example, a single generator struggles to map continuous noise (e.g. a uniform distribution) to discontinuous output (e.g. separate Gaussians) or complex output (e.g. intersecting parabolas). We address this problem by learning to generate from multiple models such that the generator's output is actually the combination of several distinct networks. We contribute a novel formulation of multi-generator models where we learn a prior over the generators conditioned on the noise, parameterized by a neural network. Thus, this network not only learns the optimal rate to sample from each generator but also optimally shapes the noise received by each generator. The resulting Noise Prior GAN (NPGAN) achieves expressivity and flexibility that surpasses both single generator models and previous multi-generator models.", "output": [ "A multi-generator GAN framework with an additional network to learn a prior over the input noise." ] }, { "id": "task668-3c423fbf50274b18b1086b0108da0296", "input": "Probability density estimation is a classical and well studied problem, but standard density estimation methods have historically lacked the power to model complex and high-dimensional image distributions. More recent generative models leverage the power of neural networks to implicitly learn and represent probability models over complex images. We describe methods to extract explicit probability density estimates from GANs, and explore the properties of these image density functions. We perform sanity check experiments to provide evidence that these probabilities are reasonable. However, we also show that density functions of natural images are difficult to interpret and thus limited in use. We study reasons for this lack of interpretability, and suggest that we can get better interpretability by doing density estimation on latent representations of images. ", "output": [ "We examine the relationship between probability density values and image content in non-invertible GANs.", "The authors try to estimate the probability distribution of the image with the help of GAN and develop a proper approximation to the PDFs in the latent space." ] }, { "id": "task668-ad37fff8c55d42ee8ea59d85a7c7f183", "input": "Methods that align distributions by minimizing an adversarial distance between them have recently achieved impressive results. However, these approaches are difficult to optimize with gradient descent and they often do not converge well without careful hyperparameter tuning and proper initialization. We investigate whether turning the adversarial min-max problem into an optimization problem by replacing the maximization part with its dual improves the quality of the resulting alignment and explore its connections to Maximum Mean Discrepancy. Our empirical results suggest that using the dual formulation for the restricted family of linear discriminators results in a more stable convergence to a desirable solution when compared with the performance of a primal min-max GAN-like objective and an MMD objective under the same restrictions. We test our hypothesis on the problem of aligning two synthetic point clouds on a plane and on a real-image domain adaptation problem on digits. In both cases, the dual formulation yields an iterative procedure that gives more stable and monotonic improvement over time.", "output": [ " We propose a dual version of the logistic adversarial distance for feature alignment and show that it yields more stable gradient step iterations than the min-max objective.", "The paper deals with fixing GANs at the computational level", "This paper studies a dual formulation of an adversarial loss based on an upper-bound of the logistic loss, and turns the standard min max problem of adversarial training into a single minimization problem.", "Proposes to re-formulate the GAN saddle point objective (for a logistic regression discriminator) as a minimization problem by dualizing the maximum likelihood objective for regularized logistic regression" ] }, { "id": "task668-1191419f7d5347b2a44d68d2f38610f5", "input": "Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks. Introducing the concept of an optimal representation space, we provide a simple theoretical resolution to this apparent paradox. In addition, we present a straightforward procedure that, without any retraining or architectural modifications, allows deep recurrent models to perform equally well (and sometimes better) when compared to shallow models. To validate our analysis, we conduct a set of consistent empirical evaluations and introduce several new sentence embedding models in the process. Even though this work is presented within the context of natural language processing, the insights are readily applicable to other domains that rely on distributed representations for transfer tasks.", "output": [ "By introducing the notion of an optimal representation space, we provide a theoretical argument and experimental validation that an unsupervised model for sentences can perform well on both supervised similarity and unsupervised transfer tasks." ] }, { "id": "task668-e2111b4d7b684817aaa92f190bf81ec0", "input": "Neural networks can learn to extract statistical properties from data, but they seldom make use of structured information from the label space to help representation learning. Although some label structure can implicitly be obtained when training on huge amounts of data, in a few-shot learning context where little data is available, making explicit use of the label structure can inform the model to reshape the representation space to reflect a global sense of class dependencies. We propose a meta-learning framework, Conditional class-Aware Meta-Learning (CAML), that conditionally transforms feature representations based on a metric space that is trained to capture inter-class dependencies. This enables a conditional modulation of the feature representations of the base-learner to impose regularities informed by the label space. Experiments show that the conditional transformation in CAML leads to more disentangled representations and achieves competitive results on the miniImageNet benchmark.", "output": [ "CAML is an instance of MAML with conditional class dependencies." ] }, { "id": "task668-e13db64c395a454880a6d28d9a4a5cc7", "input": "In some misspecified settings, the posterior distribution in Bayesian statistics may lead to inconsistent estimates. To fix this issue, it has been suggested to replace the likelihood by a pseudo-likelihood, that is the exponential of a loss function enjoying suitable robustness properties. In this paper, we build a pseudo-likelihood based on the Maximum Mean Discrepancy, defined via an embedding of probability distributions into a reproducing kernel Hilbert space. We show that this MMD-Bayes posterior is consistent and robust to model misspecification. As the posterior obtained in this way might be intractable, we also prove that reasonable variational approximations of this posterior enjoy the same properties. We provide details on a stochastic gradient algorithm to compute these variational approximations. Numerical simulations indeed suggest that our estimator is more robust to misspecification than the ones based on the likelihood.\n", "output": [ "Robust Bayesian Estimation via Maximum Mean Discrepancy" ] }, { "id": "task668-14a21787d21048c09ec44dffbe88ec71", "input": "Visual Question Answering (VQA) models have struggled with counting objects in natural images so far. We identify a fundamental problem due to soft attention in these models as a cause. To circumvent this problem, we propose a neural network component that allows robust counting from object proposals. Experiments on a toy task show the effectiveness of this component and we obtain state-of-the-art accuracy on the number category of the VQA v2 dataset without negatively affecting other categories, even outperforming ensemble models with our single model. On a difficult balanced pair metric, the component gives a substantial improvement in counting over a strong baseline by 6.6%.", "output": [ "Enabling Visual Question Answering models to count by handling overlapping object proposals.", "This paper proposes a hand-designed network architecture on a graph of object proposals to perform soft non-maximum suppression to get object count.", "Focuses on a counting problem in visual question answering using attention mechanism and propsoe a differentiable counting compoent which explicitly counts the number of objects", "This paper tackles the object counting problem in visual question answering, it proposes many heuristics to find the correct count." ] }, { "id": "task668-399f7959924f4fc4bb2bf0be6f35674a", "input": "Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers by adding imperceptible perturbations. An intriguing property of adversarial examples is their good transferability, making black-box attacks feasible in real-world applications. Due to the threat of adversarial attacks, many methods have been proposed to improve the robustness, and several state-of-the-art defenses are shown to be robust against transferable adversarial examples. In this paper, we identify the attention shift phenomenon, which may hinder the transferability of adversarial examples to the defense models. It indicates that the defenses rely on different discriminative regions to make predictions compared with normally trained models. Therefore, we propose an attention-invariant attack method to generate more transferable adversarial examples. Extensive experiments on the ImageNet dataset validate the effectiveness of the proposed method. Our best attack fools eight state-of-the-art defenses at an 82% success rate on average based only on the transferability, demonstrating the insecurity of the defense techniques.", "output": [ "We propose an attention-invariant attack method to generate more transferable adversarial examples for black-box attacks, which can fool state-of-the-art defenses with a high success rate.", "The paper proposes a new way of overcoming state of the art defences against adversarial attacks on CNN.", "This paper suggests that \"attention shift\" is a key property behind failure of adversarial attacks to transfer and propose an attention-invariant attack method" ] }, { "id": "task668-f857cd91ccae4d7f9db59db4655ff580", "input": "In recent years, substantial progress has been made on graph convolutional networks (GCN). In this paper, for the first time, we theoretically analyze the connections between GCN and matrix factorization (MF), and unify GCN as matrix factorization with co-training and unitization. Moreover, under the guidance of this theoretical analysis, we propose an alternative model to GCN named Co-training and Unitized Matrix Factorization (CUMF). The correctness of our analysis is verified by thorough experiments. The experimental results show that CUMF achieves similar or superior performances compared to GCN. In addition, CUMF inherits the benefits of MF-based methods to naturally support constructing mini-batches, and is more friendly to distributed computing comparing with GCN. The distributed CUMF on semi-supervised node classification significantly outperforms distributed GCN methods. Thus, CUMF greatly benefits large scale and complex real-world applications.", "output": [ "We unify graph convolutional networks as co-training and unitized matrix factorization." ] }, { "id": "task668-b9a27316a6f845e5956cb78a6c286b90", "input": "Obtaining high-quality uncertainty estimates is essential for many applications of deep neural networks. In this paper, we theoretically justify a scheme for estimating uncertainties, based on sampling from a prior distribution. Crucially, the uncertainty estimates are shown to be conservative in the sense that they never underestimate a posterior uncertainty obtained by a hypothetical Bayesian algorithm. We also show concentration, implying that the uncertainty estimates converge to zero as we get more data. Uncertainty estimates obtained from random priors can be adapted to any deep network architecture and trained using standard supervised learning pipelines. We provide experimental evaluation of random priors on calibration and out-of-distribution detection on typical computer vision tasks, demonstrating that they outperform deep ensembles in practice.", "output": [ "We provide theoretical support to uncertainty estimates for deep learning obtained fitting random priors." ] }, { "id": "task668-ee7e8c13b20444368cfecb0f553185a8", "input": "In this paper, a new intrinsic reward generation method for sparse-reward reinforcement learning is proposed based on an ensemble of dynamics models. In the proposed method, the mixture of multiple dynamics models is used to approximate the true unknown transition probability, and the intrinsic reward is designed as the minimum of the surprise seen from each dynamics model to the mixture of the dynamics models. In order to show the effectiveness of the proposed intrinsic reward generation method, a working algorithm is constructed by combining the proposed intrinsic reward generation method with the proximal policy optimization (PPO) algorithm. Numerical results show that for representative locomotion tasks, the proposed model-ensemble-based intrinsic reward generation method outperforms the previous methods based on a single dynamics model.", "output": [ "For sparse-reward reinforcement learning, the ensemble of multiple dynamics models is used to generate intrinsic reward designed as the minimum of the surprise." ] }, { "id": "task668-9f670fe7468b4c8c882406faa7e39aae", "input": "Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent reports (Han et al., 2015; Narang et al., 2017) prune deep networks at the cost of only a marginal loss in accuracy and achieve a sizable reduction in model size. This hints at the possibility that the baseline models in these experiments are perhaps severely over-parameterized at the outset and a viable alternative for model compression might be to simply reduce the number of hidden units while maintaining the model's dense connection structure, exposing a similar trade-off in model size and accuracy. We investigate these two distinct paths for model compression within the context of energy-efficient inference in resource-constrained environments and propose a new gradual pruning technique that is simple and straightforward to apply across a variety of models/datasets with minimal tuning and can be seamlessly incorporated within the training process. We compare the accuracy of large, but pruned models (large-sparse) and their smaller, but dense (small-dense) counterparts with identical memory footprint. Across a broad range of neural network architectures (deep CNNs, stacked LSTM, and seq2seq LSTM models), we find large-sparse models to consistently outperform small-dense models and achieve up to 10x reduction in number of non-zero parameters with minimal loss in accuracy.", "output": [ "We demonstrate that large, but pruned models (large-sparse) outperform their smaller, but dense (small-dense) counterparts with identical memory footprint." ] }, { "id": "task668-0bfd0033457d40e99390f06a17237b04", "input": "Loss functions play a crucial role in deep metric learning thus a variety of them have been proposed. Some supervise the learning process by pairwise or tripletwise similarity constraints while others take the advantage of structured similarity information among multiple data points. In this work, we approach deep metric learning from a novel perspective. We propose instance cross entropy (ICE) which measures the difference between an estimated instance-level matching distribution and its ground-truth one. ICE has three main appealing properties. Firstly, similar to categorical cross entropy (CCE), ICE has clear probabilistic interpretation and exploits structured semantic similarity information for learning supervision. Secondly, ICE is scalable to infinite training data as it learns on mini-batches iteratively and is independent of the training set size. Thirdly, motivated by our relative weight analysis, seamless sample reweighting is incorporated. It rescales samples’ gradients to control the differentiation degree over training examples instead of truncating them by sample mining. In addition to its simplicity and intuitiveness, extensive experiments on three real-world benchmarks demonstrate the superiority of ICE.", "output": [ "We propose instance cross entropy (ICE) which measures the difference between an estimated instance-level matching distribution and its ground-truth one. " ] }, { "id": "task668-8aa1f69252874944b48adc8f08efad67", "input": "Machine learning systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a different distribution from the one used for training. With their growing use in critical applications, it becomes important to develop systems that are able to accurately quantify its predictive uncertainty and screen out these anomalous inputs. However, unlike standard learning tasks, there is currently no well established guiding principle for designing architectures that can accurately quantify uncertainty. Moreover, commonly used OoD detection approaches are prone to errors and even sometimes assign higher likelihoods to OoD samples. To address these problems, we first seek to identify guiding principles for designing uncertainty-aware architectures, by proposing Neural Architecture Distribution Search (NADS). Unlike standard neural architecture search methods which seek for a single best performing architecture, NADS searches for a distribution of architectures that perform well on a given task, allowing us to identify building blocks common among all uncertainty aware architectures. With this formulation, we are able to optimize a stochastic outlier detection objective and construct an ensemble of models to perform OoD detection. We perform multiple OoD detection experiments and observe that our NADS performs favorably compared to state-of-the-art OoD detection methods.", "output": [ "We propose an architecture search method to identify a distribution of architectures and use it to construct a Bayesian ensemble for outlier detection." ] }, { "id": "task668-f3469f6b6cee483ea3a8d2db5b93dac1", "input": "Deep neural networks (DNN) are widely used in many applications. However, their deployment on edge devices has been difficult because they are resource hungry. Binary neural networks (BNN) help to alleviate the prohibitive resource requirements of DNN, where both activations and weights are limited to 1-bit. We propose an improved binary training method (BNN+), by introducing a regularization function that encourages training weights around binary values. In addition to this, to enhance model performance we add trainable scaling factors to our regularization functions. Furthermore, we use an improved approximation of the derivative of the sign activation function in the backward computation. These additions are based on linear operations that are easily implementable into the binary training framework. We show experimental results on CIFAR-10 obtaining an accuracy of 86.5%, on AlexNet and 91.3% with VGG network. On ImageNet, our method also outperforms the traditional BNN method and XNOR-net, using AlexNet by a margin of 4% and 2% top-1 accuracy respectively.", "output": [ "The paper presents an improved training mechanism for obtaining binary networks with smaller accuracy drop that helps close the gap with it's full precision counterpart" ] }, { "id": "task668-a3c8aeec8f9d4cd5992cfcf4682c1853", "input": "We propose a novel algorithm, Difference-Seeking Generative Adversarial Network (DSGAN), developed from traditional GAN. DSGAN considers the scenario that the training samples of target distribution, $p_{t}$, are difficult to collect.\n\n Suppose there are two distributions $p_{\\bar{d}}$ and $p_{d}$ such that the density of the target distribution can be the differences between the densities of $p_{\\bar{d}}$ and $p_{d}$. We show how to learn the target distribution $p_{t}$ only via samples from $p_{d}$ and $p_{\\bar{d}}$ (relatively easy to obtain).\n\n DSGAN has the flexibility to produce samples from various target distributions (e.g. the out-of-distribution). Two key applications, semi-supervised learning and adversarial training, are taken as examples to validate the effectiveness of DSGAN. We also provide theoretical analyses about the convergence of DSGAN.", "output": [ "We proposed \"Difference-Seeking Generative Adversarial Network\" (DSGAN) model to learn the target distribution which is hard to collect training data.", "This paper presents DS-GAN, which aims to learn the difference between any two distributions whose samples are difficult or impossible to collect, and shows its effectiveness on semi-supervised learning and adversarial training tasks.", "This paper considers the problem of learning a GAN to capture a target distribution with only very few training samples from that distribution available." ] }, { "id": "task668-e3418a2bf2bb4e8aa24521c02e6a2cb5", "input": "In the industrial field, the positron annihilation is not affected by complex environment, and the gamma-ray photon penetration is strong, so the nondestructive detection of industrial parts can be realized. Due to the poor image quality caused by gamma-ray photon scattering, attenuation and short sampling time in positron process, we propose the idea of combining deep learning to generate positron images with good quality and clear details by adversarial nets. The structure of the paper is as follows: firstly, we encode to get the hidden vectors of medical CT images based on transfer Learning, and use PCA to extract positron image features. Secondly, we construct a positron image memory based on attention mechanism as a whole input to the adversarial nets which uses medical hidden variables as a query. Finally, we train the whole model jointly and update the input parameters until convergence. Experiments have proved the possibility of generating rare positron images for industrial non-destructive testing using countermeasure networks, and good imaging results have been achieved.", "output": [ "adversarial nets, attention mechanism, positron images, data scarcity" ] }, { "id": "task668-8b51be07a07b411f901ba6f91c594338", "input": "Keyword spotting—or wakeword detection—is an essential feature for hands-free operation of modern voice-controlled devices. With such devices becoming ubiquitous, users might want to choose a personalized custom wakeword. In this work, we present DONUT, a CTC-based algorithm for online query-by-example keyword spotting that enables custom wakeword detection. The algorithm works by recording a small number of training examples from the user, generating a set of label sequence hypotheses from these training examples, and detecting the wakeword by aggregating the scores of all the hypotheses given a new audio recording. Our method combines the generalization and interpretability of CTC-based keyword spotting with the user-adaptation and convenience of a conventional query-by-example system. DONUT has low computational requirements and is well-suited for both learning and inference on embedded systems without requiring private user data to be uploaded to the cloud.", "output": [ "We propose an interpretable model for detecting user-chosen wakewords that learns from the user's examples." ] }, { "id": "task668-93cd73cb28c1425392250b5d47c85610", "input": "Bidirectional Encoder Representations from Transformers (BERT) reach state-of-the-art results in a variety of Natural Language Processing tasks. However, understanding of their internal functioning is still insufficient and unsatisfactory. In order to better understand BERT and other Transformer-based models, we present a layer-wise analysis of BERT's hidden states. Unlike previous research, which mainly focuses on explaining Transformer models by their \\hbox{attention} weights, we argue that hidden states contain equally valuable information. Specifically, our analysis focuses on models fine-tuned on the task of Question Answering (QA) as an example of a complex downstream task. We inspect how QA models transform token vectors in order to find the correct answer. To this end, we apply a set of general and QA-specific probing tasks that reveal the information stored in each representation layer. Our qualitative analysis of hidden state visualizations provides additional insights into BERT's reasoning process. Our results show that the transformations within BERT go through phases that are related to traditional pipeline tasks. The system can therefore implicitly incorporate task-specific information into its token representations. Furthermore, our analysis reveals that fine-tuning has little impact on the models' semantic abilities and that prediction errors can be recognized in the vector representations of even early layers.", "output": [ "We investigate hidden state activations of Transformer Models in Question Answering Tasks." ] }, { "id": "task668-28210e3cca164c41bde1002336e07a38", "input": "Continuous Bag of Words (CBOW) is a powerful text embedding method. Due to its strong capabilities to encode word content, CBOW embeddings perform well on a wide range of downstream tasks while being efficient to compute. However, CBOW is not capable of capturing the word order. The reason is that the computation of CBOW's word embeddings is commutative, i.e., embeddings of XYZ and ZYX are the same. In order to address this shortcoming, we propose a\n learning algorithm for the Continuous Matrix Space Model, which we call Continual Multiplication of Words (CMOW). Our algorithm is an adaptation of word2vec, so that it can be trained on large quantities of unlabeled text. We empirically show that CMOW better captures linguistic properties, but it is inferior to CBOW in memorizing word content. Motivated by these findings, we propose a hybrid model that combines the strengths of CBOW and CMOW. Our results show that the hybrid CBOW-CMOW-model retains CBOW's strong ability to memorize word content while at the same time substantially improving its ability to encode other linguistic information by 8%. As a result, the hybrid also performs better on 8 out of 11 supervised downstream tasks with an average improvement of 1.2%.", "output": [ "We present a novel training scheme for efficiently obtaining order-aware sentence representations." ] }, { "id": "task668-f4cfb3294fc04272b156a5be0f075574", "input": "Injecting adversarial examples during training, known as adversarial training, can improve robustness against one-step attacks, but not for unknown iterative attacks. To address this challenge, we first show iteratively generated adversarial images easily transfer between networks trained with the same strategy. Inspired by this observation, we propose cascade adversarial training, which transfers the knowledge of the end results of adversarial training. We train a network from scratch by injecting iteratively generated adversarial images crafted from already defended networks in addition to one-step adversarial images from the network being trained. We also propose to utilize embedding space for both classification and low-level (pixel-level) similarity learning to ignore unknown pixel level perturbation. During training, we inject adversarial images without replacing their corresponding clean images and penalize the distance between the two embeddings (clean and adversarial). Experimental results show that cascade adversarial training together with our proposed low-level similarity learning efficiently enhances the robustness against iterative attacks, but at the expense of decreased robustness against one-step attacks. We show that combining those two techniques can also improve robustness under the worst case black box attack scenario.", "output": [ "Cascade adversarial training + low level similarity learning improve robustness against both white box and black box attacks." ] }, { "id": "task668-58fa99ac9fa246858b3a5057d7523e85", "input": "There is a growing interest in automated neural architecture search (NAS). To improve the efficiency of NAS, previous approaches adopt weight sharing method to force all models share the same set of weights. However, it has been observed that a model performing better with shared weights does not necessarily perform better when trained alone. In this paper, we analyse existing weight sharing one-shot NAS approaches from a Bayesian point of view and identify the posterior fading problem, which compromises the effectiveness of shared weights. To alleviate this problem, we present a practical approach to guide the parameter posterior towards its true distribution. Moreover, a hard latency constraint is introduced during the search so that the desired latency can be achieved. The resulted method, namely Posterior Convergent NAS (PC-NAS), achieves state-of-the-art performance under standard GPU latency constraint on ImageNet. In our small search space, our model PC-NAS-S attains76.8% top-1 accuracy, 2.1% higher than MobileNetV2 (1.4x) with the same latency. When adopted to our large search space, PC-NAS-L achieves 78.1% top-1 accuracy within 11ms. The discovered architecture also transfers well to other computer vision applications such as object detection and person re-identification.", "output": [ "Our paper identifies the issue of existing weight sharing approach in neural architecture search and propose a practical method, achieving strong results.", "Author identifies an issue with NAS called posterior fading and introduces Posterior Convergent NAS to mitigate this effect" ] }, { "id": "task668-19264818044947a59eab6c65247d052f", "input": "A key problem in neuroscience and life sciences more generally is that the data generation process is often best thought of as a hierarchy of dynamic systems. One example of this is in-vivo calcium imaging data, where observed calcium transients are driven by a combination of electro-chemical kinetics where hypothesized trajectories around manifolds determining the frequency of these transients. A recent approach using sequential variational auto-encoders demonstrated it was possible to learn the latent dynamic structure of reaching behaviour from spiking data modelled as a Poisson process. Here we extend this approach using a ladder method to infer the spiking events driving calcium transients along with the deeper latent dynamic system. We show strong performance of this approach on a benchmark synthetic dataset against a number of alternatives.", "output": [ "We propose an extension to LFADS capable of inferring spike trains to reconstruct calcium fluorescence traces using hierarchical VAEs." ] }, { "id": "task668-d38d10d617bf4dba91a7c57cabfbb145", "input": "Among multiple ways of interpreting a machine learning model, measuring the importance of a set of features tied to a prediction is probably one of the most intuitive way to explain a model. In this paper, we establish the link between a set of features to a prediction with a new evaluation criteria, robustness analysis, which measures the minimum tolerance of adversarial perturbation. By measuring the tolerance level for an adversarial attack, we can extract a set of features that provides most robust support for a current prediction, and also can extract a set of features that contrasts the current prediction to a target class by setting a targeted adversarial attack. By applying this methodology to various prediction tasks across multiple domains, we observed the derived explanations are indeed capturing the significant feature set qualitatively and quantitatively.", "output": [ "We propose new objective measurement for evaluating explanations based on the notion of adversarial robustness. The evaluation criteria further allows us to derive new explanations which capture pertinent features qualitatively and quantitatively." ] }, { "id": "task668-19106632806247288ffdc93f1202d1d8", "input": "While bigger and deeper neural network architectures continue to advance the state-of-the-art for many computer vision tasks, real-world adoption of these networks is impeded by hardware and speed constraints. Conventional model compression methods attempt to address this problem by modifying the architecture manually or using pre-defined heuristics. Since the space of all reduced architectures is very large, modifying the architecture of a deep neural network in this way is a difficult task. In this paper, we tackle this issue by introducing a principled method for learning reduced network architectures in a data-driven way using reinforcement learning. Our approach takes a larger 'teacher' network as input and outputs a compressed 'student' network derived from the 'teacher' network. In the first stage of our method, a recurrent policy network aggressively removes layers from the large 'teacher' model. In the second stage, another recurrent policy network carefully reduces the size of each remaining layer. The resulting network is then evaluated to obtain a reward -- a score based on the accuracy and compression of the network. Our approach uses this reward signal with policy gradients to train the policies to find a locally optimal student network. Our experiments show that we can achieve compression rates of more than 10x for models such as ResNet-34 while maintaining similar performance to the input 'teacher' network. We also present a valuable transfer learning result which shows that policies which are pre-trained on smaller 'teacher' networks can be used to rapidly speed up training on larger 'teacher' networks.", "output": [ "A novel reinforcement learning based approach to compress deep neural networks with knowledge distillation", "This paper proposes to use reinforcement learning instead of pre-defined heuristics to determine the structure of the compressed model in the knowledge distillation process", "Introduces a principled way of network to network compression, which uses policy gradients for optimizing two policies which compress a strong teacher into a strong but smaller student model." ] }, { "id": "task668-ee41152899ce4d03a3ed24e1e845e31d", "input": "Deep reinforcement learning algorithms have proven successful in a variety of domains. However, tasks with sparse rewards remain challenging when the state space is large. Goal-oriented tasks are among the most typical problems in this domain, where a reward can only be received when the final goal is accomplished. In this work, we propose a potential solution to such problems with the introduction of an experience-based tendency reward mechanism, which provides the agent with additional hints based on a discriminative learning on past experiences during an automated reverse curriculum. This mechanism not only provides dense additional learning signals on what states lead to success, but also allows the agent to retain only this tendency reward instead of the whole histories of experience during multi-phase curriculum learning. We extensively study the advantages of our method on the standard sparse reward domains like Maze and Super Mario Bros and show that our method performs more efficiently and robustly than prior approaches in tasks with long time horizons and large state space. In addition, we demonstrate that using an optional keyframe scheme with very small quantity of key states, our approach can solve difficult robot manipulation challenges directly from perception and sparse rewards.", "output": [ "We propose Tendency RL to efficiently solve goal-oriented tasks with large state space using automated curriculum learning and discriminative shaping reward, which has the potential to tackle robot manipulation tasks with perception." ] }, { "id": "task668-097ef882a6f84ee4ae4d6e03ddedca95", "input": "Deep Neural Networks (DNNs) are increasingly deployed in cloud servers and autonomous agents due to their superior performance. The deployed DNN is either leveraged in a white-box setting (model internals are publicly known) or a black-box setting (only model outputs are known) depending on the application. A practical concern in the rush to adopt DNNs is protecting the models against Intellectual Property (IP) infringement. We propose BlackMarks, the first end-to-end multi-bit watermarking framework that is applicable in the black-box scenario. BlackMarks takes the pre-trained unmarked model and the owner’s binary signature as inputs. The output is the corresponding marked model with specific keys that can be later used to trigger the embedded watermark. To do so, BlackMarks first designs a model-dependent encoding scheme that maps all possible classes in the task to bit ‘0’ and bit ‘1’. Given the owner’s watermark signature (a binary string), a set of key image and label pairs is designed using targeted adversarial attacks. The watermark (WM) is then encoded in the distribution of output activations of the DNN by fine-tuning the model with a WM-specific regularized loss. To extract the WM, BlackMarks queries the model with the WM key images and decodes the owner’s signature from the corresponding predictions using the designed encoding scheme. We perform a comprehensive evaluation of BlackMarks’ performance on MNIST, CIFAR-10, ImageNet datasets and corroborate its effectiveness and robustness. BlackMarks preserves the functionality of the original DNN and incurs negligible WM embedding overhead as low as 2.054%.", "output": [ "Proposing the first watermarking framework for multi-bit signature embedding and extraction using the outputs of the DNN. ", "Proposes a method for multi-bit watermarking of neural networks in a black-box setting and demonstrate that the predictions of existing models can carry a multi-bit string that can later be used to verify ownership.", "The paper proposes an approach for model watermarking where the watermark is a bit string embedded in the model as part of a fine-tuning procedure" ] }, { "id": "task668-92976bf155684204a17ff92e58be77b9", "input": "Deep learning methods have achieved high performance in sound recognition tasks. Deciding how to feed the training data is important for further performance improvement. We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning). Our strategy is to learn a discriminative feature space by recognizing the between-class sounds as between-class sounds. We generate between-class sounds by mixing two sounds belonging to different classes with a random ratio. We then input the mixed sound to the model and train the model to output the mixing ratio. The advantages of BC learning are not limited only to the increase in variation of the training data; BC learning leads to an enlargement of Fisher’s criterion in the feature space and a regularization of the positional relationship among the feature distributions of the classes. The experimental results show that BC learning improves the performance on various sound recognition networks, datasets, and data augmentation schemes, in which BC learning proves to be always beneficial. Furthermore, we construct a new deep sound recognition network (EnvNet-v2) and train it with BC learning. As a result, we achieved a performance surpasses the human level.", "output": [ "We propose an novel learning method for deep sound recognition named BC learning.", "Authors defined a new learning task that requires a DNN to predict mixing ratio between sounds from two different classes to increase discriminitive power of the final learned network.", "Proposes a method to improve the performance of a generic learning method by generating \"in between class\" training samples and presents the basic intuition and necessity of the proposed technique." ] }, { "id": "task668-f8377645840a41a1b9b273b4b56aff66", "input": "Importance sampling (IS) is a standard Monte Carlo (MC) tool to compute information about random variables such as moments or quantiles with unknown distributions. IS is \nasymptotically consistent as the number of MC samples, and hence deltas (particles) that parameterize the density estimate, go to infinity. However, retaining infinitely many particles is intractable. We propose a scheme for only keeping a \\emph{finite representative subset} of particles and their augmented importance weights that is \\emph{nearly consistent}. To do so in {an online manner}, we approximate importance sampling in two ways. First, we replace the deltas by kernels, yielding kernel density estimates (KDEs). Second, we sequentially project KDEs onto nearby lower-dimensional subspaces. We characterize the asymptotic bias of this scheme as determined by a compression parameter and kernel bandwidth, which yields a tunable tradeoff between consistency and memory. In experiments, we observe a favorable tradeoff between memory and accuracy, providing for the first time near-consistent compressions of arbitrary posterior distributions.", "output": [ "We proposed a novel compressed kernelized importance sampling algorithm." ] }, { "id": "task668-e4ac675eeea04ca0954d36d50729d9c5", "input": "We present a new approach to defining a sequence loss function to train a summarizer by using a secondary encoder-decoder as a loss function, alleviating a shortcoming of word level training for sequence outputs. The technique is based on the intuition that if a summary is a good one, it should contain the most essential information from the original article, and therefore should itself be a good input sequence, in lieu of the original, from which a summary can be generated. We present experimental results where we apply this additional loss function to a general abstractive summarizer on a news summarization dataset. The result is an improvement in the ROUGE metric and an especially large improvement in human evaluations, suggesting enhanced performance that is competitive with specialized state-of-the-art models.", "output": [ "We present the use of a secondary encoder-decoder as a loss function to help train a summarizer." ] }, { "id": "task668-0cadb285495e41519a4bcba1c1bd2a4f", "input": "We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. \n This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. \n With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. \n We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. \n This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator.", "output": [ "We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model." ] }, { "id": "task668-2d077181ccbe434096e0d1da972716fc", "input": "Graph Convolutional Networks (GCNs) are a recently proposed architecture which has had success in semi-supervised learning on graph-structured data. At the same time, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper we propose a model, Network of GCNs (N-GCN), which marries these two lines of work. At its core, N-GCN trains multiple instances of GCNs over node pairs discovered at different distances in random walks, and learns a combination of the instance outputs which optimizes the classification objective. Our experiments show that our proposed N-GCN model achieves state-of-the-art performance on all of the challenging node classification tasks we consider: Cora, Citeseer, Pubmed, and PPI. In addition, our proposed method has other desirable properties, including generalization to recently proposed semi-supervised learning methods such as GraphSAGE, allowing us to propose N-SAGE, and resilience to adversarial input perturbations.", "output": [ "We make a network of Graph Convolution Networks, feeding each a different power of the adjacency matrix, combining all their representation into a classification sub-network, achieving state-of-the-art on semi-supervised node classification.", "Proposes a new network of GCNs with two approaches: a fully connected layer on top of stacked features and attention mechanism that uses scalar weight per GCN.", "Presents a Network of Graph Convolutional Networks that uses random walk statistics to extract information from near and distant neighbors in the graph" ] }, { "id": "task668-243ab94f72ee4b119e65178030e5c5a7", "input": "Deep latent variable models have become a popular model choice due to the scalable learning algorithms introduced by (Kingma & Welling 2013, Rezende et al. 2014). These approaches maximize a variational lower bound on the intractable log likelihood of the observed data. Burda et al. (2015) introduced a multi-sample variational bound, IWAE, that is at least as tight as the standard variational lower bound and becomes increasingly tight as the number of samples increases. Counterintuitively, the typical inference network gradient estimator for the IWAE bound performs poorly as the number of samples increases (Rainforth et al. 2018, Le et al. 2018). Roeder et a. (2017) propose an improved gradient estimator, however, are unable to show it is unbiased. We show that it is in fact biased and that the bias can be estimated efficiently with a second application of the reparameterization trick. The doubly reparameterized gradient (DReG) estimator does not suffer as the number of samples increases, resolving the previously raised issues. The same idea can be used to improve many recently introduced training techniques for latent variable models. In particular, we show that this estimator reduces the variance of the IWAE gradient, the reweighted wake-sleep update (RWS) (Bornschein & Bengio 2014), and the jackknife variational inference (JVI) gradient (Nowozin 2018). Finally, we show that this computationally efficient, drop-in estimator translates to improved performance for all three objectives on several modeling tasks.", "output": [ "Doubly reparameterized gradient estimators provide unbiased variance reduction which leads to improved performance.", "Author experimentally found that the estimator of the existing work(STL) is biased and proposes to reduce the bias to improve the gradient estimator of the ELBO." ] }, { "id": "task668-dd0ded1910754ba7a9b4ebc0239f7819", "input": "In many partially observable scenarios, Reinforcement Learning (RL) agents must rely on long-term memory in order to learn an optimal policy. We demonstrate that using techniques from NLP and supervised learning fails at RL tasks due to stochasticity from the environment and from exploration. Utilizing our insights on the limitations of traditional memory methods in RL, we propose AMRL, a class of models that can learn better policies with greater sample efficiency and are resilient to noisy inputs. Specifically, our models use a standard memory module to summarize short-term context, and then aggregate all prior states from the standard model without respect to order. We show that this provides advantages both in terms of gradient decay and signal-to-noise ratio over time. Evaluating in Minecraft and maze environments that test long-term memory, we find that our model improves average return by 19% over a baseline that has the same number of parameters and by 9% over a stronger baseline that has far more parameters.", "output": [ "In Deep RL, order-invariant functions can be used in conjunction with standard memory modules to improve gradient decay and resilience to noise." ] }, { "id": "task668-4b4741fba4ba43aea7669c5d4560a561", "input": "Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns. However, the mechanisms and functional significance of these spatial representations remain largely mysterious. As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on velocity inputs. Surprisingly, we find that grid-like spatial response patterns emerge in trained networks, along with units that exhibit other spatial correlates, including border cells and band-like cells. All these different functional types of neurons have been observed experimentally. The order of the emergence of grid-like and border cells is also consistent with observations from developmental studies. Together, our results suggest that grid cells, border cells and others as observed in EC may be a natural solution for representing space efficiently given the predominant recurrent connections in the neural circuits.\n", "output": [ "To our knowledge, this is the first study to show how neural representations of space, including grid-like cells and border cells as observed in the brain, could emerge from training a recurrent neural network to perform navigation tasks." ] }, { "id": "task668-bdf40395226b4b5cb1c9288fc3d5fb06", "input": "Supervised deep learning requires a large amount of training samples with annotations (e.g. label class for classification task, pixel- or voxel-wised label map for segmentation tasks), which are expensive and time-consuming to obtain. During the training of a deep neural network, the annotated samples are fed into the network in a mini-batch way, where they are often regarded of equal importance. However, some of the samples may become less informative during training, as the magnitude of the gradient start to vanish for these samples. In the meantime, other samples of higher utility or hardness may be more demanded for the training process to proceed and require more exploitation. To address the challenges of expensive annotations and loss of sample informativeness, here we propose a novel training framework which adaptively selects informative samples that are fed to the training process. The adaptive selection or sampling is performed based on a hardness-aware strategy in the latent space constructed by a generative model. To evaluate the proposed training framework, we perform experiments on three different datasets, including MNIST and CIFAR-10 for image classification task and a medical image dataset IVUS for biophysical simulation task. On all three datasets, the proposed framework outperforms a random sampling method, which demonstrates the effectiveness of our framework.", "output": [ "This paper introduces a framework for data-efficient representation learning by adaptive sampling in latent space.", "A method for sequential and adaptive selection of training examples to be presented to the training algorithm, where selection happens in the latent space based on choosing samples in the direction of the gradient of the loss.", "A method to efficiently select hard samples during neural network training, achieved via a variational auto-encoder that encodes samples into a latent space." ] }, { "id": "task668-83765421525441d48c5206adec5e4049", "input": "In cooperative multi-agent reinforcement learning (MARL), how to design a suitable reward signal to accelerate learning and stabilize convergence is a critical problem. The global reward signal assigns the same global reward to all agents without distinguishing their contributions, while the local reward signal provides different local rewards to each agent based solely on individual behavior. Both of the two reward assignment approaches have some shortcomings: the former might encourage lazy agents, while the latter might produce selfish agents.\n\n In this paper, we study reward design problem in cooperative MARL based on packet routing environments. Firstly, we show that the above two reward signals are prone to produce suboptimal policies. Then, inspired by some observations and considerations, we design some mixed reward signals, which are off-the-shelf to learn better policies. Finally, we turn the mixed reward signals into the adaptive counterparts, which achieve best results in our experiments. Other reward signals are also discussed in this paper. As reward design is a very fundamental problem in RL and especially in MARL, we hope that MARL researchers can rethink the rewards used in their systems.", "output": [ "We study reward design problem in cooperative MARL based on packet routing environments. The experimental results remind us to be careful to design the rewards, as they are really important to guide the agent behavior." ] }, { "id": "task668-fcab1fbd95184c14a0691196da4bc8bd", "input": "One form of characterizing the expressiveness of a piecewise linear neural network is by the number of linear regions, or pieces, of the function modeled. We have observed substantial progress in this topic through lower and upper bounds on the maximum number of linear regions and a counting procedure. However, these bounds only account for the dimensions of the network and the exact counting may take a prohibitive amount of time, therefore making it infeasible to benchmark the expressiveness of networks. In this work, we approximate the number of linear regions of specific rectifier networks with an algorithm for probabilistic lower bounds of mixed-integer linear sets. In addition, we present a tighter upper bound that leverages network coefficients. We test both on trained networks. The algorithm for probabilistic lower bounds is several orders of magnitude faster than exact counting and the values reach similar orders of magnitude, hence making our approach a viable method to compare the expressiveness of such networks. The refined upper bound is particularly stronger on networks with narrow layers. ", "output": [ "We provide improved upper bounds for the number of linear regions used in network expressivity, and an highly efficient algorithm (w.r.t. exact counting) to obtain probabilistic lower bounds on the actual number of linear regions.", "Contributes to the study of the number of linear regions in RELU neural networks by using an approximate probabilistic counting algorithm and analysis", "Builds off previous work studying the counting of linear regions in deep neural networks, and improves the upper bound previously proposed by changing the dimensionality constraint", "The paper deals with expressiveness of a piecewise linear neural network, characterized by the number of linear regions of the function modeled, and leverages probabilistic algorithms to compute the bounds faster, and proves tighter bounds." ] }, { "id": "task668-94280c8f6dfe4f738fb5067b4dfbdb90", "input": "Deep neural networks (DNNs) had great success on NLP tasks such as language modeling, machine translation and certain question answering (QA) tasks. However, the success is limited at more knowledge intensive tasks such as QA from a big corpus. Existing end-to-end deep QA models (Miller et al., 2016; Weston et al., 2014) need to read the entire text after observing the question, and therefore their complexity in responding a question is linear in the text size. This is prohibitive for practical tasks such as QA from Wikipedia, a novel, or the Web. We propose to solve this scalability issue by using symbolic meaning representations, which can be indexed and retrieved efficiently with complexity that is independent of the text size. More specifically, we use sequence-to-sequence models to encode knowledge symbolically and generate programs to answer questions from the encoded knowledge. We apply our approach, called the N-Gram Machine (NGM), to the bAbI tasks (Weston et al., 2015) and a special version of them (“life-long bAbI”) which has stories of up to 10 million sentences. Our experiments show that NGM can successfully solve both of these tasks accurately and efficiently. Unlike fully differentiable memory models, NGM’s time complexity and answering quality are not affected by the story length. The whole system of NGM is trained end-to-end with REINFORCE (Williams, 1992). To avoid high variance in gradient estimation, which is typical in discrete latent variable models, we use beam search instead of sampling. To tackle the exponentially large search space, we use a stabilized auto-encoding objective and a structure tweak procedure to iteratively reduce and refine the search space.\n", "output": [ "We propose a framework that learns to encode knowledge symbolically and generate programs to reason about the encoded knowledge.", "\nThe authors propose the N-Gram machine to answer questions over long documents.", "This paper presents the n-gram machine, a model that encodes sentences into simple symbolic representations which can be queried efficiently." ] }, { "id": "task668-a704218dddef413aac8ae8f5d133f02d", "input": "We introduce two approaches for conducting efficient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i.e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops. The resulting class of simulators are used extensively throughout the sciences and can be interpreted as probabilistic generative models. However, drawing inferences from them poses a substantial challenge due to the inability to evaluate even their unnormalised density, preventing the use of many standard inference procedures like Markov Chain Monte Carlo (MCMC). To address this, we introduce inference algorithms based on a two-step approach that first approximates the conditional densities of the individual sub-procedures, before using these approximations to run MCMC methods on the full program. Because the sub-procedures can be dealt with separately and are lower-dimensional than that of the overall problem, this two-step process allows them to be isolated and thus be tractably dealt with, without placing restrictions on the overall dimensionality of the problem. We demonstrate the utility of our approach on a simple, artificially constructed simulator.", "output": [ "We introduce two approaches for efficient and scalable inference in stochastic simulators for which the density cannot be evaluated directly due to, for example, rejection sampling loops." ] }, { "id": "task668-c94b85a57ce149f9a712079fce0ce0a9", "input": "In this paper, we present an approach to learn recomposable motor primitives across large-scale and diverse manipulation demonstrations. Current approaches to decomposing demonstrations into primitives often assume manually defined primitives and bypass the difficulty of discovering these primitives. On the other hand, approaches in primitive discovery put restrictive assumptions on the complexity of a primitive, which limit applicability to narrow tasks. Our approach attempts to circumvent these challenges by jointly learning both the underlying motor primitives and recomposing these primitives to form the original demonstration. Through constraints on both the parsimony of primitive decomposition and the simplicity of a given primitive, we are able to learn a diverse set of motor primitives, as well as a coherent latent representation for these primitives. We demonstrate, both qualitatively and quantitatively, that our learned primitives capture semantically meaningful aspects of a demonstration. This allows us to compose these primitives in a hierarchical reinforcement learning setup to efficiently solve robotic manipulation tasks like reaching and pushing.", "output": [ "We learn a space of motor primitives from unannotated robot demonstrations, and show these primitives are semantically meaningful and can be composed for new robot tasks." ] }, { "id": "task668-2e41a6f5056349d197d01d0ec5d79e31", "input": "It has been an open research challenge for developing an end-to-end multi-domain task-oriented dialogue system, in which a human can converse with the dialogue agent to complete tasks in more than one domain. First, tracking belief states of multi-domain dialogues is difficult as the dialogue agent must obtain the complete belief states from all relevant domains, each of which can have shared slots common among domains as well as unique slots specifically for the domain only. Second, the dialogue agent must also process various types of information, including contextual information from dialogue context, decoded dialogue states of current dialogue turn, and queried results from a knowledge base, to semantically shape context-aware and task-specific responses to human. To address these challenges, we propose an end-to-end neural architecture for task-oriented dialogues in multiple domains. We propose a novel Multi-level Neural Belief Tracker which tracks the dialogue belief states by learning signals at both slot and domain level independently. The representations are combined in a Late Fusion approach to form joint feature vectors of (domain, slot) pairs. Following recent work in end-to-end dialogue systems, we incorporate the belief tracker with generation components to address end-to-end dialogue tasks. We achieve state-of-the-art performance on the MultiWOZ2.1 benchmark with 50.91% joint goal accuracy and competitive measures in task-completion and response generation.", "output": [ "We proposed an end-to-end dialogue system with a novel multi-level dialogue state tracker and achieved consistent performance on MultiWOZ2.1 in state tracking, task completion, and response generation performance." ] }, { "id": "task668-9736d30a8f4c4a45b37b58c648c24fc6", "input": "Inferring temporally coherent data features is crucial for a large variety of learning tasks. We propose a network architecture that introduces temporal recurrent connections for the internal state of the widely used residual blocks. We demonstrate that, with these connections, convolutional neural networks can more robustly learn stable temporal states that persist between evaluations. We demonstrate their potential for inferring high-quality super-resolution images from low resolution images produced with real-time renderers. This data arises in a wide range of applications, and is particularly challenging as it contains a strongly aliased signal. Hence, the data differs substantially from the smooth inputs encountered in natural videos, and existing techniques do not succeed at producing acceptable image quality. We additionally propose a series of careful adjustments of typical generative adversarial architectures for video super-resolution to arrive at a first model that can produce detailed, yet temporally coherent images from an aliased stream of inputs from a real-time renderer.", "output": [ "A method for persistent latent states in ResBlocks demonstrated for super-resolution of alised image sequences." ] }, { "id": "task668-7a5f452671c04399a2ebcce3459aaf77", "input": "We study the error landscape of deep linear and nonlinear neural networks with the squared error loss. Minimizing the loss of a deep linear neural network is a nonconvex problem, and despite recent progress, our understanding of this loss surface is still incomplete. For deep linear networks, we present necessary and sufficient conditions for a critical point of the risk function to be a global minimum. Surprisingly, our conditions provide an efficiently checkable test for global optimality, while such tests are typically intractable in nonconvex optimization. We further extend these results to deep nonlinear neural networks and prove similar sufficient conditions for global optimality, albeit in a more limited function space setting.", "output": [ "We provide efficiently checkable necessary and sufficient conditions for global optimality in deep linear neural networks, with some initial extensions to nonlinear settings.", "The paper gives conditions for the global optimality of the loss function of deep linear neural networks", "The paper gives theoretical results regarding the existence of local minima in the objective function of deep neural networks.", "Studies some theoretical properties of deep linear networks." ] }, { "id": "task668-2d873f83733f49c9b3e8ae05e1bd3160", "input": "When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas. The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query. We present a unified framework, based on the relation-aware self-attention mechanism,to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder. On the challenging Spider dataset this framework boosts the exact match accuracy to 53.7%, compared to 47.4% for the previous state-of-the-art model unaugmented with BERT embeddings. In addition, we observe qualitative improvements in the model’s understanding of schema linking and alignment.", "output": [ "State of the art in complex text-to-SQL parsing by combining hard and soft relational reasoning in schema/question encoding." ] }, { "id": "task668-ec36b5d7915542faaf0a6e2d8bba85a0", "input": " {\\em Saliency methods} attempt to explain a deep net's decision by assigning a {\\em score} to each feature/pixel in the input, often doing this credit-assignment via the gradient of the output with respect to input. \n Recently \\citet{adebayosan} questioned the validity of many of these methods since they do not pass simple {\\em sanity checks}, which test whether the scores shift/vanish when layers of the trained net are randomized, or when the net is retrained using random labels for inputs. % for the inputs. %Surprisingly, the tested methods did not pass these checks: the explanations were relatively unchanged . \n\nWe propose a simple fix to existing saliency methods that helps them pass sanity checks, which we call {\\em competition for pixels}. This involves computing saliency maps for all possible labels in the classification task, and using a simple competition among them to identify and remove less relevant pixels from the map. Some theoretical justification is provided for it and its performance is empirically demonstrated on several popular methods.", "output": [ "We devise a mechanism called competition among pixels that allows (approximately) complete saliency methods to pass the sanity checks." ] }, { "id": "task668-ae23bd8ed9184c008ab659ca6622e038", "input": "We present a representation for describing transition models in complex uncertain domains using relational rules. For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state. An iterative greedy algorithm is used to construct a set of deictic references that determine which objects are relevant in any given state. Feed-forward neural networks are used to learn the transition distribution on the relevant objects' properties. This strategy is demonstrated to be both more versatile and more sample efficient than learning a monolithic transition model in a simulated domain in which a robot pushes stacks of objects on a cluttered table.", "output": [ "A new approach that learns a representation for describing transition models in complex uncertaindomains using relational rules. " ] }, { "id": "task668-68e8d11efb3642e58c2e6f845ffa8f35", "input": "In this work, we propose the Sparse Deep Scattering Croisé Network (SDCSN) a novel architecture based on the Deep Scattering Network (DSN). The DSN is achieved by cascading wavelet transform convolutions with a complex modulus and a time-invariant operator. We extend this work by first,\n crossing multiple wavelet family transforms to increase the feature diversity while avoiding any learning. Thus providing a more informative latent representation and benefit from the development of highly specialized wavelet filters over the last decades. Beside, by combining all the different wavelet representations, we reduce the amount of prior information needed regarding the signals at hand.\n Secondly, we develop an optimal thresholding strategy for over-complete filter banks that regularizes the network and controls instabilities such as inherent non-stationary noise in the signal. Our systematic and principled solution sparsifies the latent representation of the network by acting as a local mask distinguishing between activity and noise. Thus, we propose to enhance the DSN by increasing the variance of the scattering coefficients representation as well as improve its robustness with respect to non-stationary noise.\n We show that our new approach is more robust and outperforms the DSN on a bird detection task.", "output": [ "We propose to enhance the Deep Scattering Network in order to improve control and stability of any given machine learning pipeline by proposing a continuous wavelet thresholding scheme" ] }, { "id": "task668-fa89d26b4b444f43b3385e7e96c08223", "input": "We explore the role of multiplicative interaction as a unifying framework to describe a range of classical and modern neural network architectural motifs, such as gating, attention layers, hypernetworks, and dynamic convolutions amongst others.\n Multiplicative interaction layers as primitive operations have a long-established presence in the literature, though this often not emphasized and thus under-appreciated. We begin by showing that such layers strictly enrich the representable function classes of neural networks. We conjecture that multiplicative interactions offer a particularly powerful inductive bias when fusing multiple streams of information or when conditional computation is required. We therefore argue that they should be considered in many situation where multiple compute or information paths need to be combined, in place of the simple and oft-used concatenation operation. Finally, we back up our claims and demonstrate the potential of multiplicative interactions by applying them in large-scale complex RL and sequence modelling tasks, where their use allows us to deliver state-of-the-art results, and thereby provides new evidence in support of multiplicative interactions playing a more prominent role when designing new neural network architectures.", "output": [ "We explore the role of multiplicative interaction as a unifying framework to describe a range of classical and modern neural network architectural motifs, such as gating, attention layers, hypernetworks, and dynamic convolutions amongst others.", "Presents multiplicative interaction as a unified characterization for representing commonly used model architecture design components, showing empirical proof of superior performance on tasks like RL and sequence modeling.", "The paper explores different types of multiplicative interactions and finds MI models able to achieve a state-of-the-art performance on language modeling and reinforcement learning problems." ] }, { "id": "task668-61e29e3198b24bb88beaefd0d73462be", "input": "Recent theoretical work has demonstrated that deep neural networks have superior performance over shallow networks, but their training is more difficult, e.g., they suffer from the vanishing gradient problem. This problem can be typically resolved by the rectified linear unit (ReLU) activation. However, here we show that even for such activation, deep and narrow neural networks (NNs) will converge to erroneous mean or median states of the target function depending on the loss with high probability. Deep and narrow NNs are encountered in solving partial differential equations with high-order derivatives. We demonstrate this collapse of such NNs both numerically and theoretically, and provide estimates of the probability of collapse. We also construct a diagram of a safe region for designing NNs that avoid the collapse to erroneous states. Finally, we examine different ways of initialization and normalization that may avoid the collapse problem. Asymmetric initializations may reduce the probability of collapse but do not totally eliminate it.", "output": [ "Deep and narrow neural networks will converge to erroneous mean or median states of the target function depending on the loss with high probability.", "This paper studies failure modes of deep and narrow networks, focusing on as small as possible models for which the undesired behavior occurs.", "This paper shows that the training of deep ReLU neural networks will converge to a constant classifier with high probability over random initialization if hidden layer widths are too small." ] }, { "id": "task668-a0e9e2034ee84008897b0ee1ebc49dc3", "input": "We introduce the concept of channel aggregation in ConvNet architecture, a novel compact representation of CNN features useful for explicitly modeling the nonlinear channels encoding especially when the new unit is embedded inside of deep architectures for action recognition. The channel aggregation is based on multiple-channels features of ConvNet and aims to be at the spot finding the optical convergence path at fast speed. We name our proposed convolutional architecture “nonlinear channels aggregation networks (NCAN)” and its new layer “nonlinear channels aggregation layer (NCAL)”. We theoretically motivate channels aggregation functions and empirically study their effect on convergence speed and classification accuracy. Another contribution in this work is an efficient and effective implementation of the NCAL, speeding it up orders of magnitude. We evaluate its performance on standard benchmarks UCF101 and HMDB51, and experimental results demonstrate that this formulation not only obtains a fast convergence but stronger generalization capability without sacrificing performance.", "output": [ "An architecture enables CNN trained on the video sequences converging rapidly " ] }, { "id": "task668-cbdf086c1896469091e010631c600224", "input": "With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact. Unfortunately, negative results from game theory show there is little hope of understanding or controlling general n-player games. We therefore introduce smooth markets (SM-games), a class of n-player games with pairwise zero sum interactions. SM-games codify a common design pattern in machine learning that includes some GANs, adversarial training, and other recent algorithms. We show that SM-games are amenable to analysis and optimization using first-order methods.", "output": [ "We introduce a class of n-player games suited to gradient-based methods." ] }, { "id": "task668-23ae4b36884640ec96af6124b6899a10", "input": "Combining domain knowledge models with neural models has been challenging. End-to-end trained neural models often perform better (lower Mean Square Error) than domain knowledge models or domain/neural combinations, and the combination is inefficient to train. In this paper, we demonstrate that by composing domain models with machine learning models, by using extrapolative testing sets, and invoking decorrelation objective functions, we create models which can predict more complex systems. The models are interpretable, extrapolative, data-efficient, and capture predictable but complex non-stochastic behavior such as unmodeled degrees of freedom and systemic measurement noise. We apply this improved modeling paradigm to several simulated systems and an actual physical system in the context of system identification. Several ways of composing domain models with neural models are examined for time series, boosting, bagging, and auto-encoding on various systems of varying complexity and non-linearity. Although this work is preliminary, we show that the ability to combine models is a very promising direction for neural modeling.", "output": [ "Improved modeling of complex systems uses hybrid neural/domain model composition, new decorrelation loss functions and extrapolative test sets ", "This paper conducts experiments to compare the extrapolative predictions of various hybrid models which compose physical models, neural networks and stochastic models, and tackles the challenge of unmodeled dynamics being a bottleneck.", "This paper presents approaches for combining neural network with non-NN models to predict behavior of complex physical systems." ] }, { "id": "task668-38e7531432d043e791e3e658dd505e61", "input": "Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present an unsupervised learning algorithm to train agents to achieve perceptually-specified goals using only a stream of observations and actions. Our agent simultaneously learns a goal-conditioned policy and a goal achievement reward function that measures how similar a state is to the goal state. This dual optimization leads to a co-operative game, giving rise to a learned reward function that reflects similarity in controllable aspects of the environment instead of distance in the space of observations. We demonstrate the efficacy of our agent to learn, in an unsupervised manner, to reach a diverse set of goals on three domains -- Atari, the DeepMind Control Suite and DeepMind Lab.", "output": [ "Unsupervised reinforcement learning method for learning a policy to robustly achieve perceptually specified goals." ] }, { "id": "task668-6aae07af44bc4e5c8141339450897615", "input": "Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failure experience to a successful one by relabeling the goals. Despite its effectiveness, HER has limited applicability because it lacks a compact and universal goal representation. We present Augmenting experienCe via TeacheR's adviCE (ACTRCE), an efficient reinforcement learning technique that extends the HER framework using natural language as the goal representation. We first analyze the differences among goal representation, and show that ACTRCE can efficiently solve difficult reinforcement learning problems in challenging 3D navigation tasks, whereas HER with non-language goal representation failed to learn. We also show that with language goal representations, the agent can generalize to unseen instructions, and even generalize to instructions with unseen lexicons. We further demonstrate it is crucial to use hindsight advice to solve challenging tasks, but we also found that little amount of hindsight advice is sufficient for the learning to take off, showing the practical aspect of the method.", "output": [ "Combine language goal representation with hindsight experience replays.", "This paper considers the assumption implicit in hindsight experience replay, that there is access to a mapping from states to goals, and proposes a natural language goal representation.", "This submission uses Hindsight Experience Replay framework with natural language goals to improve the sample-efficiency of instruction-following models." ] }, { "id": "task668-647c7a69d3b74f10ad771f3133de9094", "input": "To communicate with new partners in new contexts, humans rapidly form new linguistic conventions. Recent language models trained with deep neural networks are able to comprehend and produce the existing conventions present in their training data, but are not able to flexibly and interactively adapt those conventions on the fly as humans do. We introduce a repeated reference task as a benchmark for models of adaptation in communication and propose a regularized continual learning framework that allows an artificial agent initialized with a generic language model to more accurately and efficiently understand their partner over time. We evaluate this framework through simulations on COCO and in real-time reference game experiments with human partners.", "output": [ "We propose a repeated reference benchmark task and a regularized continual learning approach for adaptive communication with humans in unfamiliar domains" ] }, { "id": "task668-8b64aa64f840482eb9698d4b626cfddf", "input": "Recent deep multi-task learning (MTL) has been witnessed its success in alleviating data scarcity of some task by utilizing domain-specific knowledge from related tasks. Nonetheless, several major issues of deep MTL, including the effectiveness of sharing mechanisms, the efficiency of model complexity and the flexibility of network architectures, still remain largely unaddressed. To this end, we propose a novel generalized latent-subspace based knowledge sharing mechanism for linking task-specific models, namely tensor ring multi-task learning (TRMTL). TRMTL has a highly compact representation, and it is very effective in transferring task-invariant knowledge while being super flexible in learning task-specific features, successfully mitigating the dilemma of both negative-transfer in lower layers and under-transfer in higher layers. Under our TRMTL, it is feasible for each task to have heterogenous input data dimensionality or distinct feature sizes at different hidden layers. Experiments on a variety of datasets demonstrate our model is capable of significantly improving each single task’s performance, particularly favourable in scenarios where some of the tasks have insufficient data.", "output": [ "a deep multi-task learning model adapting tensor ring representation", "A variant of tensor ring formulation for multi-task learning by sharing some of the TT cores for learning \"common task\" while learning individual TT cores for each separate task" ] }, { "id": "task668-8c767a4e88254036913ad3ba31d70f4b", "input": "While most approaches to the problem of Inverse Reinforcement Learning (IRL) focus on estimating a reward function that best explains an expert agent’s policy or demonstrated behavior on a control task, it is often the case that such behavior is more succinctly represented by a simple reward combined with a set of hard constraints. In this setting, the agent is attempting to maximize cumulative rewards subject to these given constraints on their behavior. We reformulate the problem of IRL on Markov Decision Processes (MDPs) such that, given a nominal model of the environment and a nominal reward function, we seek to estimate state, action, and feature constraints in the environment that motivate an agent’s behavior. Our approach is based on the Maximum Entropy IRL framework, which allows us to reason about the likelihood of an expert agent’s demonstrations given our knowledge of an MDP. Using our method, we can infer which constraints can be added to the MDP to most increase the likelihood of observing these demonstrations. We present an algorithm which iteratively infers the Maximum Likelihood Constraint to best explain observed behavior, and we evaluate its efficacy using both simulated behavior and recorded data of humans navigating around an obstacle.", "output": [ "Our method infers constraints on task execution by leveraging the principle of maximum entropy to quantify how demonstrations differ from expected, un-constrained behavior." ] }, { "id": "task668-92a13c5e757d4ac2b9548c7cd32284ee", "input": "Motivated by the flexibility of biological neural networks whose connectivity structure changes significantly during their lifetime,we introduce the Unrestricted Recursive Network (URN) and demonstrate that it can exhibit similar flexibility during training via gradient descent. We show empirically that many of the different neural network structures commonly used in practice today (including fully connected, locally connected and residual networks of differ-ent depths and widths) can emerge dynamically from the same URN.These different structures can be derived using gradient descent on a single general loss function where the structure of the data and the relative strengths of various regulator terms determine the structure of the emergent network. We show that this loss function and the regulators arise naturally when considering the symmetries of the network as well as the geometric properties of the input data.", "output": [ "We introduce a network framework which can modify its structure during training and show that it can converge to various ML network archetypes such as MLPs and LCNs. " ] }, { "id": "task668-f18e73f9b28f4c94841c2d599f4fd272", "input": "This paper introduces HybridNet, a hybrid neural network to speed-up autoregressive\n models for raw audio waveform generation. As an example, we propose\n a hybrid model that combines an autoregressive network named WaveNet and a\n conventional LSTM model to address speech synthesis. Instead of generating\n one sample per time-step, the proposed HybridNet generates multiple samples per\n time-step by exploiting the long-term memory utilization property of LSTMs. In\n the evaluation, when applied to text-to-speech, HybridNet yields state-of-art performance.\n HybridNet achieves a 3.83 subjective 5-scale mean opinion score on\n US English, largely outperforming the same size WaveNet in terms of naturalness\n and provide 2x speed up at inference.", "output": [ "It is a hybrid neural architecture to speed-up autoregressive model. ", "Concludes that in order to scale up the model size without increasing inference time for sequential prediction, use a model that predicts multiple timesteps at once.", "This paper presents HybridNet, a neural speech and other audio synthesis system that combines the WaveNet model with an LSTM with the goal of offering a model with faster inference-time audio generation." ] }, { "id": "task668-5c26fb59a42f42029a1fca400ab2093a", "input": "Rewards are sparse in the real world and most of today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more suitable for learning. In particular, inspired by curious behaviour in animals, observing something novel could be rewarded with a bonus. Such bonus is summed up with the real task reward - making it possible for RL algorithms to learn from the combined reward. We propose a new curiosity method which uses episodic memory to form the novelty bonus. To determine the bonus, the current observation is compared with the observations in memory. Crucially, the comparison is done based on how many environment steps it takes to reach the current observation from those in memory - which incorporates rich information about environment dynamics. This allows us to overcome the known \"couch-potato\" issues of prior work - when the agent finds a way to instantly gratify itself by exploiting actions which lead to hardly predictable consequences. We test our approach in visually rich 3D environments in ViZDoom, DMLab and MuJoCo. In navigational tasks from ViZDoom and DMLab, our agent outperforms the state-of-the-art curiosity method ICM. In MuJoCo, an ant equipped with our curiosity module learns locomotion out of the first-person-view curiosity only. The code is available at https://github.com/google-research/episodic-curiosity/.", "output": [ "We propose a novel model of curiosity based on episodic memory and the ideas of reachability which allows us to overcome the known \"couch-potato\" issues of prior work.", "Proposes to give exploration bonuses in RL algorithms by giving larger bonuses to observations that are father away in environment steps.", "The authors propose an exploration bonus that is aimed to aid in sparse reward RL problems and considers many experiments on complex 3D environments" ] }, { "id": "task668-2215885eb986499cac5e9a32ebd83258", "input": "Two main families of reinforcement learning algorithms, Q-learning and policy gradients, have recently been proven to be equivalent when using a softmax relaxation on one part, and an entropic regularization on the other. We relate this result to the well-known convex duality of Shannon entropy and the softmax function. Such a result is also known as the Donsker-Varadhan formula. This provides a short proof of the equivalence. We then interpret this duality further, and use ideas of convex analysis to prove a new policy inequality relative to soft Q-learning.", "output": [ "A short proof of the equivalence of soft Q-learning and policy gradients." ] }, { "id": "task668-ccde6ca75cba4204a19571e79d3bc918", "input": "The key challenge in semi-supervised learning is how to effectively leverage unlabeled data to improve learning performance. The classical label propagation method, despite its popularity, has limited modeling capability in that it only exploits graph information for making predictions. In this paper, we consider label propagation from a graph signal processing perspective and decompose it into three components: signal, filter, and classifier. By extending the three components, we propose a simple generalized label propagation (GLP) framework for semi-supervised learning. GLP naturally integrates graph and data feature information, and offers the flexibility of selecting appropriate filters and domain-specific classifiers for different applications. Interestingly, GLP also provides new insight into the popular graph convolutional network and elucidates its working mechanisms. Extensive experiments on three citation networks, one knowledge graph, and one image dataset demonstrate the efficiency and effectiveness of GLP.", "output": [ "We extend the classical label propation methods to jointly model graph and feature information from a graph filtering perspective, and show connections to the graph convlutional networks." ] }, { "id": "task668-248da2a2e3244aaeb5484ead7f4f62fe", "input": "Stochastic gradient descent (SGD), which trades off noisy gradient updates for computational efficiency, is the de-facto optimization algorithm to solve large-scale machine learning problems. SGD can make rapid learning progress by performing updates using subsampled training data, but the noisy updates also lead to slow asymptotic convergence. Several variance reduction algorithms, such as SVRG, introduce control variates to obtain a lower variance gradient estimate and faster convergence. Despite their appealing asymptotic guarantees, SVRG-like algorithms have not been widely adopted in deep learning. The traditional asymptotic analysis in stochastic optimization provides limited insight into training deep learning models under a fixed number of epochs. In this paper, we present a non-asymptotic analysis of SVRG under a noisy least squares regression problem. Our primary focus is to compare the exact loss of SVRG to that of SGD at each iteration t. We show that the learning dynamics of our regression model closely matches with that of neural networks on MNIST and CIFAR-10 for both the underparameterized and the overparameterized models. Our analysis and experimental results suggest there is a trade-off between the computational cost and the convergence speed in underparametrized neural networks. SVRG outperforms SGD after a few epochs in this regime. However, SGD is shown to always outperform SVRG in the overparameterized regime.", "output": [ "Non-asymptotic analysis of SGD and SVRG, showing the strength of each algorithm in convergence speed and computational cost, in both under-parametrized and over-parametrized settings." ] }, { "id": "task668-de7d024b4e404a6d8ba03465e6197576", "input": "The practical usage of reinforcement learning agents is often bottlenecked by the duration of training time. To accelerate training, practitioners often turn to distributed reinforcement learning architectures to parallelize and accelerate the training process. However, modern methods for scalable reinforcement learning (RL) often tradeoff between the throughput of samples that an RL agent can learn from (sample throughput) and the quality of learning from each sample (sample efficiency). In these scalable RL architectures, as one increases sample throughput (i.e. increasing parallelization in IMPALA (Espeholt et al., 2018)), sample efficiency drops significantly. To address this, we propose a new distributed reinforcement learning algorithm, IMPACT. IMPACT extends PPO with three changes: a target network for stabilizing the surrogate objective, a circular buffer, and truncated importance sampling. In discrete action-space environments, we show that IMPACT attains higher reward and, simultaneously, achieves up to 30% decrease in training wall-time than that of IMPALA. For continuous control environments, IMPACT trains faster than existing scalable agents while preserving the sample efficiency of synchronous PPO.", "output": [ "IMPACT helps RL agents train faster by decreasing training wall-clock time and increasing sample efficiency simultaneously." ] }, { "id": "task668-d0cc95880de242c0be7a42643ec4bd39", "input": "State-of-the-art performances on language comprehension tasks are achieved by huge language models pre-trained on massive unlabeled text corpora, with very light subsequent fine-tuning in a task-specific supervised manner. It seems the pre-training procedure learns a very good common initialization for further training on various natural language understanding tasks, such that only few steps need to be taken in the parameter space to learn each task. In this work, using Bidirectional Encoder Representations from Transformers (BERT) as an example, we verify this hypothesis by showing that task-specific fine-tuned language models are highly close in parameter space to the pre-trained one. Taking advantage of such observations, we further show that the fine-tuned versions of these huge models, having on the order of $10^8$ floating-point parameters, can be made very computationally efficient. First, fine-tuning only a fraction of critical layers suffices. Second, fine-tuning can be adequately performed by learning a binary multiplicative mask on pre-trained weights, \\textit{i.e.} by parameter-sparsification. As a result, with a single effort, we achieve three desired outcomes: (1) learning to perform specific tasks, (2) saving memory by storing only binary masks of certain layers for each task, and (3) saving compute on appropriate hardware by performing sparse operations with model parameters. ", "output": [ "Sparsification as fine-tuning of language models" ] }, { "id": "task668-9730567078de45a6a9cc4a6c4a3cef48", "input": "Sequential decision problems for real-world applications often need to be solved in real-time, requiring algorithms to perform well with a restricted computational budget. Width-based lookaheads have shown state-of-the-art performance in classical planning problems as well as over the Atari games with tight budgets. In this work we investigate width-based lookaheads over Stochastic Shortest paths (SSP). We analyse why width-based algorithms perform poorly over SSP problems, and overcome these pitfalls proposing a method to estimate costs-to-go. We formalize width-based lookaheads as an instance of the rollout algorithm, give a definition of width for SSP problems and explain its sample complexity. Our experimental results over a variety of SSP benchmarks show the algorithm to outperform other state-of-the-art rollout algorithms such as UCT and RTDP.", "output": [ "We propose a new Monte Carlo Tree Search / rollout algorithm that relies on width-based search to construct a lookahead." ] }, { "id": "task668-1ef3772e6ab042fd98dfc728935d437d", "input": "One of the main challenges in applying graph convolutional neural networks on gene-interaction data is the lack of understanding of the vector space to which they belong and also the inherent difficulties involved in representing those interactions on a significantly lower dimension, viz Euclidean spaces. The challenge becomes more prevalent when dealing with various types of heterogeneous data. We introduce a systematic, generalized method, called iSOM-GSN, used to transform ``multi-omic'' data with higher dimensions onto a two-dimensional grid. Afterwards, we apply a convolutional neural network to predict disease states of various types. Based on the idea of Kohonen's self-organizing map, we generate a two-dimensional grid for each sample for a given set of genes that represent a gene similarity network. We have tested the model to predict breast and prostate cancer using gene expression, DNA methylation and copy number alteration, yielding prediction accuracies in the 94-98% range for tumor stages of breast cancer and calculated Gleason scores of prostate cancer with just 11 input genes for both cases. The scheme not only outputs nearly perfect classification accuracy, but also provides an enhanced scheme for representation learning, visualization, dimensionality reduction, and interpretation of the results.", "output": [ "This paper presents a deep learning model that combines self-organizing maps and convolutional neural networks for representation learning of multi-omics data" ] }, { "id": "task668-3bf59086a48d4bddb8abe2e86f46d2f0", "input": "The complexity of large-scale neural networks can lead to poor understanding of their internal details. We show that this opaqueness provides an opportunity for adversaries to embed unintended functionalities into the network in the form of Trojan horse attacks. Our novel framework hides the existence of a malicious network within a benign transport network. Our attack is flexible, easy to execute, and difficult to detect. We prove theoretically that the malicious network's detection is computationally infeasible and demonstrate empirically that the transport network does not compromise its disguise. Our attack exposes an important, previously unknown loophole that unveils a new direction in machine learning security.", "output": [ "Parameters of a trained neural network can be permuted to produce a completely separate model for a different task, enabling the embedding of Trojan horse networks inside another network." ] }, { "id": "task668-b1886c125e974f3d8c9b8091aaf42579", "input": "This paper proposes a new model for document embedding. Existing approaches either require complex inference or use recurrent neural networks that are difficult to parallelize. We take a different route and use recent advances in language modeling to develop a convolutional neural network embedding model. This allows us to train deeper architectures that are fully parallelizable. Stacking layers together increases the receptive filed allowing each successive layer to model increasingly longer range semantic dependences within the document. Empirically we demonstrate superior results on two publicly available benchmarks. Full code will be released with the final version of this paper.", "output": [ "Convolutional neural network model for unsupervised document embedding.", "Introduces a new model for the general task of inducing document representations (embeddings) which uses a CNN architecture to improve computational efficiency.", "This paper proposes using CNNs with a skip-gram like objective as a fast way to output document embeddings" ] }, { "id": "task668-423cad411f7f4feca205e3ae3517283e", "input": "Estimating the frequencies of elements in a data stream is a fundamental task in data analysis and machine learning. The problem is typically addressed using streaming algorithms which can process very large data using limited storage. Today's streaming algorithms, however, cannot exploit patterns in their input to improve performance. We propose a new class of algorithms that automatically learn relevant patterns in the input data and use them to improve its frequency estimates. The proposed algorithms combine the benefits of machine learning with the formal guarantees available through algorithm theory. We prove that our learning-based algorithms have lower estimation errors than their non-learning counterparts. We also evaluate our algorithms on two real-world datasets and demonstrate empirically their performance gains.", "output": [ "Data stream algorithms can be improved using deep learning, while retaining performance guarantees." ] }, { "id": "task668-275d61e108fa41d58fd8162ae6ed1a85", "input": "Deep learning models for graphs have advanced the state of the art on many tasks. Despite their recent success, little is known about their robustness. We investigate training time attacks on graph neural networks for node classification that perturb the discrete graph structure. Our core principle is to use meta-gradients to solve the bilevel problem underlying training-time attacks, essentially treating the graph as a hyperparameter to optimize. Our experiments show that small graph perturbations consistently lead to a strong decrease in performance for graph convolutional networks, and even transfer to unsupervised embeddings. Remarkably, the perturbations created by our algorithm can misguide the graph neural networks such that they perform worse than a simple baseline that ignores all relational information. Our attacks do not assume any knowledge about or access to the target classifiers.", "output": [ "We use meta-gradients to attack the training procedure of deep neural networks for graphs.", "Studies the problem of learning a better poisoned graph parameters that can maximize the loss of a graph neural network. ", "An algorithm to alter graph structure by adding/deleting edges so as to degrade the global performance of node classification, and the idea to use meta-learning to solve the bilevel optimization problem." ] }, { "id": "task668-69596ed840124622a394fa83a2c38743", "input": "This paper presents the Variation Network (VarNet), a generative model providing means to manipulate the high-level attributes of a given input. The originality of our approach is that VarNet is not only capable of handling pre-defined attributes but can also learn the relevant attributes of the dataset by itself. These two settings can be easily combined which makes VarNet applicable for a wide variety of tasks. Further, VarNet has a sound probabilistic interpretation which grants us with a novel way to navigate in the latent spaces as well as means to control how the attributes are learned. We demonstrate experimentally that this model is capable of performing interesting input manipulation and that the learned attributes are relevant and interpretable.", "output": [ "The Variation Network is a generative model able to learn high-level attributes without supervision that can then be used for controlled input manipulation." ] }, { "id": "task668-f073daf9437848878cee54906e71620e", "input": "The impressive lifelong learning in animal brains is primarily enabled by plastic changes in synaptic connectivity. Importantly, these changes are not passive, but are actively controlled by neuromodulation, which is itself under the control of the brain. The resulting self-modifying abilities of the brain play an important role in learning and adaptation, and are a major basis for biological reinforcement learning. Here we show for the first time that artificial neural networks with such neuromodulated plasticity can be trained with gradient descent. Extending previous work on differentiable Hebbian plasticity, we propose a differentiable formulation for the neuromodulation of plasticity. We show that neuromodulated plasticity improves the performance of neural networks on both reinforcement learning and supervised learning tasks. In one task, neuromodulated plastic LSTMs with millions of parameters outperform standard LSTMs on a benchmark language modeling task (controlling for the number of parameters). We conclude that differentiable neuromodulation of plasticity offers a powerful new framework for training neural networks.", "output": [ "Neural networks can be trained to modify their own connectivity, improving their online learning performance on challenging tasks." ] }, { "id": "task668-40b98839281a4c798c4b350ade4800cb", "input": "The large memory requirements of deep neural networks strain the capabilities of many devices, limiting their deployment and adoption. Model compression methods effectively reduce the memory requirements of these models, usually through applying transformations such as weight pruning or quantization. In this paper, we present a novel scheme for lossy weight encoding which complements conventional compression techniques. The encoding is based on the Bloomier filter, a probabilistic data structure that can save space at the cost of introducing random errors. Leveraging the ability of neural networks to tolerate these imperfections and by re-training around the errors, the proposed technique, Weightless, can compress DNN weights by up to 496x; with the same model accuracy, this results in up to a 1.51x improvement over the state-of-the-art.", "output": [ "We propose a new way to compress neural networks using probabilistic data structures." ] }, { "id": "task668-9575a44533bf4ff3a314c1ec1837a4fe", "input": "We outline new approaches to incorporate ideas from deep learning into wave-based least-squares imaging. The aim, and main contribution of this work, is the combination of handcrafted constraints with deep convolutional neural networks, as a way to harness their remarkable ease of generating natural images. The mathematical basis underlying our method is the expectation-maximization framework, where data are divided in batches and coupled to additional \"latent\" unknowns. These unknowns are pairs of elements from the original unknown space (but now coupled to a specific data batch) and network inputs. In this setting, the neural network controls the similarity between these additional parameters, acting as a \"center\" variable. The resulting problem amounts to a maximum-likelihood estimation of the network parameters when the augmented data model is marginalized over the latent variables.", "output": [ "We combine hard handcrafted constraints with a deep prior weak constraint to perform seismic imaging and reap information on the \"posterior\" distribution leveraging multiplicity in the data." ] }, { "id": "task668-0a9f59f340f343c0823fd4d98d85ddd0", "input": "Variational Bayesian neural networks (BNN) perform variational inference over weights, but it is difficult to specify meaningful priors and approximating posteriors in a high-dimensional weight space. We introduce functional variational Bayesian neural networks (fBNNs), which maximize an Evidence Lower BOund (ELBO) defined directly on stochastic processes, i.e. distributions over functions. We prove that the KL divergence between stochastic processes is equal to the supremum of marginal KL divergences over all finite sets of inputs. Based on this, we introduce a practical training objective which approximates the functional ELBO using finite measurement sets and the spectral Stein gradient estimator. With fBNNs, we can specify priors which entail rich structure, including Gaussian processes and implicit stochastic processes. Empirically, we find that fBNNs extrapolate well using various structured priors, provide reliable uncertainty estimates, and can scale to large datasets.", "output": [ "We perform functional variational inference on the stochastic processes defined by Bayesian neural networks.", "Fitting of variational Bayesian Neural Network approximations in functional form and considering matching to a stochastic process prior implicitly via samples.", "Presents a novel ELBO objective for training BNNs which allows for more meaningful priors to be encoded in the model rather than the less informative weight priors features in the literature.", "Presents a new variational inference algorithm for Bayesian neural network models where the prior is specified functionally rather than via a prior over weights. " ] }, { "id": "task668-abb7d786c262425b8298d2d83e498e48", "input": "The ability to design biological structures such as DNA or proteins would have considerable medical and industrial impact. Doing so presents a challenging black-box optimization problem characterized by the large-batch, low round setting due to the need for labor-intensive wet lab evaluations. In response, we propose using reinforcement learning (RL) based on proximal-policy optimization (PPO) for biological sequence design. RL provides a flexible framework for optimization generative sequence models to achieve specific criteria, such as diversity among the high-quality sequences discovered. We propose a model-based variant of PPO, DyNA-PPO, to improve sample efficiency, where the policy for a new round is trained offline using a simulator fit on functional measurements from prior rounds. To accommodate the growing number of observations across rounds, the simulator model is automatically selected at each round from a pool of diverse models of varying capacity. On the tasks of designing DNA transcription factor binding sites, designing antimicrobial proteins, and optimizing the energy of Ising models based on protein structure, we find that DyNA-PPO performs significantly better than existing methods in settings in which modeling is feasible, while still not performing worse in situations in which a reliable model cannot be learned.", "output": [ "We augment model-free policy learning with a sequence-level surrogate reward functions and count-based visitation bonus and demonstrate effectiveness in the large batch, low-round regime seen in designing DNA and protein sequences." ] }, { "id": "task668-2d5cf274895a4068bf554350ab7412c3", "input": " Reinforcement learning (RL) has led to increasingly complex looking behavior in recent years. However, such complexity can be misleading and hides over-fitting. We find that visual representations may be a useful metric of complexity, and both correlates well objective optimization and causally effects reward optimization. We then propose curious representation learning (CRL) which allows us to use better visual representation learning algorithms to correspondingly increase visual representation in policy through an intrinsic objective on both simulated environments and transfer to real images. Finally, we show better visual representations induced by CRL allows us to obtain better performance on Atari without any reward than other curiosity objectives.", "output": [ "We present a formulation of curiosity as a visual representation learning problem and show that it allows good visual representations in agents.", "This paper formulates curiosity based RL training as learning a visual representation model, arguing that focusing on better LR and maximising model loss for novel scenes will get better overall performance." ] }, { "id": "task668-5567e42a762248f48c756c6fb747a364", "input": "Temporal Difference learning with function approximation has been widely used recently and has led to several successful results. However, compared with the original tabular-based methods, one major drawback of temporal difference learning with neural networks and other function approximators is that they tend to over-generalize across temporally successive states, resulting in slow convergence and even instability. In this work, we propose a novel TD learning method, Hadamard product Regularized TD (HR-TD), that reduces over-generalization and thus leads to faster convergence. This approach can be easily applied to both linear and nonlinear function approximators. \n HR-TD is evaluated on several linear and nonlinear benchmark domains, where we show improvement in learning behavior and performance.", "output": [ "A regularization technique for TD learning that avoids temporal over-generalization, especially in Deep Networks", "A variation on temporal difference learning for the function approximation case that attempts to resolve the issue of over-generalization across temporally-successive states.", "The paper introduces HR-TD, a variation of the TD(0) algorithm, meant to improve the over-generalization problem in conventional TD" ] }, { "id": "task668-beffcec25b3f475c8ab7ed1eb6e8d407", "input": "Recent work has focused on combining kernel methods and deep learning. With this in mind, we introduce Deepström networks -- a new architecture of neural networks which we use to replace top dense layers of standard convolutional architectures with an approximation of a kernel function by relying on the Nyström approximation. \n Our approach is easy highly flexible. It is compatible with any kernel function and it allows exploiting multiple kernels. \n We show that Deepström networks reach state-of-the-art performance on standard datasets like SVHN and CIFAR100. One benefit of the method lies in its limited number of learnable parameters which make it particularly suited for small training set sizes, e.g. from 5 to 20 samples per class. Finally we illustrate two ways of using multiple kernels, including a multiple Deepström setting, that exploits a kernel on each feature map output by the convolutional part of the model. ", "output": [ "A new neural architecture where top dense layers of standard convolutional architectures are replaced with an approximation of a kernel function by relying on the Nyström approximation." ] }, { "id": "task668-458917097ecf4040b7f25be147c6875b", "input": "Deep learning models have outperformed traditional methods in many fields such\n as natural language processing and computer vision. However, despite their\n tremendous success, the methods of designing optimal Convolutional Neural Networks\n (CNNs) are still based on heuristics or grid search. The resulting networks\n obtained using these techniques are often overparametrized with huge computational\n and memory requirements. This paper focuses on a structured, explainable\n approach towards optimal model design that maximizes accuracy while keeping\n computational costs tractable. We propose a single-shot analysis of a trained CNN\n that uses Principal Component Analysis (PCA) to determine the number of filters\n that are doing significant transformations per layer, without the need for retraining.\n It can be interpreted as identifying the dimensionality of the hypothesis space\n under consideration. The proposed technique also helps estimate an optimal number\n of layers by looking at the expansion of dimensions as the model gets deeper.\n This analysis can be used to design an optimal structure of a given network on\n a dataset, or help to adapt a predesigned network on a new dataset. We demonstrate\n these techniques by optimizing VGG and AlexNet networks on CIFAR-10,\n CIFAR-100 and ImageNet datasets.", "output": [ "We present a single shot analysis of a trained neural network to remove redundancy and identify optimal network structure", "This paper proposes a set of heuristics for identifying a good neural network architecture, based on PCA of unit activations over the dataset", "This paper presents a framework for optimising neural networks architectures through the identification of redundant filters across layers" ] }, { "id": "task668-62d2994a19174ad096198c8285910f54", "input": "We prove, under two sufficient conditions, that idealised models can have no adversarial examples. We discuss which idealised models satisfy our conditions, and show that idealised Bayesian neural networks (BNNs) satisfy these. We continue by studying near-idealised BNNs using HMC inference, demonstrating the theoretical ideas in practice. We experiment with HMC on synthetic data derived from MNIST for which we know the ground-truth image density, showing that near-perfect epistemic uncertainty correlates to density under image manifold, and that adversarial images lie off the manifold in our setting. This suggests why MC dropout, which can be seen as performing approximate inference, has been observed to be an effective defence against adversarial examples in practice; We highlight failure-cases of non-idealised BNNs relying on dropout, suggesting a new attack for dropout models and a new defence as well. Lastly, we demonstrate the defence on a cats-vs-dogs image classification task with a VGG13 variant.", "output": [ "We prove that idealised Bayesian neural networks can have no adversarial examples, and give empirical evidence with real-world BNNs.", "The paper studies the adversarial robustness of Bayesian classifiers and state two conditions that they show are provably sufficient for \"idealised models\" on \"idealised datasets\" to not have adversarial examples", "Paper posit a class of discriminative BAyesian classifiers that do not have any adversarial examples." ] }, { "id": "task668-fba8812191534afbb423b5e799ccaa0e", "input": "Sample inefficiency is a long-lasting problem in reinforcement learning (RL). The state-of-the-art uses action value function to derive policy while it usually involves an extensive search over the state-action space and unstable optimization. Towards the sample-efficient RL, we propose ranking policy gradient (RPG), a policy gradient method that learns the optimal rank of a set of discrete actions. To accelerate the learning of policy gradient methods, we establish the equivalence between maximizing the lower bound of return and imitating a near-optimal policy without accessing any oracles. These results lead to a general off-policy learning framework, which preserves the optimality, reduces variance, and improves the sample-efficiency. We conduct extensive experiments showing that when consolidating with the off-policy learning framework, RPG substantially reduces the sample complexity, comparing to the state-of-the-art.", "output": [ "We propose ranking policy gradient that learns the optimal rank of actions to maximize return. We propose a general off-policy learning framework with the properties of optimality preserving, variance reduction, and sample-efficiency.", "This paper proposes to reparameterize the policy using a form of ranking to convert the RL problem into a supervised learning problem.", "This paper presents a new view on policy gradient methods from the perspective of ranking. " ] }, { "id": "task668-dbc151ed92374c9e8a79fc9f7a7815ed", "input": "We present a simple idea that allows to record a speaker in a given language and synthesize their voice in other languages that they may not even know. These techniques open a wide range of potential applications such as cross-language communication, language learning or automatic video dubbing. We call this general problem multi-language speaker-conditioned speech synthesis and we present a simple but strong baseline for it.\n\n Our model architecture is similar to the encoder-decoder Char2Wav model or Tacotron. The main difference is that, instead of conditioning on characters or phonemes that are specific to a given language, we condition on a shared phonetic representation that is universal to all languages. This cross-language phonetic representation of text allows to synthesize speech in any language while preserving the vocal characteristics of the original speaker. Furthermore, we show that fine-tuning the weights of our model allows us to extend our results to speakers outside of the training dataset.", "output": [ "We present a simple idea that allows to record a speaker in a given language and synthesize their voice in other languages that they may not even know." ] }, { "id": "task668-299f590bc32d4c8d9b8c6514616d16a9", "input": "In this work we introduce a new framework for performing temporal predictions\n in the presence of uncertainty. It is based on a simple idea of disentangling com-\n ponents of the future state which are predictable from those which are inherently\n unpredictable, and encoding the unpredictable components into a low-dimensional\n latent variable which is fed into the forward model. Our method uses a simple su-\n pervised training objective which is fast and easy to train. We evaluate it in the\n context of video prediction on multiple datasets and show that it is able to consi-\n tently generate diverse predictions without the need for alternating minimization\n over a latent space or adversarial training.", "output": [ "A simple and easy to train method for multimodal prediction in time series. ", "This paper introduces a times-series prediction model that that learns a deterministic mapping and trains another net to predict future frames given the input and residual error from the first network.", "The paper proposes a model for prediction under uncertainty where they separate out deterministic component prediction and uncertain component prediction." ] }, { "id": "task668-2497ec2a08914f15b9c2c911bb8e49ee", "input": "We propose an active learning algorithmic architecture, capable of organizing its learning process in order to achieve a field of complex tasks by learning sequences of primitive motor policies : Socially Guided Intrinsic Motivation with Procedure Babbling (SGIM-PB). The learner can generalize over its experience to continuously learn new outcomes, by choosing actively what and how to learn guided by empirical measures of its own progress. In this paper, we are considering the learning of a set of interrelated complex outcomes hierarchically organized.\n\n We introduce a new framework called \"procedures\", which enables the autonomous discovery of how to combine previously learned skills in order to learn increasingly more complex motor policies (combinations of primitive motor policies). Our architecture can actively decide which outcome to focus on and which exploration strategy to apply. Those strategies could be autonomous exploration, or active social guidance, where it relies on the expertise of a human teacher providing demonstrations at the learner's request. We show on a simulated environment that our new architecture is capable of tackling the learning of complex motor policies, to adapt the complexity of its policies to the task at hand. We also show that our \"procedures\" increases the agent's capability to learn complex tasks.", "output": [ "The paper describes a strategic intrinsically motivated learning algorithm which tackles the learning of complex motor policies." ] }, { "id": "task668-02dc8841ec924b70884daedb8110cd31", "input": "Existing multi-agent reinforcement learning (MARL) communication methods have relied on a trusted third party (TTP) to distribute reward to agents, leaving them inapplicable in peer-to-peer environments. This paper proposes reward distribution using {\\em Neuron as an Agent} (NaaA) in MARL without a TTP with two key ideas: (i) inter-agent reward distribution and (ii) auction theory. Auction theory is introduced because inter-agent reward distribution is insufficient for optimization. Agents in NaaA maximize their profits (the difference between reward and cost) and, as a theoretical result, the auction mechanism is shown to have agents autonomously evaluate counterfactual returns as the values of other agents. NaaA enables representation trades in peer-to-peer environments, ultimately regarding unit in neural networks as agents. Finally, numerical experiments (a single-agent environment from OpenAI Gym and a multi-agent environment from ViZDoom) confirm that NaaA framework optimization leads to better performance in reinforcement learning.", "output": [ "Neuron as an Agent (NaaA) enable us to train multi-agent communication without a trusted third party." ] }, { "id": "task668-3d214fd016d8407ba72cd2b87e18ef7d", "input": "Robustness verification that aims to formally certify the prediction behavior of neural networks has become an important tool for understanding the behavior of a given model and for obtaining safety guarantees. However, previous methods are usually limited to relatively simple neural networks. In this paper, we consider the robustness verification problem for Transformers. Transformers have complex self-attention layers that pose many challenges for verification, including cross-nonlinearity and cross-position dependency, which have not been discussed in previous work. We resolve these challenges and develop the first verification algorithm for Transformers. The certified robustness bounds computed by our method are significantly tighter than those by naive Interval Bound Propagation. These bounds also shed light on interpreting Transformers as they consistently reflect the importance of words in sentiment analysis.", "output": [ "We propose the first algorithm for verifying the robustness of Transformers." ] }, { "id": "task668-1cc799a865bb423c9d5bfc0ce9c976f1", "input": "The cost of annotating training data has traditionally been a bottleneck for supervised learning approaches. The problem is further exacerbated when supervised learning is applied to a number of correlated tasks simultaneously since the amount of labels required scales with the number of tasks. To mitigate this concern, we propose an active multitask learning algorithm that achieves knowledge transfer between tasks. The approach forms a so-called committee for each task that jointly makes decisions and directly shares data across similar tasks. Our approach reduces the number of queries needed during training while maintaining high accuracy on test data. Empirical results on benchmark datasets show significant improvements on both accuracy and number of query requests.", "output": [ "We propose an active multitask learning algorithm that achieves knowledge transfer between tasks." ] }, { "id": "task668-942d55542b8c4ac1b5b4b5cc9368500b", "input": "Existing neural networks are vulnerable to \"adversarial examples\"---created by adding maliciously designed small perturbations in inputs to induce a misclassification by the networks. The most investigated defense strategy is adversarial training which augments training data with adversarial examples. However, applying single-step adversaries in adversarial training does not support the robustness of the networks, instead, they will even make the networks to be overfitted. In contrast to the single-step, multi-step training results in the state-of-the-art performance on MNIST and CIFAR10, yet it needs a massive amount of time. Therefore, we propose a method, Stochastic Quantized Activation (SQA) that solves overfitting problems in single-step adversarial training and fastly achieves the robustness comparable to the multi-step. SQA attenuates the adversarial effects by providing random selectivity to activation functions and allows the network to learn robustness with only single-step training. Throughout the experiment, our method demonstrates the state-of-the-art robustness against one of the strongest white-box attacks as PGD training, but with much less computational cost. Finally, we visualize the learning process of the network with SQA to handle strong adversaries, which is different from existing methods.", "output": [ "This paper proposes Stochastic Quantized Activation that solves overfitting problems in FGSM adversarial training and fastly achieves the robustness comparable to multi-step training.", "The paper proposes a model to improve adversarial training by introducing random perturbations in the activations of one of the hidden layers" ] }, { "id": "task668-05bce8e94c2740a2831ce6176c013925", "input": "Activation is a nonlinearity function that plays a predominant role in the convergence and performance of deep neural networks. While Rectified Linear Unit (ReLU) is the most successful activation function, its derivatives have shown superior performance on benchmark datasets. In this work, we explore the polynomials as activation functions (order ≥ 2) that can approximate continuous real valued function within a given interval. Leveraging this property, the main idea is to learn the nonlinearity, accepting that the ensuing function may not be monotonic. While having the ability to learn more suitable nonlinearity, we cannot ignore the fact that it is a challenge to achieve stable performance due to exploding gradients - which is prominent with the increase in order. To handle this issue, we introduce dynamic input scaling, output scaling, and lower learning rate for the polynomial weights. Moreover, lower learning rate will control the abrupt fluctuations of the polynomials between weight updates. In experiments on three public datasets, our proposed method matches the performance of prior activation functions, thus providing insight into a network’s nonlinearity preference.", "output": [ "We propose polynomial as activation functions.", "The authors introduce learnable activation functions that are parameterized by polynomial functions and show results slightly better than ReLU." ] }, { "id": "task668-52a52a18b19e4728bb7b7d5dbe34c0e0", "input": "Exploration in sparse reward reinforcement learning remains an open challenge. Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward signal, giving the agent more opportunities to receive feedback during exploration. Commonly these signals are added as bonus rewards, which results in a mixture policy that neither conducts exploration nor task fulfillment resolutely.\n In this paper, we instead learn separate intrinsic and extrinsic task policies and schedule between these different drives to accelerate exploration and stabilize learning. Moreover, we introduce a new type of intrinsic reward denoted as successor feature control (SFC), which is general and not task-specific. It takes into account statistics over complete trajectories and thus differs from previous methods that only use local information to evaluate intrinsic motivation. We evaluate our proposed scheduled intrinsic drive (SID) agent using three different environments with pure visual inputs: VizDoom, DeepMind Lab and DeepMind Control Suite. The results show a substantially improved exploration efficiency with SFC and the hierarchical usage of the intrinsic drives. A video of our experimental results can be found at https://gofile.io/?c=HpEwTd .", "output": [ "A new intrinsic reward signal based on successor features and a novel way to combine extrinsic and intrinsic reward." ] }, { "id": "task668-b602e42bec634ee2ba44bb17976faf7c", "input": "Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner where knowledge gained from previous tasks is retained and used for future learning. It is essential towards the development of intelligent machines that can adapt to their surroundings. In this work we focus on a lifelong learning approach to generative modeling where we continuously incorporate newly observed streaming distributions into our learnt model. We do so through a student-teacher architecture which allows us to learn and preserve all the distributions seen so far without the need to retain the past data nor the past models. Through the introduction of a novel cross-model regularizer, the student model leverages the information learnt by the teacher, which acts as a summary of everything seen till now. The regularizer has the additional benefit of reducing the effect of catastrophic interference that appears when we learn over streaming data. We demonstrate its efficacy on streaming distributions as well as its ability to learn a common latent representation across a complex transfer learning scenario.\n", "output": [ "Lifelong distributional learning through a student-teacher architecture coupled with a cross model posterior regularizer." ] }, { "id": "task668-3b3bfa1727c34b30b149e7c3b16ec0ab", "input": "This paper proposes variational domain adaptation, a unified, scalable, simple framework for learning multiple distributions through variational inference. Unlike the existing methods on domain transfer through deep generative models, such as StarGAN (Choi et al., 2017) and UFDN (Liu et al., 2018), the variational domain adaptation has three advantages. Firstly, the samples from the target are not required. Instead, the framework requires one known source as a prior $p(x)$ and binary discriminators, $p(\\mathcal{D}_i|x)$, discriminating the target domain $\\mathcal{D}_i$ from others. Consequently, the framework regards a target as a posterior that can be explicitly formulated through the Bayesian inference, $p(x|\\mathcal{D}_i) \\propto p(\\mathcal{D}_i|x)p(x)$, as exhibited by a further proposed model of dual variational autoencoder (DualVAE). Secondly, the framework is scablable to large-scale domains. As well as VAE encodes a sample $x$ as a mode on a latent space: $\\mu(x) \\in \\mathcal{Z}$, DualVAE encodes a domain $\\mathcal{D}_i$ as a mode on the dual latent space $\\mu^*(\\mathcal{D}_i) \\in \\mathcal{Z}^*$, named domain embedding. It reformulates the posterior with a natural paring $\\langle, \\rangle: \\mathcal{Z} \\times \\mathcal{Z}^* \\rightarrow \\Real$, which can be expanded to uncountable infinite domains such as continuous domains as well as interpolation. Thirdly, DualVAE fastly converges without sophisticated automatic/manual hyperparameter search in comparison to GANs as it requires only one additional parameter to VAE. Through the numerical experiment, we demonstrate the three benefits with multi-domain image generation task on CelebA with up to 60 domains, and exhibits that DualVAE records the state-of-the-art performance outperforming StarGAN and UFDN.", "output": [ "This paper proposes variational domain adaptation, a unified, scalable, simple framework for learning multiple distributions through variational inference" ] }, { "id": "task668-2ff25d721bd544d0a1c07953197328a4", "input": "Achieving faster execution with shorter compilation time can foster further diversity and innovation in neural networks. However, the current paradigm of executing neural networks either relies on hand-optimized libraries, traditional compilation heuristics, or very recently genetic algorithms and other stochastic methods. These methods suffer from frequent costly hardware measurements rendering them not only too time consuming but also suboptimal. As such, we devise a solution that can learn to quickly adapt to a previously unseen design space for code optimization, both accelerating the search and improving the output performance. This solution dubbed CHAMELEON leverages reinforcement learning whose solution takes fewer steps to converge, and develops an adaptive sampling algorithm that not only focuses on the costly samples (real hardware measurements) on representative points but also uses a domain knowledge inspired logic to improve the samples itself. Experimentation with real hardware shows that CHAMELEON provides 4.45×speed up in optimization time over AutoTVM, while also improving inference time of the modern deep networks by 5.6%.", "output": [ "Reinforcement learning and Adaptive Sampling for Optimized Compilation of Deep Neural Networks." ] }, { "id": "task668-6c2038dbf93945deb35e511e90e966db", "input": "Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future. In this paper, we propose a novel formulation that simultaneously learns a hierarchical, disentangled object representation and a dynamics model for object parts from unlabeled videos. Our Parts, Structure, and Dynamics (PSD) model learns to, first, recognize the object parts via a layered image representation; second, predict hierarchy via a structural descriptor that composes low-level concepts into a hierarchical structure; and third, model the system dynamics by predicting the future. Experiments on multiple real and synthetic datasets demonstrate that our PSD model works well on all three tasks: segmenting object parts, building their hierarchical structure, and capturing their motion distributions.", "output": [ "Learning object parts, hierarchical structure, and dynamics by watching how they move", "Proposes an unsupervised learning model that learns to disentangle objects into parts, predict hierarchical structure for the parts, and based on the disentangled parts and the hierarchy, predict motion." ] }, { "id": "task668-cf0e7c841a4147cdaf13db1065950de9", "input": "Generative Adversarial Networks (GANs) are a very powerful framework for generative modeling. However, they are often hard to train, and learning of GANs often becomes unstable. Wasserstein GAN (WGAN) is a promising framework to deal with the instability problem as it has a good convergence property. One drawback of the WGAN is that it evaluates the Wasserstein distance in the dual domain, which requires some approximation, so that it may fail to optimize the true Wasserstein distance. In this paper, we propose evaluating the exact empirical optimal transport cost efficiently in the primal domain and performing gradient descent with respect to its derivative to train the generator network. Experiments on the MNIST dataset show that our method is significantly stable to converge, and achieves the lowest Wasserstein distance among the WGAN variants at the cost of some sharpness of generated images. Experiments on the 8-Gaussian toy dataset show that better gradients for the generator are obtained in our method. In addition, the proposed method enables more flexible generative modeling than WGAN.", "output": [ "We have proposed a flexible generative model that learns stably by directly minimizing exact empirical Wasserstein distance." ] }, { "id": "task668-cb4cbb34145e4334a63f17bb5c18fbff", "input": "Neural networks in the brain and in neuromorphic chips confer systems with the ability to perform multiple cognitive tasks. However, both kinds of networks experience a wide range of physical perturbations, ranging from damage to edges of the network to complete node deletions, that ultimately could lead to network failure. A critical question is to understand how the computational properties of neural networks change in response to node-damage and whether there exist strategies to repair these networks in order to compensate for performance degradation. Here, we study the damage-response characteristics of two classes of neural networks, namely multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) trained to classify images from MNIST and CIFAR-10 datasets respectively. We also propose a new framework to discover efficient repair strategies to rescue damaged neural networks. The framework involves defining damage and repair operators for dynamically traversing the neural networks loss landscape, with the goal of mapping its salient geometric features. Using this strategy, we discover features that resemble path-connected attractor sets in the loss landscape. We also identify that a dynamic recovery scheme, where networks are constantly damaged and repaired, produces a group of networks resilient to damage as it can be quickly rescued. Broadly, our work shows that we can design fault-tolerant networks by applying on-line retraining consistently during damage for real-time applications in biology and machine learning.", "output": [ "strategy to repair damaged neural networks" ] }, { "id": "task668-627f73b66a434ff1a47f2050015a838d", "input": "Stochastic Gradient Descent or SGD is the most popular optimization algorithm for large-scale problems. SGD estimates the gradient by uniform sampling with sample size one. There have been several other works that suggest faster epoch wise convergence by using weighted non-uniform sampling for better gradient estimates. Unfortunately, the per-iteration cost of maintaining this adaptive distribution for gradient estimation is more than calculating the full gradient. As a result, the false impression of faster convergence in iterations leads to slower convergence in time, which we call a chicken-and-egg loop. In this paper, we break this barrier by providing the first demonstration of a sampling scheme, which leads to superior gradient estimation, while keeping the sampling cost per iteration similar to that of the uniform sampling. Such an algorithm is possible due to the sampling view of Locality Sensitive Hashing (LSH), which came to light recently. As a consequence of superior and fast estimation, we reduce the running time of all existing gradient descent algorithms. We demonstrate the benefits of our proposal on both SGD and AdaGrad.", "output": [ "We improve the running of all existing gradient descent algorithms.", "Authors propose sampling stochastic gradients from a monotonic function proportional to gradient magnitudes by using LSH. ", "Considers SGD over an objective of the form of a sum over examples of a quadratic loss." ] }, { "id": "task668-06e6abbb517a42389fd889bb165c17ea", "input": "Convolutional networks are not aware of an object's geometric variations, which leads to inefficient utilization of model and data capacity. To overcome this issue, recent works on deformation modeling seek to spatially reconfigure the data towards a common arrangement such that semantic recognition suffers less from deformation. This is typically done by augmenting static operators with learned free-form sampling grids in the image space, dynamically tuned to the data and task for adapting the receptive field. Yet adapting the receptive field does not quite reach the actual goal -- what really matters to the network is the *effective* receptive field (ERF), which reflects how much each pixel contributes. It is thus natural to design other approaches to adapt the ERF directly during runtime. In this work, we instantiate one possible solution as Deformable Kernels (DKs), a family of novel and generic convolutional operators for handling object deformations by directly adapting the ERF while leaving the receptive field untouched. At the heart of our method is the ability to resample the original kernel space towards recovering the deformation of objects. This approach is justified with theoretical insights that the ERF is strictly determined by data sampling locations and kernel values. We implement DKs as generic drop-in replacements of rigid kernels and conduct a series of empirical studies whose results conform with our theories. Over several tasks and standard base models, our approach compares favorably against prior works that adapt during runtime. In addition, further experiments suggest a working mechanism orthogonal and complementary to previous works.", "output": [ "Don't deform your convolutions -- deform your kernels." ] }, { "id": "task668-12d5687052a44264b26793665096801a", "input": "The emergence of language in multi-agent settings is a promising research direction to ground natural language in simulated agents. If AI would be able to understand the meaning of language through its using it, it could also transfer it to other situations flexibly. That is seen as an important step towards achieving general AI. The scope of emergent communication is so far, however, still limited. It is necessary to enhance the learning possibilities for skills associated with communication to increase the emergable complexity. We took an example from human language acquisition and the importance of the empathic connection in this process. We propose an approach to introduce the notion of empathy to multi-agent deep reinforcement learning. We extend existing approaches on referential games with an auxiliary task for the speaker to predict the listener's mind change improving the learning time. Our experiments show the high potential of this architectural element by doubling the learning speed of the test setup.", "output": [ "An auxiliary prediction task can speed up learning in language emergence setups." ] }, { "id": "task668-92526f7a485a4016896a3879e51ed17e", "input": "Time series forecasting plays a crucial role in marketing, finance and many other quantitative fields. A large amount of methodologies has been developed on this topic, including ARIMA, Holt–Winters, etc. However, their performance is easily undermined by the existence of change points and anomaly points, two structures commonly observed in real data, but rarely considered in the aforementioned methods. In this paper, we propose a novel state space time series model, with the capability to capture the structure of change points and anomaly points, as well as trend and seasonality. To infer all the hidden variables, we develop a Bayesian framework, which is able to obtain distributions and forecasting intervals for time series forecasting, with provable theoretical properties. For implementation, an iterative algorithm with Markov chain Monte Carlo (MCMC), Kalman filter and Kalman smoothing is proposed. In both synthetic data and real data applications, our methodology yields a better performance in time series forecasting compared with existing methods, along with more accurate change point detection and anomaly detection.", "output": [ "We propose a novel state space time series model with the capability to capture the structure of change points and anomaly points, so that it has a better forecasting performance when there exist change points and anomalies in the time series." ] }, { "id": "task668-2448f8224011493a98644c07fdcc2523", "input": "Low-precision training is a promising way of decreasing the time and energy cost of training machine learning models.\n Previous work has analyzed low-precision training algorithms, such as low-precision stochastic gradient descent, and derived theoretical bounds on their convergence rates.\n These bounds tend to depend on the dimension of the model $d$ in that the number of bits needed to achieve a particular error bound increases as $d$ increases.\n This is undesirable because a motivating application for low-precision training is large-scale models, such as deep learning, where $d$ can be huge.\n In this paper, we prove dimension-independent bounds for low-precision training algorithms that use fixed-point arithmetic, which lets us better understand what affects the convergence of these algorithms as parameters scale.\n Our methods also generalize naturally to let us prove new convergence bounds on low-precision training with other quantization schemes, such as low-precision floating-point computation and logarithmic quantization.", "output": [ "we proved dimension-independent bounds for low-precision training algorithms", "This paper discusses conditions under which the convergence of training models with low-precision weights do not rely on model dimension." ] }, { "id": "task668-8e71fd9b75944c188eca8ce59cf75319", "input": "Neural networks trained through stochastic gradient descent (SGD) have been around for more than 30 years, but they still escape our understanding. This paper takes an experimental approach, with a divide-and-conquer strategy in mind: we start by studying what happens in single neurons. While being the core building block of deep neural networks, the way they encode information about the inputs and how such encodings emerge is still unknown. We report experiments providing strong evidence that hidden neurons behave like binary classifiers during training and testing. During training, analysis of the gradients reveals that a neuron separates two categories of inputs, which are impressively constant across training. During testing, we show that the fuzzy, binary partition described above embeds the core information used by the network for its prediction. These observations bring to light some of the core internal mechanics of deep neural networks, and have the potential to guide the next theoretical and practical developments.", "output": [ "We report experiments providing strong evidence that a neuron behaves like a binary classifier during training and testing" ] }, { "id": "task668-3f939e25617c4c5d820b416e8eaa2fcb", "input": "Knowledge bases (KB) are often represented as a collection of facts in the form (HEAD, PREDICATE, TAIL), where HEAD and TAIL are entities while PREDICATE is a binary relationship that links the two. It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction. However, though frequently ignored, these repositories also contain numerical facts. Numerical facts link entities to numerical values via numerical predicates; e.g., (PARIS, LATITUDE, 48.8). Likewise, numerical facts also suffer from the incompleteness problem. To address this issue, we introduce the numerical attribute prediction problem. This problem involves a new type of query where the relationship is a numerical predicate. Consequently, and contrary to link prediction, the answer to this query is a numerical value. We argue that the numerical values associated with entities explain, to some extent, the relational structure of the knowledge base. Therefore, we leverage knowledge base embedding methods to learn representations that are useful predictors for the numerical attributes. An extensive set of experiments on benchmark versions of FREEBASE and YAGO show that our approaches largely outperform sensible baselines. We make the datasets available under a permissive BSD-3 license.", "output": [ "Prediction of numerical attribute values associated with entities in knowledge bases." ] }, { "id": "task668-7a90373843f04da69d13f9d8d6d2ddaa", "input": "Generative Adversarial Networks (GANs) have shown impressive results in modeling distributions over complicated manifolds such as those of natural images. However, GANs often suffer from mode collapse, which means they are prone to characterize only a single or a few modes of the data distribution. In order to address this problem, we propose a novel framework called LDMGAN. We first introduce Latent Distribution Matching (LDM) constraint which regularizes the generator by aligning distribution of generated samples with that of real samples in latent space. To make use of such latent space, we propose a regularized AutoEncoder (AE) that maps the data distribution to prior distribution in encoded space. Extensive experiments on synthetic data and real world datasets show that our proposed framework significantly improves GAN’s stability and diversity.", "output": [ "We propose an AE-based GAN that alleviates mode collapse in GANs." ] }, { "id": "task668-8870a3cd1b944c2e83759bbf8b3e35ec", "input": "Reinforcement learning provides a powerful and general framework for decision\n making and control, but its application in practice is often hindered by the need\n for extensive feature and reward engineering. Deep reinforcement learning methods\n can remove the need for explicit engineering of policy or value features, but\n still require a manually specified reward function. Inverse reinforcement learning\n holds the promise of automatic reward acquisition, but has proven exceptionally\n difficult to apply to large, high-dimensional problems with unknown dynamics. In\n this work, we propose AIRL, a practical and scalable inverse reinforcement learning\n algorithm based on an adversarial reward learning formulation that is competitive\n with direct imitation learning algorithms. Additionally, we show that AIRL is\n able to recover portable reward functions that are robust to changes in dynamics,\n enabling us to learn policies even under significant variation in the environment\n seen during training.", "output": [ "We propose an adversarial inverse reinforcement learning algorithm capable of learning reward functions which can transfer to new, unseen environments." ] }, { "id": "task668-d5ad2fe5136048df9e6d49ebe3fa87e5", "input": "Neural networks powered with external memory simulate computer behaviors. These models, which use the memory to store data for a neural controller, can learn algorithms and other complex tasks. In this paper, we introduce a new memory to store weights for the controller, analogous to the stored-program memory in modern computer architectures. The proposed model, dubbed Neural Stored-program Memory, augments current memory-augmented neural networks, creating differentiable machines that can switch programs through time, adapt to variable contexts and thus fully resemble the Universal Turing Machine. A wide range of experiments demonstrate that the resulting machines not only excel in classical algorithmic problems, but also have potential for compositional, continual, few-shot learning and question-answering tasks.", "output": [ "A neural simulation of Universal Turing Machine" ] }, { "id": "task668-30bba21f6f9b448893f442595ec0bcac", "input": "Recent evidence shows that convolutional neural networks (CNNs) are biased towards textures so that CNNs are non-robust to adversarial perturbations over textures, while traditional robust visual features like SIFT (scale-invariant feature transforms) are designed to be robust across a substantial range of affine distortion, addition of noise, etc with the mimic of human perception nature. This paper aims to leverage good properties of SIFT to renovate CNN architectures towards better accuracy and robustness. We borrow the scale-space extreme value idea from SIFT, and propose EVPNet (extreme value preserving network) which contains three novel components to model the extreme values: (1) parametric differences of Gaussian (DoG) to extract extrema, (2) truncated ReLU to suppress non-stable extrema and (3) projected normalization layer (PNL) to mimic PCA-SIFT like feature normalization. Experiments demonstrate that EVPNets can achieve similar or better accuracy than conventional CNNs, while achieving much better robustness on a set of adversarial attacks (FGSM,PGD,etc) even without adversarial training.", "output": [ "This paper aims to leverage good properties of robust visual features like SIFT to renovate CNN architectures towards better accuracy and robustness." ] }, { "id": "task668-16a1ab5eda62412896166d72e66a9745", "input": "The tasks that an agent will need to solve often aren’t known during training. However, if the agent knows which properties of the environment we consider im- portant, then after learning how its actions affect those properties the agent may be able to use this knowledge to solve complex tasks without training specifi- cally for them. Towards this end, we consider a setup in which an environment is augmented with a set of user defined attributes that parameterize the features of interest. We propose a model that learns a policy for transitioning between “nearby” sets of attributes, and maintains a graph of possible transitions. Given a task at test time that can be expressed in terms of a target set of attributes, and a current state, our model infers the attributes of the current state and searches over paths through attribute space to get a high level plan, and then uses its low level policy to execute the plan. We show in grid-world games and 3D block stacking that our model is able to generalize to longer, more complex tasks at test time even when it only sees short, simple tasks at train time.\n", "output": [ "Compositional attribute-based planning that generalizes to long test tasks, despite being trained on short & simple tasks." ] }, { "id": "task668-76c8e345b9ed4e3db9f37ace8fc06738", "input": "We propose a neural clustering model that jointly learns both latent features and how they cluster. Unlike similar methods our model does not require a predefined number of clusters. Using a supervised approach, we agglomerate latent features towards randomly sampled targets within the same space whilst progressively removing the targets until we are left with only targets which represent cluster centroids. To show the behavior of our model across different modalities we apply our model on both text and image data and very competitive results on MNIST. Finally, we also provide results against baseline models for fashion-MNIST, the 20 newsgroups dataset, and a Twitter dataset we ourselves create.", "output": [ "Neural clustering without needing a number of clusters" ] }, { "id": "task668-8481e4e2d98c41cc8900134f49abd08d", "input": "The Convolutional Neural Network (CNN) has been successfully applied in many fields during recent decades; however it lacks the ability to utilize prior domain knowledge when dealing with many realistic problems. We present a framework called Geometric Operator Convolutional Neural Network (GO-CNN) that uses domain knowledge, wherein the kernel of the first convolutional layer is replaced with a kernel generated by a geometric operator function. This framework integrates many conventional geometric operators, which allows it to adapt to a diverse range of problems. Under certain conditions, we theoretically analyze the convergence and the bound of the generalization errors between GO-CNNs and common CNNs. Although the geometric operator convolution kernels have fewer trainable parameters than common convolution kernels, the experimental results indicate that GO-CNN performs more accurately than common CNN on CIFAR-10/100. Furthermore, GO-CNN reduces dependence on the amount of training examples and enhances adversarial stability.", "output": [ "Traditional image processing algorithms are combined with Convolutional Neural Networks，a new neural network." ] }, { "id": "task668-098a751b0c7b4e02b378f05adf5a1724", "input": "In seeking for sparse and efficient neural network models, many previous works investigated on enforcing L1 or L0 regularizers to encourage weight sparsity during training. The L0 regularizer measures the parameter sparsity directly and is invariant to the scaling of parameter values. But it cannot provide useful gradients and therefore requires complex optimization techniques. The L1 regularizer is almost everywhere differentiable and can be easily optimized with gradient descent. Yet it is not scale-invariant and causes the same shrinking rate to all parameters, which is inefficient in increasing sparsity. Inspired by the Hoyer measure (the ratio between L1 and L2 norms) used in traditional compressed sensing problems, we present DeepHoyer, a set of sparsity-inducing regularizers that are both differentiable almost everywhere and scale-invariant. Our experiments show that enforcing DeepHoyer regularizers can produce even sparser neural network models than previous works, under the same accuracy level. We also show that DeepHoyer can be applied to both element-wise and structural pruning.", "output": [ "We propose almost everywhere differentiable and scale invariant regularizers for DNN pruning, which can lead to supremum sparsity through standard SGD training.", "The paper proposes a scale-invariant regularizer (DeepHoyer) inspired by the Hoyer measure to enforce sparsity in neural networks. " ] }, { "id": "task668-e4eb6c0f27534b7cbc3ecc356d344edd", "input": "This paper presents a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size. MINE is back-propable and we prove that it is strongly consistent. We illustrate a handful of applications in which MINE is succesfully applied to enhance the property of generative models in both unsupervised and supervised settings. We apply our framework to estimate the information bottleneck, and apply it in tasks related to supervised classification problems. Our results demonstrate substantial added flexibility and improvement in these settings.\n", "output": [ "A scalable in sample size and dimensions mutual information estimator." ] }, { "id": "task668-467a7d1a50b54a7d9961228f69e96825", "input": "One of the distinguishing aspects of human language is its compositionality, which allows us to describe complex environments with limited vocabulary. Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e.g. hand- engineered features). Humans, however, do not learn to communicate based on well-summarized features. In this work, we train neural agents to simultaneously develop visual perception from raw image pixels, and learn to communicate with a sequence of discrete symbols. The agents play an image description game where the image contains factors such as colors and shapes. We train the agents using the obverter technique where an agent introspects to generate messages that maximize its own understanding. Through qualitative analysis, visualization and a zero-shot test, we show that the agents can develop, out of raw image pixels, a language with compositional properties, given a proper pressure from the environment.", "output": [ "We train neural network agents to develop a language with compositional properties from raw pixel input." ] }, { "id": "task668-1f840fb332864458bdfc0fcd5797a138", "input": "Deep generative models have achieved remarkable progress in recent years. Despite this progress, quantitative evaluation and comparison of generative models remains as one of the important challenges. One of the most popular metrics for evaluating generative models is the log-likelihood. While the direct computation of log-likelihood can be intractable, it has been recently shown that the log-likelihood of some of the most interesting generative models such as variational autoencoders (VAE) or generative adversarial networks (GAN) can be efficiently estimated using annealed importance sampling (AIS). In this work, we argue that the log-likelihood metric by itself cannot represent all the different performance characteristics of generative models, and propose to use rate distortion curves to evaluate and compare deep generative models. We show that we can approximate the entire rate distortion curve using one single run of AIS for roughly the same computational cost as a single log-likelihood estimate. We evaluate lossy compression rates of different deep generative models such as VAEs, GANs (and its variants) and adversarial autoencoders (AAE) on MNIST and CIFAR10, and arrive at a number of insights not obtainable from log-likelihoods alone.", "output": [ "We study rate distortion approximations for evaluating deep generative models, and show that rate distortion curves provide more insights about the model than the log-likelihood alone while requiring roughly the same computational cost." ] }, { "id": "task668-9b0aea9c2f084a1ebca2ff956b310772", "input": "Many real-world sequential decision-making problems can be formulated as optimal control with high-dimensional observations and unknown dynamics. A promising approach is to embed the high-dimensional observations into a lower-dimensional latent representation space, estimate the latent dynamics model, then utilize this model for control in the latent space. An important open question is how to learn a representation that is amenable to existing control algorithms? In this paper, we focus on learning representations for locally-linear control algorithms, such as iterative LQR (iLQR). By formulating and analyzing the representation learning problem from an optimal control perspective, we establish three underlying principles that the learned representation should comprise: 1) accurate prediction in the observation space, 2) consistency between latent and observation space dynamics, and 3) low curvature in the latent space transitions. These principles naturally correspond to a loss function that consists of three terms: prediction, consistency, and curvature (PCC). Crucially, to make PCC tractable, we derive an amortized variational bound for the PCC loss function. Extensive experiments on benchmark domains demonstrate that the new variational-PCC learning algorithm benefits from significantly more stable and reproducible training, and leads to superior control performance. Further ablation studies give support to the importance of all three PCC components for learning a good latent space for control.", "output": [ "Learning embedding for control with high-dimensional observations" ] }, { "id": "task668-4415621d84214c6cb96cc3089afa8c0f", "input": "To analyze deep ReLU network, we adopt a student-teacher setting in which an over-parameterized student network learns from the output of a fixed teacher network of the same depth, with Stochastic Gradient Descent (SGD). Our contributions are two-fold. First, we prove that when the gradient is zero (or bounded above by a small constant) at every data point in training, a situation called \\emph{interpolation setting}, there exists many-to-one \\emph{alignment} between student and teacher nodes in the lowest layer under mild conditions. This suggests that generalization in unseen dataset is achievable, even the same condition often leads to zero training error. Second, analysis of noisy recovery and training dynamics in 2-layer network shows that strong teacher nodes (with large fan-out weights) are learned first and subtle teacher nodes are left unlearned until late stage of training. As a result, it could take a long time to converge into these small-gradient critical points. Our analysis shows that over-parameterization plays two roles: (1) it is a necessary condition for alignment to happen at the critical points, and (2) in training dynamics, it helps student nodes cover more teacher nodes with fewer iterations. Both improve generalization. Experiments justify our finding.", "output": [ "This paper analyzes training dynamics and critical points of training deep ReLU network via SGD in the teacher-student setting. ", "Study of over-parametrization in student-teacher multilayer ReLU networks, a theoretical part about SGD critical points for the teacher-student setting, and a heuristic and empirical part on dynamics of the SDG algorithm as a function of teacher networks." ] }, { "id": "task668-12daf8ca4e844d39bb8d82bc1852b85a", "input": "Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques. Much of the progress in open-domain QA systems has been realized through advances in information retrieval methods and corpus construction. In this paper, we focus on the recently introduced ARC Challenge dataset, which contains 2,590 multiple choice questions authored for grade-school science exams. These questions are selected to be the most challenging for current QA systems, and current state of the art performance is only slightly better than random chance. We present a system that reformulates a given question into queries that are used to retrieve supporting text from a large corpus of science-related text. Our rewriter is able to incorporate background knowledge from ConceptNet and -- in tandem with a generic textual entailment system trained on SciTail that identifies support in the retrieved results -- outperforms several strong baselines on the end-to-end QA task despite only being trained to identify essential terms in the original source question. We use a generalizable decision methodology over the retrieved evidence and answer candidates to select the best answer. By combining query reformulation, background knowledge, and textual entailment our system is able to outperform several strong baselines on the ARC dataset.", "output": [ "We explore how using background knowledge with query reformulation can help retrieve better supporting evidence when answering multiple-choice science questions." ] }, { "id": "task668-3c0dfa5548ee40e29838a8f906092d67", "input": "Reinforcement learning (RL) is a powerful technique to train an agent to perform a task. However, an agent that is trained using RL is only capable of achieving the single task that is specified via its reward function. Such an approach does not scale well to settings in which an agent needs to perform a diverse set of tasks, such as navigating to varying positions in a room or moving objects to varying locations. Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing in its environment. We use a generator network to propose tasks for the agent to try to achieve, each task being specified as reaching a certain parametrized subset of the state-space. The generator network is optimized using adversarial training to produce tasks that are always at the appropriate level of difficulty for the agent. Our method thus automatically produces a curriculum of tasks for the agent to learn. We show that, by using this framework, an agent can efficiently and automatically learn to perform a wide set of tasks without requiring any prior knowledge of its environment (Videos and code available at: https://sites.google.com/view/goalgeneration4rl). Our method can also learn to achieve tasks with sparse rewards, which pose significant challenges for traditional RL methods.", "output": [ "We efficiently solve multi-task problems with an automatic curriculum generation algorithm based on a generative model that tracks the learning agent's performance." ] }, { "id": "task668-a68cfc61f32a40b28c63feb8a6c9daaa", "input": "Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale this technique to large datasets, perturbations are crafted using fast single-step methods that maximize a linear approximation of the model's loss.\n We show that this form of adversarial training converges to a degenerate global minimum, wherein small curvature artifacts near the data points obfuscate a linear approximation of the loss. The model thus learns to generate weak perturbations, rather than defend against strong ones. As a result, we find that adversarial training remains vulnerable to black-box attacks, where we transfer perturbations computed on undefended models, as well as to a powerful novel single-step attack that escapes the non-smooth vicinity of the input data via a small random step.\n We further introduce Ensemble Adversarial Training, a technique that augments training data with perturbations transferred from other models. On ImageNet, Ensemble Adversarial Training yields models with strong robustness to black-box attacks. In particular, our most robust model won the first round of the NIPS 2017 competition on Defenses against Adversarial Attacks.", "output": [ "Adversarial training with single-step methods overfits, and remains vulnerable to simple black-box and white-box attacks. We show that including adversarial examples from multiple sources helps defend against black-box attacks." ] }, { "id": "task668-0214942c4486421ab6caa1367fae51e8", "input": "Employing deep neural networks as natural image priors to solve inverse problems either requires large amounts of data to sufficiently train expressive generative models or can succeed with no data via untrained neural networks. However, very few works have considered how to interpolate between these no- to high-data regimes. In particular, how can one use the availability of a small amount of data (even 5-25 examples) to one's advantage in solving these inverse problems and can a system's performance increase as the amount of data increases as well? In this work, we consider solving linear inverse problems when given a small number of examples of images that are drawn from the same distribution as the image of interest. Comparing to untrained neural networks that use no data, we show how one can pre-train a neural network with a few given examples to improve reconstruction results in compressed sensing and semantic image recovery problems such as colorization. Our approach leads to improved reconstruction as the amount of available data increases and is on par with fully trained generative models, while requiring less than 1% of the data needed to train a generative model.", "output": [ "We show how pre-training an untrained neural network with as few as 5-25 examples can improve reconstruction results in compressed sensing and semantic recovery problems like colorization." ] }, { "id": "task668-4fa3872ae66e4087874758354d2e6efa", "input": "In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget. In this paper, we propose a novel approach for cost-sensitive feature acquisition at the prediction-time. The suggested method acquires features incrementally based on a context-aware feature-value function. We formulate the problem in the reinforcement learning paradigm, and introduce a reward function based on the utility of each feature. Specifically, MC dropout sampling is used to measure expected variations of the model uncertainty which is used as a feature-value function. Furthermore, we suggest sharing representations between the class predictor and value function estimator networks. The suggested approach is completely online and is readily applicable to stream learning setups. The solution is evaluated on three different datasets including the well-known MNIST dataset as a benchmark as well as two cost-sensitive datasets: Yahoo Learning to Rank and a dataset in the medical domain for diabetes classification. According to the results, the proposed method is able to efficiently acquire features and make accurate predictions.", "output": [ "An online algorithm for cost-aware feature acquisition and prediction" ] }, { "id": "task668-dde7ffbca1174c50984618cef876d9f7", "input": "Information need of humans is essentially multimodal in nature, enabling maximum exploitation of situated context. We introduce a dataset for sequential procedural (how-to) text generation from images in cooking domain. The dataset consists of 16,441 cooking recipes with 160,479 photos associated with different steps. We setup a baseline motivated by the best performing model in terms of human evaluation for the Visual Story Telling (ViST) task. In addition, we introduce two models to incorporate high level structure learnt by a Finite State Machine (FSM) in neural sequential generation process by: (1) Scaffolding Structure in Decoder (SSiD) (2) Scaffolding Structure in Loss (SSiL). These models show an improvement in empirical as well as human evaluation. Our best performing model (SSiL) achieves a METEOR score of 0.31, which is an improvement of 0.6 over the baseline model. We also conducted human evaluation of the generated grounded recipes, which reveal that 61% found that our proposed (SSiL) model is better than the baseline model in terms of overall recipes, and 72.5% preferred our model in terms of coherence and structure. We also discuss analysis of the output highlighting key important NLP issues for prospective directions.\n", "output": [ "The paper presents two techniques to incorporate high level structure in generating procedural text from a sequence of images." ] }, { "id": "task668-e4ed812d6c8b461c8870fb4c3f1dc8cb", "input": "Reinforcement learning agents are typically trained and evaluated according to their performance averaged over some distribution of environment settings. But does the distribution over environment settings contain important biases, and do these lead to agents that fail in certain cases despite high average-case performance? In this work, we consider worst-case analysis of agents over environment settings in order to detect whether there are directions in which agents may have failed to generalize. Specifically, we consider a 3D first-person task where agents must navigate procedurally generated mazes, and where reinforcement learning agents have recently achieved human-level average-case performance. By optimizing over the structure of mazes, we find that agents can suffer from catastrophic failures, failing to find the goal even on surprisingly simple mazes, despite their impressive average-case performance. Additionally, we find that these failures transfer between different agents and even significantly different architectures. We believe our findings highlight an important role for worst-case analysis in identifying whether there are directions in which agents have failed to generalize. Our hope is that the ability to automatically identify failures of generalization will facilitate development of more general and robust agents. To this end, we report initial results on enriching training with settings causing failure.", "output": [ "We find environment settings in which SOTA agents trained on navigation tasks display extreme failures suggesting failures in generalization." ] }, { "id": "task668-6c266714395a4f05916345c43e768695", "input": "For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN), a versatile deep generative model that integrates a probabilistic text decoder, probabilistic image encoder, and GAN into a coherent end-to-end multi-modality learning framework. VHE randomized GAN (VHE-GAN) encodes an image to decode its associated text, and feeds the variational posterior as the source of randomness into the GAN image generator. We plug three off-the-shelf modules, including a deep topic model, a ladder-structured image encoder, and StackGAN++, into VHE-GAN, which already achieves competitive performance. This further motivates the development of VHE-raster-scan-GAN that generates photo-realistic images in not only a multi-scale low-to-high-resolution manner, but also a hierarchical-semantic coarse-to-fine fashion. By capturing and relating hierarchical semantic and visual concepts with end-to-end training, VHE-raster-scan-GAN achieves state-of-the-art performance in a wide variety of image-text multi-modality learning and generation tasks.", "output": [ "A novel Bayesian deep learning framework that captures and relates hierarchical semantic and visual concepts, performing well on a variety of image and text modeling and generation tasks." ] }, { "id": "task668-97ce8b20329a4de8b77fff9e68224937", "input": "Very recently, it comes to be a popular approach for answering open-domain questions by first searching question-related passages, then applying reading comprehension models to extract answers. Existing works usually extract answers from single passages independently, thus not fully make use of the multiple searched passages, especially for the some questions requiring several evidences, which can appear in different passages, to be answered. The above observations raise the problem of evidence aggregation from multiple passages. In this paper, we deal with this problem as answer re-ranking. Specifically, based on the answer candidates generated from the existing state-of-the-art QA model, we propose two different re-ranking methods, strength-based and coverage-based re-rankers, which make use of the aggregated evidences from different passages to help entail the ground-truth answer for the question. Our model achieved state-of-the-arts on three public open-domain QA datasets, Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8\\% improvement on the former two datasets.", "output": [ "We propose a method that can make use of the multiple passages information for open-domain QA." ] }, { "id": "task668-be1cfc8c88b64bf885e75cb50f03da7b", "input": "Computer vision tasks such as image classification, image retrieval and few-shot learning are currently dominated by Euclidean and spherical embeddings, so that the final decisions about class belongings or the degree of similarity are made using linear hyperplanes, Euclidean distances, or spherical geodesic distances (cosine similarity). In this work, we demonstrate that in many practical scenarios hyperbolic embeddings provide a better alternative.", "output": [ "We show that hyperbolic embeddings are useful for high-level computer vision tasks, especially for few-shot classification." ] }, { "id": "task668-dcd73a52af9c49ba95a66c2156e5baf1", "input": "We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization. \n Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists even in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed in practice. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the features learned by robust models tend to align better with salient data characteristics and human perception.", "output": [ "We show that adversarial robustness might come at the cost of standard classification performance, but also yields unexpected benefits." ] }, { "id": "task668-f9c1ccc571424361875c0d47cca8cc96", "input": "State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet. In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs. Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or English-Chinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation. Our code, embeddings and dictionaries are publicly available.", "output": [ "Aligning languages without the Rosetta Stone: with no parallel data, we construct bilingual dictionaries using adversarial training, cross-domain local scaling, and an accurate proxy criterion for cross-validation." ] }, { "id": "task668-2aebc82afcd64f8cb1fde580df48a8bc", "input": "Generating visualizations and interpretations from high-dimensional data is a\n common problem in many fields. Two key approaches for tackling this problem \n are clustering and representation learning. There are very performant deep\n clustering models on the one hand and interpretable representation learning techniques, \n often relying on latent topological structures such as self-organizing maps,\n on the other hand. However, current methods do not yet successfully combine\n these two approaches. We present a new deep architecture for probabilistic clustering, \n VarPSOM, and its extension to time series data, VarTPSOM, composed of VarPSOM \n modules connected by LSTM cells. We show that they achieve superior \n clustering performance compared to current deep clustering methods on static \n MNIST/Fashion-MNIST data as well as medical time series, while inducing an\n interpretable representation. Moreover, on the medical time series, VarTPSOM\n successfully predicts future trajectories in the original data space.", "output": [ "We present a new deep architecture, VarPSOM, and its extension to time series data, VarTPSOM, which achieve superior clustering performance compared to current deep clustering methods on static and temporal data." ] }, { "id": "task668-a50e6fa8f4464e7d9c43826f87bcc80e", "input": "Support Vector Machines (SVMs) are one of the most popular algorithms for classification and regression analysis. Despite their popularity, even efficient implementations have proven to be computationally expensive to train at a large-scale, especially in streaming settings. In this paper, we propose a novel coreset construction algorithm for efficiently generating compact representations of massive data sets to speed up SVM training. A coreset is a weighted subset of the original data points such that SVMs trained on the coreset are provably competitive with those trained on the original (massive) data set. We provide both lower and upper bounds on the number of samples required to obtain accurate approximations to the SVM problem as a function of the complexity of the input data. Our analysis also establishes sufficient conditions on the existence of sufficiently compact and representative coresets for the SVM problem. We empirically evaluate the practical effectiveness of our algorithm against synthetic and real-world data sets.", "output": [ "We present an algorithm for speeding up SVM training on massive data sets by constructing compact representations that provide efficient and provably approximate inference.", "Studies the approach of coreset for SVM and aims at sampling a small set of weighted points such that the loss function over the points provably approximates that over the whole dataset", "The paper suggests an importance sampling based Coreset construction to represent large training data for SVMs" ] }, { "id": "task668-8acb615616a34d6fa55508e50e050c55", "input": "The state-of-the-art (SOTA) for mixed precision training is dominated by variants of low precision floating point operations, and in particular, FP16 accumulating into FP32 Micikevicius et al. (2017). On the other hand, while a lot of research has also happened in the domain of low and mixed-precision Integer training, these works either present results for non-SOTA networks (for instance only AlexNet for ImageNet-1K), or relatively small datasets (like CIFAR-10). In this work, we train state-of-the-art visual understanding neural networks on the ImageNet-1K dataset, with Integer operations on General Purpose (GP) hardware. In particular, we focus on Integer Fused-Multiply-and-Accumulate (FMA) operations which take two pairs of INT16 operands and accumulate results into an INT32 output.We propose a shared exponent representation of tensors and develop a Dynamic Fixed Point (DFP) scheme suitable for common neural network operations. The nuances of developing an efficient integer convolution kernel is examined, including methods to handle overflow of the INT32 accumulator. We implement CNN training for ResNet-50, GoogLeNet-v1, VGG-16 and AlexNet; and these networks achieve or exceed SOTA accuracy within the same number of iterations as their FP32 counterparts without any change in hyper-parameters and with a 1.8X improvement in end-to-end training throughput. To the best of our knowledge these results represent the first INT16 training results on GP hardware for ImageNet-1K dataset using SOTA CNNs and achieve highest reported accuracy using half precision", "output": [ "Mixed precision training pipeline using 16-bit integers on general purpose HW; SOTA accuracy for ImageNet-class CNNs; Best reported accuracy for ImageNet-1K classification task with any reduced precision training;", "This paper shows that a careful implementation of mixed-precision dynamic fixed point computation can achieve state of the art accuracy using a reduced precision deep learning model with a 16 bit integer representation", "Proposes a \"dynamic fixed point\" scheme that shares the exponent part for a tensor and develops procedures to do NN computing with this format and demonstrates this for limited precision training." ] }, { "id": "task668-e79e50f226894a16b4f212429b6571ff", "input": "We study the behavior of weight-tied multilayer vanilla autoencoders under the assumption of random weights. Via an exact characterization in the limit of large dimensions, our analysis reveals interesting phase transition phenomena when the depth becomes large. This, in particular, provides quantitative answers and insights to three questions that were yet fully understood in the literature. Firstly, we provide a precise answer on how the random deep weight-tied autoencoder model performs “approximate inference” as posed by Scellier et al. (2018), and its connection to reversibility considered by several theoretical studies. Secondly, we show that deep autoencoders display a higher degree of sensitivity to perturbations in the parameters, distinct from the shallow counterparts. Thirdly, we obtain insights on pitfalls in training initialization practice, and demonstrate experimentally that it is possible to train a deep autoencoder, even with the tanh activation and a depth as large as 200 layers, without resorting to techniques such as layer-wise pre-training or batch normalization. Our analysis is not specific to any depths or any Lipschitz activations, and our analytical techniques may have broader applicability.", "output": [ "We study the behavior of weight-tied multilayer vanilla autoencoders under the assumption of random weights. Via an exact characterization in the limit of large dimensions, our analysis reveals interesting phase transition phenomena.", "A theoretical analysis of autoencoders with weights tied between encoder and decoder (weight-tied) via mean field analysis", "Analyses the performances of weighted tied auto-encoders by building on recent progress in analysis of high-dimensional statistics problems and specifically, the message passing algorithm", "This paper studies auto-encoders under several assumptions, and points out that this model of random autoencoder can be elegantly and rigorously analysed with one-dimensional equations." ] }, { "id": "task668-a4fc53f83fe44a87b4fc58d5bd9a91f9", "input": "Recurrent neural networks (RNNs) have achieved state-of-the-art performance on many diverse tasks, from machine translation to surgical activity recognition, yet training RNNs to capture long-term dependencies remains difficult. To date, the vast majority of successful RNN architectures alleviate this problem using nearly-additive connections between states, as introduced by long short-term memory (LSTM). We take an orthogonal approach and introduce MIST RNNs, a NARX RNN architecture that allows direct connections from the very distant past. We show that MIST RNNs 1) exhibit superior vanishing-gradient properties in comparison to LSTM and previously-proposed NARX RNNs; 2) are far more efficient than previously-proposed NARX RNN architectures, requiring even fewer computations than LSTM; and 3) improve performance substantially over LSTM and Clockwork RNNs on tasks requiring very long-term dependencies.", "output": [ "We introduce MIST RNNs, which a) exhibit superior vanishing-gradient properties in comparison to LSTM; b) improve performance substantially over LSTM and Clockwork RNNs on tasks requiring very long-term dependencies; and c) are much more efficient than previously-proposed NARX RNNs, with even fewer parameters and operations than LSTM." ] }, { "id": "task668-4c7a76da67d4451c8308ef16ad294a90", "input": "Generative Adversarial Networks (GANs) have become the gold standard when it comes to learning generative models for high-dimensional distributions. Since their advent, numerous variations of GANs have been introduced in the literature, primarily focusing on utilization of novel loss functions, optimization/regularization strategies and network architectures. In this paper, we turn our attention to the generator and investigate the use of high-order polynomials as an alternative class of universal function approximators. Concretely, we propose PolyGAN, where we model the data generator by means of a high-order polynomial whose unknown parameters are naturally represented by high-order tensors. We introduce two tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks that only employ linear/convolutional blocks. We exhibit for the first time that by using our approach a GAN generator can approximate the data distribution without using any activation functions. Thorough experimental evaluation on both synthetic and real data (images and 3D point clouds) demonstrates the merits of PolyGAN against the state of the art.", "output": [ "We model the data generator (in GAN) by means of a high-order polynomial represented by high-order tensors." ] }, { "id": "task668-b9618f47be254da485b24de98c83bac3", "input": "Generative priors have become highly effective in solving inverse problems including denoising, inpainting, and reconstruction from few and noisy measurements. With a generative model we can represent an image with a much lower dimensional latent codes. In the context of compressive sensing, if the unknown image belongs to the range of a pretrained generative network, then we can recover the image by estimating the underlying compact latent code from the available measurements. However, recent studies revealed that even untrained deep neural networks can work as a prior for recovering natural images. These approaches update the network weights keeping latent codes fixed to reconstruct the target image from the given measurements. In this paper, we optimize over network weights and latent codes to use untrained generative network as prior for video compressive sensing problem. We show that by optimizing over latent code, we can additionally get concise representation of the frames which retain the structural similarity of the video frames. We also apply low-rank constraint on the latent codes to represent the video sequences in even lower dimensional latent space. We empirically show that our proposed methods provide better or comparable accuracy and low computational complexity compared to the existing methods.", "output": [ "Recover videos from compressive measurements by learning a low-dimensional (low-rank) representation directly from measurements while training a deep generator. " ] }, { "id": "task668-1520381a3d9c45c799591477e3ae3822", "input": "Inspired by the combination of feedforward and iterative computations in the visual cortex, and taking advantage of the ability of denoising autoencoders to estimate the score of a joint distribution, we propose a novel approach to iterative inference for capturing and exploiting the complex joint distribution of output variables conditioned on some input variables. This approach is applied to image pixel-wise segmentation, with the estimated conditional score used to perform gradient ascent towards a mode of the estimated conditional distribution. This extends previous work on score estimation by denoising autoencoders to the case of a conditional distribution, with a novel use of a corrupted feedforward predictor replacing Gaussian corruption. An advantage of this approach over more classical ways to perform iterative inference for structured outputs, like conditional random fields (CRFs), is that it is not any more necessary to define an explicit energy function linking the output variables. To keep computations tractable, such energy function parametrizations are typically fairly constrained, involving only a few neighbors of each of the output variables in each clique. We experimentally find that the proposed iterative inference from conditional score estimation by conditional denoising autoencoders performs better than comparable models based on CRFs or those not using any explicit modeling of the conditional joint distribution of outputs.", "output": [ "Refining segmentation proposals by performing iterative inference with conditional denoising autoencoders." ] }, { "id": "task668-d7b4396d72c84ecf8aa6a71a586d6f3a", "input": "In this paper we present a novel optimization algorithm called Advanced Neuroevolution. The aim for this algorithm is to train deep neural networks, and eventually act as an alternative to Stochastic Gradient Descent (SGD) and its variants as needed.We evaluated our algorithm on the MNIST dataset, as well as on several global optimization problems such as the Ackley function. We find the algorithm performing relatively well for both cases, overtaking other global optimization algorithms such as Particle Swarm Optimization (PSO) and Evolution Strategies (ES).\n", "output": [ "A new algorithm to train deep neural networks. Tested on optimization functions and MNIST." ] }, { "id": "task668-706f1b4827fd4e5eaefd8202ea81c8e3", "input": "Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich and complex probabilistic models. However, this expressiveness comes at the cost of substantially complicating the process of drawing inferences from the model. In particular, inference can become challenging when the support of the model varies between executions. Though general-purpose inference engines have been designed to operate in such settings, they are typically inefficient, often relying on proposing from the prior to make transitions. To address this, we introduce a new inference framework: Divide, Conquer, and Combine (DCC). DCC divides the program into separate straight-line sub-programs, each of which has a fixed support allowing more powerful inference algorithms to be run locally, before recombining their outputs in a principled fashion. We show how DCC can be implemented as an automated and general-purpose PPS inference engine, and empirically confirm that it can provide substantial performance improvements over previous approaches.", "output": [ "Divide, Conquer, and Combine is a new inference scheme that can be performed on the probabilistic programs with stochastic support, i.e. the very existence of variables is stochastic." ] }, { "id": "task668-e92be75a651747e38408c67e330ee546", "input": "Many challenging prediction problems, from molecular optimization to program synthesis, involve creating complex structured objects as outputs. However, available training data may not be sufficient for a generative model to learn all possible complex transformations. By leveraging the idea that evaluation is easier than generation, we show how a simple, broadly applicable, iterative target augmentation scheme can be surprisingly effective in guiding the training and use of such models. Our scheme views the generative model as a prior distribution, and employs a separately trained filter as the likelihood. In each augmentation step, we filter the model's outputs to obtain additional prediction targets for the next training epoch. Our method is applicable in the supervised as well as semi-supervised settings. We demonstrate that our approach yields significant gains over strong baselines both in molecular optimization and program synthesis. In particular, our augmented model outperforms the previous state-of-the-art in molecular optimization by over 10% in absolute gain.", "output": [ "We improve generative models by proposing a meta-algorithm that filters new training data from the model's outputs." ] }, { "id": "task668-da402e3280b742718d00f171bd07a1cc", "input": "Vector semantics, especially sentence vectors, have recently been used successfully in many areas of natural language processing. However, relatively little work has explored the internal structure and properties of spaces of sentence vectors. In this paper, we will explore the properties of sentence vectors by studying a particular real-world application: Automatic Summarization. In particular, we show that cosine similarity between sentence vectors and document vectors is strongly correlated with sentence importance and that vector semantics can identify and correct gaps between the sentences chosen so far and the document. In addition, we identify specific dimensions which are linked to effective summaries. To our knowledge, this is the first time specific dimensions of sentence embeddings have been connected to sentence properties. We also compare the features of different methods of sentence embeddings. Many of these insights have applications in uses of sentence embeddings far beyond summarization.", "output": [ "A comparison and detailed analysis of various sentence embedding models through the real-world task of automatic summarization." ] }, { "id": "task668-1b550bc08a3540f884d512387dc5954f", "input": "Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights. Recent work developing this class of methods has explored ever richer parameterizations of the approximate posterior in the hope of improving performance. In contrast, here we share a curious experimental finding that suggests instead restricting the variational distribution to a more compact parameterization. For a variety of deep Bayesian neural networks trained using Gaussian mean-field variational inference, we find that the posterior standard deviations consistently exhibit strong low-rank structure after convergence. This means that by decomposing these variational parameters into a low-rank factorization, we can make our variational approximation more compact without decreasing the models' performance. Furthermore, we find that such factorized parameterizations improve the signal-to-noise ratio of stochastic gradient estimates of the variational lower bound, resulting in faster convergence.", "output": [ "Mean field VB uses twice as many parameters; we tie variance parameters in mean field VB without any loss in ELBO, gaining speed and lower variance gradients." ] }, { "id": "task668-68bff24bf2474d98bc6c41275e004a90", "input": "We study the problem of training machine learning models incrementally using active learning with access to imperfect or noisy oracles. We specifically consider the setting of batch active learning, in which multiple samples are selected as opposed to a single sample as in classical settings so as to reduce the training overhead. Our approach bridges between uniform randomness and score based importance sampling of clusters when selecting a batch of new samples. Experiments on\n benchmark image classification datasets (MNIST, SVHN, and CIFAR10) shows improvement over existing active learning strategies. We introduce an extra denoising layer to deep networks to make active learning robust to label noises and show significant improvements.\n", "output": [ "We address the active learning in batch setting with noisy oracles and use model uncertainty to encode the decision quality of active learning algorithm during acquisition." ] }, { "id": "task668-a6768e6159d1427b8d480b4ce8195c42", "input": "We investigate low-bit quantization to reduce computational cost of deep neural network (DNN) based keyword spotting (KWS). We propose approaches to further reduce quantization bits via integrating quantization into keyword spotting model training, which we refer to as quantization-aware training. Our experimental results on large dataset indicate that quantization-aware training can recover performance models quantized to lower bits representations. By combining quantization-aware training and weight matrix factorization, we are able to significantly reduce model size and computation for small-footprint keyword spotting, while maintaining performance.", "output": [ "We investigate quantization-aware training in very low-bit quantized keyword spotters to reduce the cost of on-device keyword spotting.", "This submission proposes a combination of low-rank decomposition and quanitization approach to compress DNN models for keyword spotting." ] }, { "id": "task668-f568c608b8df40988b2643c9bbf39882", "input": "Learning rate decay (lrDecay) is a \\emph{de facto} technique for training modern neural networks. It starts with a large learning rate and then decays it multiple times. It is empirically observed to help both optimization and generalization. Common beliefs in how lrDecay works come from the optimization analysis of (Stochastic) Gradient Descent: 1) an initially large learning rate accelerates training or helps the network escape spurious local minima; 2) decaying the learning rate helps the network converge to a local minimum and avoid oscillation. Despite the popularity of these common beliefs, experiments suggest that they are insufficient in explaining the general effectiveness of lrDecay in training modern neural networks that are deep, wide, and nonconvex. We provide another novel explanation: an initially large learning rate suppresses the network from memorizing noisy data while decaying the learning rate improves the learning of complex patterns. The proposed explanation is validated on a carefully-constructed dataset with tractable pattern complexity. And its implication, that additional patterns learned in later stages of lrDecay are more complex and thus less transferable, is justified in real-world datasets. We believe that this alternative explanation will shed light into the design of better training strategies for modern neural networks.", "output": [ "We provide another novel explanation of learning rate decay: an initially large learning rate suppresses the network from memorizing noisy data while decaying the learning rate improves the learning of complex patterns." ] }, { "id": "task668-12485090ce794c3c898843743b4214b0", "input": "We develop a mean field theory for batch normalization in fully-connected feedforward neural networks. In so doing, we provide a precise characterization of signal propagation and gradient backpropagation in wide batch-normalized networks at initialization. Our theory shows that gradient signals grow exponentially in depth and that these exploding gradients cannot be eliminated by tuning the initial weight variances or by adjusting the nonlinear activation function. Indeed, batch normalization itself is the cause of gradient explosion. As a result, vanilla batch-normalized networks without skip connections are not trainable at large depths for common initialization schemes, a prediction that we verify with a variety of empirical simulations. While gradient explosion cannot be eliminated, it can be reduced by tuning the network close to the linear regime, which improves the trainability of deep batch-normalized networks without residual connections. Finally, we investigate the learning dynamics of batch-normalized networks and observe that after a single step of optimization the networks achieve a relatively stable equilibrium in which gradients have dramatically smaller dynamic range. Our theory leverages Laplace, Fourier, and Gegenbauer transforms and we derive new identities that may be of independent interest.", "output": [ "Batch normalization causes exploding gradients in vanilla feedforward networks.", "Develops a mean field theory for batch normalization (BN) in fully-connected networks with randomly initialized weights.", "Provides a dynamic perspective on deep neural network using the evolution of the covariance matrix along with the layers." ] }, { "id": "task668-189fc0e87a6b4fcfb7e147f319efb2d6", "input": "Hierarchical agents have the potential to solve sequential decision making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents can break down tasks into sets of subtasks that only require short sequences of decisions. In order to realize this potential of faster learning, hierarchical agents need to be able to learn their multiple levels of policies in parallel so these simpler subproblems can be solved simultaneously. Yet, learning multiple levels of policies in parallel is hard because it is inherently unstable: changes in a policy at one level of the hierarchy may cause changes in the transition and reward functions at higher levels in the hierarchy, making it difficult to jointly learn multiple levels of policies. In this paper, we introduce a new Hierarchical Reinforcement Learning (HRL) framework, Hierarchical Actor-Critic (HAC), that can overcome the instability issues that arise when agents try to jointly learn multiple levels of policies. The main idea behind HAC is to train each level of the hierarchy independently of the lower levels by training each level as if the lower level policies are already optimal. We demonstrate experimentally in both grid world and simulated robotics domains that our approach can significantly accelerate learning relative to other non-hierarchical and hierarchical methods. Indeed, our framework is the first to successfully learn 3-level hierarchies in parallel in tasks with continuous state and action spaces.", "output": [ "We introduce the first Hierarchical RL approach to successfully learn 3-level hierarchies in parallel in tasks with continuous state and action spaces." ] }, { "id": "task668-ef945fbf5f024f69a59f7aca243cb4bb", "input": "To backpropagate the gradients through stochastic binary layers, we propose the augment-REINFORCE-merge (ARM) estimator that is unbiased, exhibits low variance, and has low computational complexity. Exploiting variable augmentation, REINFORCE, and reparameterization, the ARM estimator achieves adaptive variance reduction for Monte Carlo integration by merging two expectations via common random numbers. The variance-reduction mechanism of the ARM estimator can also be attributed to either antithetic sampling in an augmented space, or the use of an optimal anti-symmetric \"self-control\" baseline function together with the REINFORCE estimator in that augmented space. Experimental results show the ARM estimator provides state-of-the-art performance in auto-encoding variational inference and maximum likelihood estimation, for discrete latent variable models with one or multiple stochastic binary layers. Python code for reproducible research is publicly available.", "output": [ "An unbiased and low-variance gradient estimator for discrete latent variable models", "Proposes a new variance-reduction technique to use when computing an expected loss gradient where the expectation is with respect to independent binary random variables.", "An algorithm combining Rao-Blackwellization and common random numbers for lowering the variance of the score-function gradient estimator in the special case of stochastic binary networks", "An unbiased and low variance augment-REINFORCE-merge (ARM) estimator for calculating and backpropagating gradients in binary neural networks" ] }, { "id": "task668-237bca728e03466da4b11160fdcfedcb", "input": "Recent studies have highlighted adversarial examples as a ubiquitous threat to different neural network models and many downstream applications. Nonetheless, as unique data properties have inspired distinct and powerful learning principles, this paper aims to explore their potentials towards mitigating adversarial inputs. In particular, our results reveal the importance of using the temporal dependency in audio data to gain discriminate power against adversarial examples. Tested on the automatic speech recognition (ASR) tasks and three recent audio adversarial attacks, we find that (i) input transformation developed from image adversarial defense provides limited robustness improvement and is subtle to advanced attacks; (ii) temporal dependency can be exploited to gain discriminative power against audio adversarial examples and is resistant to adaptive attacks considered in our experiments. Our results not only show promising means of improving the robustness of ASR systems, but also offer novel insights in exploiting domain-specific data properties to mitigate negative effects of adversarial examples.", "output": [ "Adversarial audio discrimination using temporal dependency" ] }, { "id": "task668-a7fcad312e0d4e8a938d4582a86b6978", "input": "We develop a stochastic whole-brain and body simulator of the nematode roundworm Caenorhabditis elegans (C. elegans) and show that it is sufficiently regularizing to allow imputation of latent membrane potentials from partial calcium fluorescence imaging observations. This is the first attempt we know of to ``complete the circle,'' where an anatomically grounded whole-connectome simulator is used to impute a time-varying ``brain'' state at single-cell fidelity from covariates that are measurable in practice. Using state of the art Bayesian machine learning methods to condition on readily obtainable data, our method paves the way for neuroscientists to recover interpretable connectome-wide state representations, automatically estimate physiologically relevant parameter values from data, and perform simulations investigating intelligent lifeforms in silico.", "output": [ "We develop a whole-connectome and body simulator for C. elegans and demonstrate joint state-space and parameter inference in the simulator." ] }, { "id": "task668-12ccb48bdc1b404399af5ae53ebbf6d5", "input": "In recent years we have made significant progress identifying computational principles that underlie neural function. While not yet complete, we have sufficient evidence that a synthesis of these ideas could result in an understanding of how neural computation emerges from a combination of innate dynamics and plasticity, and which could potentially be used to construct new AI technologies with unique capabilities. I discuss the relevant principles, the advantages they have for computation, and how they can benefit AI. Limitations of current AI are generally recognized, but fewer people are aware that we understand enough about the brain to immediately offer novel AI formulations.\n", "output": [ "Limitations of current AI are generally recognized, but fewer people are aware that we understand enough about the brain to immediately offer novel AI formulations." ] }, { "id": "task668-4a34768e23bc44cf80e56514035bd248", "input": "This work views neural networks as data generating systems and applies anomalous pattern detection techniques on that data in order to detect when a network is processing a group of anomalous inputs. Detecting anomalies is a critical component for multiple machine learning problems including detecting the presence of adversarial noise added to inputs. More broadly, this work is a step towards giving neural networks the ability to detect groups of out-of-distribution samples. This work introduces ``Subset Scanning methods from the anomalous pattern detection domain to the task of detecting anomalous inputs to neural networks. Subset Scanning allows us to answer the question: \"``Which subset of inputs have larger-than-expected activations at which subset of nodes? \" Framing the adversarial detection problem this way allows us to identify systematic patterns in the activation space that span multiple adversarially noised images. Such images are ``\"weird together\". Leveraging this common anomalous pattern, we show increased detection power as the proportion of noised images increases in a test set. Detection power and accuracy results are provided for targeted adversarial noise added to CIFAR-10 images on a 20-layer ResNet using the Basic Iterative Method attack.", "output": [ "We efficiently find a subset of images that have higher than expected activations for some subset of nodes. These images appear more anomalous and easier to detect when viewed as a group. ", "The paper proposed a scheme to detect the presence of anomalous inputs based on a \"subset scanning\" approach to detect anomalous activations in the deep learning network." ] }, { "id": "task668-8302da0780fb443b9cb80783201ded25", "input": "Despite the state-of-the-art accuracy of Deep Neural Networks (DNN) in various classification problems, their deployment onto resource constrained edge computing devices remains challenging due to their large size and complexity. Several recent studies have reported remarkable results in reducing this complexity through quantization of DNN models. However, these studies usually do not consider the changes in the loss function when performing quantization, nor do they take the different importances of DNN model parameters to the accuracy into account. We address these issues in this paper by proposing a new method, called adaptive quantization, which simplifies a trained DNN model by finding a unique, optimal precision for each network parameter such that the increase in loss is minimized. The optimization problem at the core of this method iteratively uses the loss function gradient to determine an error margin for each parameter and assigns it a precision accordingly. Since this problem uses linear functions, it is computationally cheap and, as we will show, has a closed-form approximate solution. Experiments on MNIST, CIFAR, and SVHN datasets showed that the proposed method can achieve near or better than state-of-the-art reduction in model size with similar error rates. Furthermore, it can achieve compressions close to floating-point model compression methods without loss of accuracy.", "output": [ "An adaptive method for fixed-point quantization of neural networks based on theoretical analysis rather than heuristics. ", "Proposes a method for quantizing neural networks that allow weights to be quantized with different precision depending on their importance, taking into account the loss.", "The paper proposes a technique for quantizing the weights of a neural network with bit-depth/precision varying on a per-parameter basis." ] }, { "id": "task668-e363d6a9ffc5465da7087e87f0eadec5", "input": "Training neural networks on large datasets can be accelerated by distributing the workload over a network of machines. As datasets grow ever larger, networks of hundreds or thousands of machines become economically viable. The time cost of communicating gradients limits the effectiveness of using such large machine counts, as may the increased chance of network faults. We explore a particularly simple algorithm for robust, communication-efficient learning---signSGD. Workers transmit only the sign of their gradient vector to a server, and the overall update is decided by a majority vote. This algorithm uses 32x less communication per iteration than full-precision, distributed SGD. Under natural conditions verified by experiment, we prove that signSGD converges in the large and mini-batch settings, establishing convergence for a parameter regime of Adam as a byproduct. Aggregating sign gradients by majority vote means that no individual worker has too much power. We prove that unlike SGD, majority vote is robust when up to 50% of workers behave adversarially. The class of adversaries we consider includes as special cases those that invert or randomise their gradient estimate. On the practical side, we built our distributed training system in Pytorch. Benchmarking against the state of the art collective communications library (NCCL), our framework---with the parameter server housed entirely on one machine---led to a 25% reduction in time for training resnet50 on Imagenet when using 15 AWS p3.2xlarge machines.", "output": [ "Workers send gradient signs to the server, and the update is decided by majority vote. We show that this algorithm is convergent, communication efficient and fault tolerant, both in theory and in practice.", "Presents a distributed implementation of signSGD with majority vote as aggregation." ] }, { "id": "task668-d269f65d49e249fcb8467ff90040e8b9", "input": "This paper introduces a probabilistic framework for k-shot image classification. The goal is to generalise from an initial large-scale classification task to a separate task comprising new classes and small numbers of examples. The new approach not only leverages the feature-based representation learned by a neural network from the initial task (representational transfer), but also information about the classes (concept transfer). The concept information is encapsulated in a probabilistic model for the final layer weights of the neural network which acts as a prior for probabilistic k-shot learning. We show that even a simple probabilistic model achieves state-of-the-art on a standard k-shot learning dataset by a large margin. Moreover, it is able to accurately model uncertainty, leading to well calibrated classifiers, and is easily extensible and flexible, unlike many recent approaches to k-shot learning.", "output": [ "This paper introduces a probabilistic framework for k-shot image classification that achieves state-of-the-art results" ] }, { "id": "task668-72e1066f8f0b4f7295e615216fb9c7c1", "input": "We aim to build complex humanoid agents that integrate perception, motor control, and memory. In this work, we partly factor this problem into low-level motor control from proprioception and high-level coordination of the low-level skills informed by vision. We develop an architecture capable of surprisingly flexible, task-directed motor control of a relatively high-DoF humanoid body by combining pre-training of low-level motor controllers with a high-level, task-focused controller that switches among low-level sub-policies. The resulting system is able to control a physically-simulated humanoid body to solve tasks that require coupling visual perception from an unstabilized egocentric RGB camera during locomotion in the environment. Supplementary video link: https://youtu.be/fBoir7PNxPk", "output": [ "Solve tasks involving vision-guided humanoid locomotion, reusing locomotion behavior from motion capture data." ] }, { "id": "task668-041ffce873384f76914a2c455e46f6d0", "input": "We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN). Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input is always mapped to a single output regardless of variations in latent code. To address such issue, we propose to explicitly regularize the generator to produce diverse outputs depending on latent codes. The proposed regularization is simple, general, and can be easily integrated into most conditional GAN objectives. Additionally, explicit regularization on generator allows our method to control a balance between visual quality and diversity. We demonstrate the effectiveness of our method on three conditional generation tasks: image-to-image translation, image inpainting, and future video prediction. We show that simple addition of our regularization to existing models leads to surprisingly diverse generations, substantially outperforming the previous approaches for multi-modal conditional generation specifically designed in each individual task.", "output": [ "We propose a simple and general approach that avoids a mode collapse problem in various conditional GANs.", "The paper proposes a regularization term for the conditional GAN objective in order to promote diverse multimodal generation and prevent mode collapse.", "The paper proposes a method for generating diverse outputs for various conditional GAN frameworks including image-to-image translation, image-inpainting, and video prediction, which can be applied to various conditional synthesis frameworks for various tasks. " ] }, { "id": "task668-d353a69b470e4eb0922751eec0beaa04", "input": "We propose a method for learning the dependency structure between latent variables in deep latent variable models. Our general modeling and inference framework combines the complementary strengths of deep generative models and probabilistic graphical models. In particular, we express the latent variable space of a variational autoencoder (VAE) in terms of a Bayesian network with a learned, flexible dependency structure. The network parameters, variational parameters as well as the latent topology are optimized simultaneously with a single objective. Inference is formulated via a sampling procedure that produces expectations over latent variable structures and incorporates top-down and bottom-up reasoning over latent variable values. We validate our framework in extensive experiments on MNIST, Omniglot, and CIFAR-10. Comparisons to state-of-the-art structured variational autoencoder baselines show improvements in terms of the expressiveness of the learned model.", "output": [ "We propose a method for learning latent dependency structure in variational autoencoders.", "Uses a matrix of binary random variables to capture dependencies between latent variables in a hierarchical deep generative model.", "This paper presents a VAE approach in which a dependency structure on the latent variable is learned during training.", "The authors propose to augment the latent space of a VAE with an auto-regressive structure, to improve the expressiveness of both the inference network and the latent prior" ] }, { "id": "task668-930497bd856d40a3b1c5b61b57713ac4", "input": "Ordinary stochastic neural networks mostly rely on the expected values of their weights to make predictions, whereas the induced noise is mostly used to capture the uncertainty, prevent overfitting and slightly boost the performance through test-time averaging. In this paper, we introduce variance layers, a different kind of stochastic layers. Each weight of a variance layer follows a zero-mean distribution and is only parameterized by its variance. It means that each object is represented by a zero-mean distribution in the space of the activations. We show that such layers can learn surprisingly well, can serve as an efficient exploration tool in reinforcement learning tasks and provide a decent defense against adversarial attacks. We also show that a number of conventional Bayesian neural networks naturally converge to such zero-mean posteriors. We observe that in these cases such zero-mean parameterization leads to a much better training objective than more flexible conventional parameterizations where the mean is being learned.", "output": [ "It is possible to learn a zero-centered Gaussian distribution over the weights of a neural network by learning only variances, and it works surprisingly well.", "This paper investigates the effects of mean of variational posterior and proposes variance layer, which only uses variance to store information", "Studies variance neural networks which approximate the posterior of Bayesian neural networks with zero-mean Gaussian distributions" ] }, { "id": "task668-e6abc48d3bb0498282a3b894154bd2b0", "input": "Determinantal point processes (DPPs) is an effective tool to deliver diversity on multiple machine learning and computer vision tasks. Under deep learning framework, DPP is typically optimized via approximation, which is not straightforward and has some conflict with diversity requirement. We note, however, there has been no deep learning paradigms to optimize DPP directly since it involves matrix inversion which may result in highly computational instability. This fact greatly hinders the wide use of DPP on some specific objectives where DPP serves as a term to measure the feature diversity. In this paper, we devise a simple but effective algorithm to address this issue to optimize DPP term directly expressed with L-ensemble in spectral domain over gram matrix, which is more flexible than learning on parametric kernels. By further taking into account some geometric constraints, our algorithm seeks to generate valid sub-gradients of DPP term in case when the DPP gram matrix is not invertible (no gradients exist in this case). In this sense, our algorithm can be easily incorporated with multiple deep learning tasks. Experiments show the effectiveness of our algorithm, indicating promising performance for practical learning problems.", "output": [ "We proposed a specific back-propagation method via proper spectral sub-gradient to integrate determinantal point process to deep learning framework." ] }, { "id": "task668-02683772d19048a9b81a1afb4b17cacf", "input": "Recent advances in neural Sequence-to-Sequence (Seq2Seq) models reveal a purely data-driven approach to the response generation task. Despite its diverse variants and applications, the existing Seq2Seq models are prone to producing short and generic replies, which blocks such neural network architectures from being utilized in practical open-domain response generation tasks. In this research, we analyze this critical issue from the perspective of the optimization goal of models and the specific characteristics of human-to-human conversational corpora. Our analysis is conducted by decomposing the goal of Neural Response Generation (NRG) into the optimizations of word selection and ordering. It can be derived from the decomposing that Seq2Seq based NRG models naturally tend to select common words to compose responses, and ignore the semantic of queries in word ordering. On the basis of the analysis, we propose a max-marginal ranking regularization term to avoid Seq2Seq models from producing the generic and uninformative responses. The empirical experiments on benchmarks with several metrics have validated our analysis and proposed methodology.", "output": [ "Analyze the reason for neural response generative models preferring universal replies; Propose a method to avoid it.", "Investigates the problem of universal replies plaguing the Seq2Seq neural generation models", "The paper looks into improving the neural response generation task by deemphasizing the common responses using modification of the loss function and presentation the common/universal responses during the training phase." ] }, { "id": "task668-a5f9d99d5ef5479caf2438d97548b44b", "input": "We propose a novel framework to generate clean video frames from a single motion-blurred image.\n While a broad range of literature focuses on recovering a single image from a blurred image, in this work, we tackle a more challenging task i.e. video restoration from a blurred image. We formulate video restoration from a single blurred image as an inverse problem by setting clean image sequence and their respective motion as latent factors, and the blurred image as an observation. Our framework is based on an encoder-decoder structure with spatial transformer network modules to restore a video sequence and its underlying motion in an end-to-end manner. We design a loss function and regularizers with complementary properties to stabilize the training and analyze variant models of the proposed network. The effectiveness and transferability of our network are highlighted through a large set of experiments on two different types of datasets: camera rotation blurs generated from panorama scenes and dynamic motion blurs in high speed videos. Our code and models will be publicly available.", "output": [ "We present a novel unified architecture that restores video frames from a single motion-blurred image in an end-to-end manner." ] }, { "id": "task668-ef08879c85b84695bada2f9a47a0967c", "input": " Several recently proposed stochastic optimization methods that have been successfully used in training deep networks such as RMSProp, Adam, Adadelta, Nadam are based on using gradient updates scaled by square roots of exponential moving averages of squared past gradients. In many applications, e.g. learning with large output spaces, it has been empirically observed that these algorithms fail to converge to an optimal solution (or a critical point in nonconvex settings). We show that one cause for such failures is the exponential moving average used in the algorithms. We provide an explicit example of a simple convex optimization setting where Adam does not converge to the optimal solution, and describe the precise problems with the previous analysis of Adam algorithm. Our analysis suggests that the convergence issues can be fixed by endowing such algorithms with ``long-term memory'' of past gradients, and propose new variants of the Adam algorithm which not only fix the convergence issues but often also lead to improved empirical performance.", "output": [ "We investigate the convergence of popular optimization algorithms like Adam , RMSProp and propose new variants of these methods which provably converge to optimal solution in convex settings. " ] }, { "id": "task668-5ed63bb2bd7c4bb2a6226607dfd30483", "input": "We describe a simple and general neural network weight compression approach, in which the network parameters (weights and biases) are represented in a “latent” space, amounting to a reparameterization. This space is equipped with a learned probability model, which is used to impose an entropy penalty on the parameter representation during training, and to compress the representation using a simple arithmetic coder after training. Classification accuracy and model compressibility is maximized jointly, with the bitrate--accuracy trade-off specified by a hyperparameter. We evaluate the method on the MNIST, CIFAR-10 and ImageNet classification benchmarks using six distinct model architectures. Our results show that state-of-the-art model compression can be achieved in a scalable and general way without requiring complex procedures such as multi-stage training.", "output": [ "An end-to-end trainable model compression method optimizing accuracy jointly with the expected model size." ] }, { "id": "task668-4fd048a82ffb493d804c7064df0e68db", "input": "Significant strides have been made toward designing better generative models in recent years. Despite this progress, however, state-of-the-art approaches are still largely unable to capture complex global structure in data. For example, images of buildings typically contain spatial patterns such as windows repeating at regular intervals; state-of-the-art generative methods can’t easily reproduce these structures. We propose to address this problem by incorporating programs representing global structure into the generative model—e.g., a 2D for-loop may represent a configuration of windows. Furthermore, we propose a framework for learning these models by leveraging program synthesis to generate training data. On both synthetic and real-world data, we demonstrate that our approach is substantially better than the state-of-the-art at both generating and completing images that contain global structure.\n", "output": [ "Applying program synthesis to the tasks of image completion and generation within a deep learning framework" ] }, { "id": "task668-a2dad13746d54b55910bf0aa2f13cc7f", "input": "Neural networks are commonly used as models for classification for a wide variety of tasks. Typically, a learned affine transformation is placed at the end of such models, yielding a per-class value used for classification. This classifier can have a vast number of parameters, which grows linearly with the number of possible classes, thus requiring increasingly more resources.\n\n In this work we argue that this classifier can be fixed, up to a global scale constant, with little or no loss of accuracy for most tasks, allowing memory and computational benefits. Moreover, we show that by initializing the classifier with a Hadamard matrix we can speed up inference as well. We discuss the implications for current understanding of neural network models.\n", "output": [ "You can fix the classifier in neural networks without losing accuracy" ] }, { "id": "task668-72ed1a50dbc04fcd8d7b4de94d831f82", "input": "Deep neural networks (DNNs) perform well on a variety of tasks despite the fact that most used in practice are vastly overparametrized and even capable of perfectly fitting randomly labeled data. Recent evidence suggests that developing \"compressible\" representations is key for adjusting the complexity of overparametrized networks to the task at hand and avoiding overfitting (Arora et al., 2018; Zhou et al., 2018). In this paper, we provide new empirical evidence that supports this hypothesis, identifying two independent mechanisms that emerge when the network’s width is increased: robustness (having units that can be removed without affecting accuracy) and redundancy (having units with similar activity). In a series of experiments with AlexNet, ResNet and Inception networks in the CIFAR-10 and ImageNet datasets, and also using shallow networks with synthetic data, we show that DNNs consistently increase either their robustness, their redundancy, or both at greater widths for a comprehensive set of hyperparameters. These results suggest that networks in the deep learning regime adjust their effective capacity by developing either robustness or redundancy.", "output": [ "Probing robustness and redundancy in deep neural networks reveals capacity-constraining features which help to explain non-overfitting." ] }, { "id": "task668-963b5ecd04db407785c54c521494f4e3", "input": "We propose a novel framework for combining datasets via alignment of their associated intrinsic dimensions. Our approach assumes that the two datasets are sampled from a common latent space, i.e., they measure equivalent systems. Thus, we expect there to exist a natural (albeit unknown) alignment of the data manifolds associated with the intrinsic geometry of these datasets, which are perturbed by measurement artifacts in the sampling process. Importantly, we do not assume any individual correspondence (partial or complete) between data points. Instead, we rely on our assumption that a subset of data features have correspondence across datasets. We leverage this assumption to estimate relations between intrinsic manifold dimensions, which are given by diffusion map coordinates over each of the datasets. We compute a correlation matrix between diffusion coordinates of the datasets by considering graph (or manifold) Fourier coefficients of corresponding data features. We then orthogonalize this correlation matrix to form an isometric transformation between the diffusion maps of the datasets. Finally, we apply this transformation to the diffusion coordinates and construct a unified diffusion geometry of the datasets together. We show that this approach successfully corrects misalignment artifacts, and allows for integrated data.", "output": [ "We propose a method for aligning the latent features learned from different datasets using harmonic correlations.", "Proposes using feature correspondences to preform manifold alignment between batches of data from the same samples to avoid the collection of noisy measurements." ] }, { "id": "task668-bc2d43339e82432e89de2f83341dd986", "input": "Ability to quantify and predict progression of a disease is fundamental for selecting an appropriate treatment. Many clinical metrics cannot be acquired frequently either because of their cost (e.g. MRI, gait analysis) or because they are inconvenient or harmful to a patient (e.g. biopsy, x-ray). In such scenarios, in order to estimate individual trajectories of disease progression, it is advantageous to leverage similarities between patients, i.e. the covariance of trajectories, and find a latent representation of progression. Most of existing methods for estimating trajectories do not account for events in-between observations, what dramatically decreases their adequacy for clinical practice. In this study, we develop a machine learning framework named Coordinatewise-Soft-Impute (CSI) for analyzing disease progression from sparse observations in the presence of confounding events. CSI is guaranteed to converge to the global minimum of the corresponding optimization problem. Experimental results also demonstrates the effectiveness of CSI using both simulated and real dataset.", "output": [ "A novel matrix completion based algorithm to model disease progression with events" ] }, { "id": "task668-f8d8a19ea999475caad8861a223c8d9a", "input": "While many recent advances in deep reinforcement learning rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsupervised data to learn environment dynamics. One prospect is to pursue hybrid approaches, as in AlphaGo, which combines Monte-Carlo Tree Search (MCTS)—a model-based method—with deep-Q networks (DQNs)—a model-free method. MCTS requires generating rollouts, which is computationally expensive. In this paper, we propose to simulate roll-outs, exploiting the latest breakthroughs in image-to-image transduction, namely Pix2Pix GANs, to predict the dynamics of the environment. Our proposed algorithm, generative adversarial tree search (GATS), simulates rollouts up to a specified depth using both a GAN- based dynamics model and a reward predictor. GATS employs MCTS for planning over the simulated samples and uses DQN to estimate the Q-function at the leaf states. Our theoretical analysis establishes some favorable properties of GATS vis-a-vis the bias-variance trade-off and empirical results show that on 5 popular Atari games, the dynamics and reward predictors converge quickly to accurate solutions. However, GATS fails to outperform DQNs in 4 out of 5 games. Notably, in these experiments, MCTS has only short rollouts (up to tree depth 4), while previous successes of MCTS have involved tree depth in the hundreds. We present a hypothesis for why tree search with short rollouts can fail even given perfect modeling.", "output": [ "Surprising negative results on Model Based + Model deep RL" ] }, { "id": "task668-c66341b4700a40fa91717375642fef90", "input": " Variational Auto-Encoders (VAEs) are designed to capture compressible information about a dataset. As a consequence the information stored in the latent space is seldom sufficient to reconstruct a particular image. To help understand the type of information stored in the latent space we train a GAN-style decoder constrained to produce images that the VAE encoder will map to the same region of latent space. This allows us to ''imagine'' the information captured in the latent space. We argue that this is necessary to make a VAE into a truly generative model. We use our GAN to visualise the latent space of a standard VAE and of a $\\beta$-VAE.", "output": [ "To understand the information stored in the latent space, we train a GAN-style decoder constrained to produce images that the VAE encoder will map to the same region of latent space." ] }, { "id": "task668-02685a23501a41cba79d4b345e68a8e8", "input": "\tA recent line of work has studied the statistical properties of neural networks to great success from a {\\it mean field theory} perspective, making and verifying very precise predictions of neural network behavior and test time performance.\n\t In this paper, we build upon these works to explore two methods for taming the behaviors of random residual networks (with only fully connected layers and no batchnorm).\n\t The first method is {\\it width variation (WV)}, i.e. varying the widths of layers as a function of depth.\n\t We show that width decay reduces gradient explosion without affecting the mean forward dynamics of the random network.\n\t The second method is {\\it variance variation (VV)}, i.e. changing the initialization variances of weights and biases over depth.\n\t We show VV, used appropriately, can reduce gradient explosion of tanh and ReLU resnets from $\\exp(\\Theta(\\sqrt L))$ and $\\exp(\\Theta(L))$ respectively to constant $\\Theta(1)$.\n\t A complete phase-diagram is derived for how variance decay affects different dynamics, such as those of gradient and activation norms.\n\t In particular, we show the existence of many phase transitions where these dynamics switch between exponential, polynomial, logarithmic, and even constant behaviors.\n\t Using the obtained mean field theory, we are able to track surprisingly well how VV at initialization time affects training and test time performance on MNIST after a set number of epochs: the level sets of test/train set accuracies coincide with the level sets of the expectations of certain gradient norms or of metric expressivity (as defined in \\cite{yang_meanfield_2017}), a measure of expansion in a random neural network.\n\t Based on insights from past works in deep mean field theory and information geometry, we also provide a new perspective on the gradient explosion/vanishing problems: they lead to ill-conditioning of the Fisher information matrix, causing optimization troubles.", "output": [ "By setting the width or the initialization variance of each layer differently, we can actually subdue gradient explosion problems in residual networks (with fully connected layers and no batchnorm). A mathematical theory is developed that not only tells you how to do it, but also surprisingly is able to predict, after you apply such tricks, how fast your network trains to achieve a certain test set performance. This is some black magic stuff, and it's called \"Deep Mean Field Theory.\"" ] }, { "id": "task668-eb69b2223e0e48c1938662ce26ad0794", "input": "While machine learning models achieve human-comparable performance on sequential data, exploiting structured knowledge is still a challenging problem. Spatio-temporal graphs have been proved to be a useful tool to abstract interaction graphs and previous works exploits carefully designed feed-forward architecture to preserve such structure. We argue to scale such network design to real-world problem, a model needs to automatically learn a meaningful representation of the possible relations. Learning such interaction structure is not trivial: on the one hand, a model has to discover the hidden relations between different problem factors in an unsupervised way; on the other hand, the mined relations have to be interpretable. \n\n In this paper, we propose an attention module able to project a graph sub-structure in a fixed size embedding, preserving the influence that the neighbours exert on a given vertex. On a comprehensive evaluation done on real-world as well as toy task, we found our model competitive against strong baselines.", "output": [ "A graph neural network able to automatically learn and leverage a dynamic interactive graph structure" ] }, { "id": "task668-e67d0ea2ee754008b06a1cc9afbecc1c", "input": "We propose a new sample-efficient methodology, called Supervised Policy Update (SPU), for deep reinforcement learning. Starting with data generated by the current policy, SPU formulates and solves a constrained optimization problem in the non-parameterized proximal policy space. Using supervised regression, it then converts the optimal non-parameterized policy to a parameterized policy, from which it draws new samples. The methodology is general in that it applies to both discrete and continuous action spaces, and can handle a wide variety of proximity constraints for the non-parameterized optimization problem. We show how the Natural Policy Gradient and Trust Region Policy Optimization (NPG/TRPO) problems, and the Proximal Policy Optimization (PPO) problem can be addressed by this methodology. The SPU implementation is much simpler than TRPO. In terms of sample efficiency, our extensive experiments show SPU outperforms TRPO in Mujoco simulated robotic tasks and outperforms PPO in Atari video game tasks.", "output": [ "first posing and solving the sample efficiency optimization problem in the non-parameterized policy space, and then solving a supervised regression problem to find a parameterized policy that is near the optimal non-parameterized policy." ] }, { "id": "task668-244a823e5fdc4885b43cc09dce991867", "input": "We propose a robust Bayesian deep learning algorithm to infer complex posteriors with latent variables. Inspired by dropout, a popular tool for regularization and model ensemble, we assign sparse priors to the weights in deep neural networks (DNN) in order to achieve automatic “dropout” and avoid over-fitting. By alternatively sampling from posterior distribution through stochastic gradient Markov Chain Monte Carlo (SG-MCMC) and optimizing latent variables via stochastic approximation (SA), the trajectory of the target weights is proved to converge to the true posterior distribution conditioned on optimal latent variables. This ensures a stronger regularization on the over-fitted parameter space and more accurate uncertainty quantification on the decisive variables. Simulations from large-p-small-n regressions showcase the robustness of this method when applied to models with latent variables. Additionally, its application on the convolutional neural networks (CNN) leads to state-of-the-art performance on MNIST and Fashion MNIST datasets and improved resistance to adversarial attacks.", "output": [ "a robust Bayesian deep learning algorithm to infer complex posteriors with latent variables" ] }, { "id": "task668-6d645a326bbe4e6c97b4bf6d52f2189e", "input": " Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training. In this work, we propose to train a policy while explicitly penalizing the mismatch between these two distributions over a fixed time horizon. We do this by using a learned model of the environment dynamics which is unrolled for multiple time steps, and training a policy network to minimize a differentiable cost over this rolled-out trajectory. This cost contains two terms: a policy cost which represents the objective the policy seeks to optimize, and an uncertainty cost which represents its divergence from the states it is trained on. We propose to measure this second cost by using the uncertainty of the dynamics model about its own predictions, using recent ideas from uncertainty estimation for deep networks. We evaluate our approach using a large-scale observational dataset of driving behavior recorded from traffic cameras, and show that we are able to learn effective driving policies from purely observational data, with no environment interaction.", "output": [ "A model-based RL approach which uses a differentiable uncertainty penalty to learn driving policies from purely observational data." ] }, { "id": "task668-fb35d9fc6c0a45f1bc6f143a954203fe", "input": "For multi-valued functions---such as when the conditional distribution on targets given the inputs is multi-modal---standard regression approaches are not always desirable because they provide the conditional mean. Modal regression approaches aim to instead find the conditional mode, but are restricted to nonparametric approaches. Such approaches can be difficult to scale, and make it difficult to benefit from parametric function approximation, like neural networks, which can learn complex relationships between inputs and targets. In this work, we propose a parametric modal regression algorithm, by using the implicit function theorem to develop an objective for learning a joint parameterized function over inputs and targets. We empirically demonstrate on several synthetic problems that our method (i) can learn multi-valued functions and produce the conditional modes, (ii) scales well to high-dimensional inputs and (iii) is even more effective for certain unimodal problems, particularly for high frequency data where the joint function over inputs and targets can better capture the complex relationship between them. We conclude by showing that our method provides small improvements on two regression datasets that have asymmetric distributions over the targets.", "output": [ "We introduce a simple and novel modal regression algorithm which is easy to scale to large problems. ", "The paper proposes an implicit function approach to learning the modes of multimodal regression.", "The present work proposes a parametric approach to estimate the conditional mode using the Implicit Function Theorem for multi-modal distributions. " ] }, { "id": "task668-d107cb4dd0f54f698218a01eb7546a44", "input": "Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging. Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of natural images requires the predictive model to build an intricate understanding of the natural world. Many existing methods tackle this problem by making simplifying assumptions about the environment. One common assumption is that the outcome is deterministic and there is only one plausible future. This can lead to low-quality predictions in real-world settings with stochastic dynamics. In this paper, we develop a stochastic variational video prediction (SV2P) method that predicts a different possible future for each sample of its latent variables. To the best of our knowledge, our model is the first to provide effective stochastic multi-frame prediction for real-world video. We demonstrate the capability of the proposed method in predicting detailed future frames of videos on multiple real-world datasets, both action-free and action-conditioned. We find that our proposed method produces substantially improved video predictions when compared to the same model without stochasticity, and to other stochastic video prediction methods. Our SV2P implementation will be open sourced upon publication.", "output": [ "Stochastic variational video prediction in real-world settings." ] }, { "id": "task668-563bb1ec7cbf4c6aa2ff35c19d839d9d", "input": "Generative adversarial networks (GANs) are an expressive class of neural generative models with tremendous success in modeling high-dimensional continuous measures. In this paper, we present a scalable method for unbalanced optimal transport (OT) based on the generative-adversarial framework. We formulate unbalanced OT as a problem of simultaneously learning a transport map and a scaling factor that push a source measure to a target measure in a cost-optimal manner. We provide theoretical justification for this formulation, showing that it is closely related to an existing static formulation by Liero et al. (2018). We then propose an algorithm for solving this problem based on stochastic alternating gradient updates, similar in practice to GANs, and perform numerical experiments demonstrating how this methodology can be applied to population modeling.", "output": [ "We propose new methodology for unbalanced optimal transport using generative adversarial networks.", "The authors consider the unbalanced optimal transport problem between two measures with different total mass using a stochastic min-max algorithm and local scaling", "The authors propose an approach to estimate unbalanced optimal transport between sampled measures that scales well in the dimension and in the number of samples.", "The paper introduces a static formulation for unbalanced optimal transport by learning simultaneously a transport map T and scaling factor xi." ] }, { "id": "task668-c0ab23d6f38949b4837d30f11b27fdae", "input": "Despite their ability to memorize large datasets, deep neural networks often achieve good generalization performance. However, the differences between the learned solutions of networks which generalize and those which do not remain unclear. Additionally, the tuning properties of single directions (defined as the activation of a single unit or some linear combination of units in response to some input) have been highlighted, but their importance has not been evaluated. Here, we connect these lines of inquiry to demonstrate that a network’s reliance on single directions is a good predictor of its generalization performance, across networks trained on datasets with different fractions of corrupted labels, across ensembles of networks trained on datasets with unmodified labels, across different hyper- parameters, and over the course of training. While dropout only regularizes this quantity up to a point, batch normalization implicitly discourages single direction reliance, in part by decreasing the class selectivity of individual units. Finally, we find that class selectivity is a poor predictor of task importance, suggesting not only that networks which generalize well minimize their dependence on individual units by reducing their selectivity, but also that individually selective units may not be necessary for strong network performance.", "output": [ "We find that deep networks which generalize poorly are more reliant on single directions than those that generalize well, and evaluate the impact of dropout and batch normalization, as well as class selectivity on single direction reliance." ] }, { "id": "task668-53a122ef41504931a91209824ac0c92c", "input": "Amortized inference has led to efficient approximate inference for large datasets. The quality of posterior inference is largely determined by two factors: a) the ability of the variational distribution to model the true posterior and b) the capacity of the recognition network to generalize inference over all datapoints. We analyze approximate inference in variational autoencoders in terms of these factors. We find that suboptimal inference is often due to amortizing inference rather than the limited complexity of the approximating distribution. We show that this is due partly to the generator learning to accommodate the choice of approximation. Furthermore, we show that the parameters used to increase the expressiveness of the approximation play a role in generalizing inference rather than simply improving the complexity of the approximation.", "output": [ "We decompose the gap between the marginal log-likelihood and the evidence lower bound and study the effect of the approximate posterior on the true posterior distribution in VAEs." ] }, { "id": "task668-34663c521028451abe447013fa250c15", "input": "Mini-batch gradient descent and its variants are commonly used in deep learning. The principle of mini-batch gradient descent is to use noisy gradient calculated on a batch to estimate the real gradient, thus balancing the computation cost per iteration and the uncertainty of noisy gradient. However, its batch size is a fixed hyper-parameter requiring manual setting before training the neural network. Yin et al. (2017) proposed a batch adaptive stochastic gradient descent (BA-SGD) that can dynamically choose a proper batch size as learning proceeds. We extend the BA-SGD to momentum algorithm and evaluate both the BA-SGD and the batch adaptive momentum (BA-Momentum) on two deep learning tasks from natural language processing to image classification. Experiments confirm that batch adaptive methods can achieve a lower loss compared with mini-batch methods after scanning the same epochs of data. Furthermore, our BA-Momentum is more robust against larger step sizes, in that it can dynamically enlarge the batch size to reduce the larger uncertainty brought by larger step sizes. We also identified an interesting phenomenon, batch size boom. The code implementing batch adaptive framework is now open source, applicable to any gradient-based optimization problems.", "output": [ "We developed a batch adaptive momentum that can achieve lower loss compared with mini-batch methods after scanning same epochs of data, and it is more robust against large step size.", "This paper addresses the problem of automatically tuning batch size during deep learning training, and claims to extend batch adaptive SGD to adaptive momentum and adopt the algorithms to complex neural networks problems.", "The paper proposes generalizing an algorithm which performs SGD with adaptive batch sizes by adding momentum to the utility function" ] }, { "id": "task668-1882d1e9cfc44f2392dc5730a7f85624", "input": "Various gradient compression schemes have been proposed to mitigate the communication cost in distributed training of large scale machine learning models. Sign-based methods, such as signSGD (Bernstein et al., 2018), have recently been gaining popularity because of their simple compression rule and connection to adaptive gradient methods, like ADAM. In this paper, we perform a general analysis of sign-based methods for non-convex optimization. Our analysis is built on intuitive bounds on success probabilities and does not rely on special noise distributions nor on the boundedness of the variance of stochastic gradients. Extending the theory to distributed setting within a parameter server framework, we assure exponentially fast variance reduction with respect to number of nodes, maintaining 1-bit compression in both directions and using small mini-batch sizes. We validate our theoretical findings experimentally.", "output": [ "General analysis of sign-based methods (e.g. signSGD) for non-convex optimization, built on intuitive bounds on success probabilities." ] }, { "id": "task668-89bf22c8708747a78352adb1bdf73c0e", "input": "Training Generative Adversarial Networks (GANs) is notoriously challenging. We propose and study an architectural modification, self-modulation, which improves GAN performance across different data sets, architectures, losses, regularizers, and hyperparameter settings. Intuitively, self-modulation allows the intermediate feature maps of a generator to change as a function of the input noise vector. While reminiscent of other conditioning techniques, it requires no labeled data. In a large-scale empirical study we observe a relative decrease of 5%-35% in FID. Furthermore, all else being equal, adding this modification to the generator leads to improved performance in 124/144 (86%) of the studied settings. Self-modulation is a simple architectural change that requires no additional parameter tuning, which suggests that it can be applied readily to any GAN.", "output": [ "A simple GAN modification that improves performance across many losses, architectures, regularization schemes, and datasets. " ] }, { "id": "task668-d71c719b032b47338a5e668ca0527499", "input": "We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference. The great success of DNNs motivates the pursuing of lightweight models for the deployment onto embedded devices. However, most of the previous studies optimize for inference while neglect training or even complicate it. Training is far more intractable, since (i) the neurons dominate the memory cost rather than the weights in inference; (ii) the dynamic activation makes previous sparse acceleration via one-off optimization on fixed weight invalid; (iii) batch normalization (BN) is critical for maintaining accuracy while its activation reorganization damages the sparsity. To address these issues, DSG activates only a small amount of neurons with high selectivity at each iteration via a dimensionreduction search and obtains the BN compatibility via a double-mask selection. Experiments show significant memory saving (1.7-4.5x) and operation reduction (2.3-4.4x) with little accuracy loss on various benchmarks.", "output": [ "We construct dynamic sparse graph via dimension-reduction search to reduce compute and memory cost in both DNN training and inference.", "The authors propose to use dynamic sparse computation graph for reducing the computation memory and time cost in deep neural network (DNN).", "This paper proposes a method to speed up training and inference of deep neural networks using dynamic pruning of the compute graph." ] }, { "id": "task668-e7ce2528c37d40289c06523b4422bf0f", "input": "Relational databases store a significant amount of the worlds data. However, accessing this data currently requires users to understand a query language such as SQL. We propose Seq2SQL, a deep neural network for translating natural language questions to corresponding SQL queries. Our model uses rewards from in the loop query execution over the database to learn a policy to generate the query, which contains unordered parts that are less suitable for optimization via cross entropy loss. Moreover, Seq2SQL leverages the structure of SQL to prune the space of generated queries and significantly simplify the generation problem. In addition to the model, we release WikiSQL, a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables fromWikipedia that is an order of magnitude larger than comparable datasets. By applying policy based reinforcement learning with a query execution environment to WikiSQL, Seq2SQL outperforms a state-of-the-art semantic parser, improving execution accuracy from 35.9% to 59.4% and logical form accuracy from 23.4% to 48.3%.", "output": [ "We introduce Seq2SQL, which translates questions to SQL queries using rewards from online query execution, and WikiSQL, a SQL table/question/query dataset orders of magnitude larger than existing datasets.", "A new semantic parsing dataset which focuses on generating SQL from natural language using a reinforcement-learning based model" ] }, { "id": "task668-d59e73fbfd34487a9688c5a857d3a05c", "input": "For many applications, in particular in natural science, the task is to\n determine hidden system parameters from a set of measurements. Often,\n the forward process from parameter- to measurement-space is well-defined,\n whereas the inverse problem is ambiguous: multiple parameter sets can\n result in the same measurement. To fully characterize this ambiguity, the full\n posterior parameter distribution, conditioned on an observed measurement,\n has to be determined. We argue that a particular class of neural networks\n is well suited for this task – so-called Invertible Neural Networks (INNs).\n Unlike classical neural networks, which attempt to solve the ambiguous\n inverse problem directly, INNs focus on learning the forward process, using\n additional latent output variables to capture the information otherwise\n lost. Due to invertibility, a model of the corresponding inverse process is\n learned implicitly. Given a specific measurement and the distribution of\n the latent variables, the inverse pass of the INN provides the full posterior\n over parameter space. We prove theoretically and verify experimentally, on\n artificial data and real-world problems from medicine and astrophysics, that\n INNs are a powerful analysis tool to find multi-modalities in parameter space,\n uncover parameter correlations, and identify unrecoverable parameters.", "output": [ "To analyze inverse problems with Invertible Neural Networks", "The author proposes to use invertible networks to solve ambiguous inverse problems and suggest to not only train the forward model, but also the inverse model with an MMD critic.", "The research paper proposes an invertible network with observations for posterior probability of complex input distributions with a theoretical valid bidirectional training scheme.\n" ] }, { "id": "task668-1d21052d02774b5dbc86eb2ef4a8664f", "input": "There have been several studies recently showing that strong natural language understanding (NLU) models are prone to relying on unwanted dataset biases without learning the underlying task, resulting in models which fail to generalize to out-of-domain datasets, and are likely to perform poorly in real-world scenarios. We propose several learning strategies to train neural models which are more robust to such biases and transfer better to out-of-domain datasets. We introduce an additional lightweight bias-only model which learns dataset biases and uses its prediction to adjust the loss of the base model to reduce the biases. In other words, our methods down-weight the importance of the biased examples, and focus training on hard examples, i.e. examples that cannot be correctly classified by only relying on biases. Our approaches are model agnostic and simple to implement. We experiment on large-scale natural language inference and fact verification datasets and their out-of-domain datasets and show that our debiased models significantly improve the robustness in all settings, including gaining 9.76 points on the FEVER symmetric evaluation dataset, 5.45 on the HANS dataset and 4.78 points on the SNLI hard set. These datasets are specifically designed to assess the robustness of models in the out-of-domain setting where typical biases in the training data do not exist in the evaluation set.\n", "output": [ "We propose several general debiasing strategies to address common biases seen in different datasets and obtain substantial improved out-of-domain performance in all settings." ] }, { "id": "task668-4c6f8fd78bf0454ea72060a5c8fa7ca7", "input": "Representations learnt through deep neural networks tend to be highly informative, but opaque in terms of what information they learn to encode. We introduce an approach to probabilistic modelling that learns to represent data with two separate deep representations: an invariant representation that encodes the information of the class from which the data belongs, and an equivariant representation that encodes the symmetry transformation defining the particular data point within the class manifold (equivariant in the sense that the representation varies naturally with symmetry transformations). This approach to representation learning is conceptually transparent, easy to implement, and in-principle generally applicable to any data comprised of discrete classes of continuous distributions (e.g. objects in images, topics in language, individuals in behavioural data). We demonstrate qualitatively compelling representation learning and competitive quantitative performance, in both supervised and semi-supervised settings, versus comparable modelling approaches in the literature with little fine tuning.", "output": [ "This paper presents a novel latent-variable generative modelling technique that enables the representation of global information into one latent variable and local information into another latent variable.", "The paper presents a VAE that uses labels to separate the learned representation into an invariant and a covariant part." ] }, { "id": "task668-23e29244d797484980b6f39315492e3c", "input": "Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To augment the compound design process we introduce Mol-CycleGAN -- a CycleGAN-based model that generates optimized compounds with a chemical scaffold of interest. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results.", "output": [ "We introduce Mol-CycleGAN - a new generative model for optimization of molecules to augment drug design.", "The paper presents an approach for optimizing molecular properties based on the application of CycleGANs to variational autoencoders for molecules and employs a domain-specific VAE called Junction Tree VAE (JT-VAE).", "This paper uses a variational autoencoders to learn a translation function, from the set of molecules without the interested property to the set of molecules with the property. " ] }, { "id": "task668-ecb3daf78857452c83c01faa4949bb89", "input": "Transfer learning for feature extraction can be used to exploit deep representations in contexts where there is very few training data, where there are limited computational resources, or when tuning the hyper-parameters needed for training is not an option. While previous contributions to feature extraction propose embeddings based on a single layer of the network, in this paper we propose a full-network embedding which successfully integrates convolutional and fully connected features, coming from all layers of a deep convolutional neural network. To do so, the embedding normalizes features in the context of the problem, and discretizes their values to reduce noise and regularize the embedding space. Significantly, this also reduces the computational cost of processing the resultant representations. The proposed method is shown to outperform single layer embeddings on several image classification tasks, while also being more robust to the choice of the pre-trained model used for obtaining the initial features. The performance gap in classification accuracy between thoroughly tuned solutions and the full-network embedding is also reduced, which makes of the proposed approach a competitive solution for a large set of applications.", "output": [ "We present a full-network embedding of CNN which outperforms single layer embeddings for transfer learning tasks." ] }, { "id": "task668-976b419285704e14891547432994b8ba", "input": "Deep learning has found numerous applications thanks to its versatility and accuracy on pattern recognition problems such as visual object detection. Learning and inference in deep neural networks, however, are memory and compute intensive and so improving efficiency is one of the major challenges for frameworks such as PyTorch, Tensorflow, and Caffe. While the efficiency problem can be partially addressed with specialized hardware and its corresponding proprietary libraries, we believe that neural network acceleration should be transparent to the user and should support all hardware platforms and deep learning libraries. \n\n To this end, we introduce a transparent middleware layer for neural network acceleration. The system is built around a compiler for deep learning, allowing one to combine device-specific libraries and custom optimizations while supporting numerous hardware devices. In contrast to other projects, we explicitly target the optimization of both prediction and training of neural networks. We present the current development status and some preliminary but encouraging results: on a standard x86 server, using CPUs our system achieves a 11.8x speed-up for inference and a 8.0x for batched-prediction (128); on GPUs we achieve a 1.7x and 2.3x speed-up respectively.", "output": [ "We introduce a transparent middleware for neural network acceleration, with own compiler engine, achieving up to 11.8x speed up on CPUs and 2.3x on GPUs.", "This paper proposes a transparent middleware layer for neural network acceleration and obtains some acceleration results on basic CPU and GPU architectures" ] }, { "id": "task668-69164d1ada9a411a80e9fec42de5cc8e", "input": "Neural networks trained only to optimize for training accuracy can often be fooled by adversarial examples --- slightly perturbed inputs misclassified with high confidence. Verification of networks enables us to gauge their vulnerability to such adversarial examples. We formulate verification of piecewise-linear neural networks as a mixed integer program. On a representative task of finding minimum adversarial distortions, our verifier is two to three orders of magnitude quicker than the state-of-the-art. We achieve this computational speedup via tight formulations for non-linearities, as well as a novel presolve algorithm that makes full use of all information available. The computational speedup allows us to verify properties on convolutional and residual networks with over 100,000 ReLUs --- several orders of magnitude more than networks previously verified by any complete verifier. In particular, we determine for the first time the exact adversarial accuracy of an MNIST classifier to perturbations with bounded l-∞ norm ε=0.1: for this classifier, we find an adversarial example for 4.38% of samples, and a certificate of robustness to norm-bounded perturbations for the remainder. Across all robust training procedures and network architectures considered, and for both the MNIST and CIFAR-10 datasets, we are able to certify more samples than the state-of-the-art and find more adversarial examples than a strong first-order attack.", "output": [ "We efficiently verify the robustness of deep neural models with over 100,000 ReLUs, certifying more samples than the state-of-the-art and finding more adversarial examples than a strong first-order attack.", "Performs a careful study of mixed integer linear programming approaches for verifying robustness of neural networks to adversarial perturbations and proposes three enhancements to MILP formulations of neural network verification." ] }, { "id": "task668-ad3fed4f70ec4e97bd602b9b3e20cdf9", "input": "We consider the dictionary learning problem, where the aim is to model the given data as a linear combination of a few columns of a matrix known as a dictionary, where the sparse weights forming the linear combination are known as coefficients. Since the dictionary and coefficients, parameterizing the linear model are unknown, the corresponding optimization is inherently non-convex. This was a major challenge until recently, when provable algorithms for dictionary learning were proposed. Yet, these provide guarantees only on the recovery of the dictionary, without explicit recovery guarantees on the coefficients. Moreover, any estimation error in the dictionary adversely impacts the ability to successfully localize and estimate the coefficients. This potentially limits the utility of existing provable dictionary learning methods in applications where coefficient recovery is of interest. To this end, we develop NOODL: a simple Neurally plausible alternating Optimization-based Online Dictionary Learning algorithm, which recovers both the dictionary and coefficients exactly at a geometric rate, when initialized appropriately. Our algorithm, NOODL, is also scalable and amenable for large scale distributed implementations in neural architectures, by which we mean that it only involves simple linear and non-linear operations. Finally, we corroborate these theoretical results via experimental evaluation of the proposed algorithm with the current state-of-the-art techniques.", "output": [ "We present a provable algorithm for exactly recovering both factors of the dictionary learning model. " ] }, { "id": "task668-4057ca60c21b49c089f153df2b1d31b2", "input": "Language modeling tasks, in which words are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases. Motivated by the observation that efforts to code\n world knowledge into machine readable knowledge bases tend to be entity-centric,\n we investigate the use of a fill-in-the-blank task to learn context independent representations of entities from the contexts in which those entities were mentioned.\n We show that large scale training of neural models allows us to learn extremely high fidelity entity typing information, which we demonstrate with few-shot reconstruction of Wikipedia categories. Our learning approach is powerful enough\n to encode specialized topics such as Giro d’Italia cyclists.", "output": [ "We learn entity representations that can reconstruct Wikipedia categories with just a few exemplars." ] }, { "id": "task668-415cbb7537104cceb08bf9e8ba2eb522", "input": "Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas. However, most state-of-the-art deep learning models either fail to obtain uncertainty estimation or need significant modification (e.g., formulating a proper Bayesian treatment) to obtain it. None of the previous methods are able to take an arbitrary model off the shelf and generate uncertainty estimation without retraining or redesigning it. To address this gap, we perform the first systematic exploration into training-free uncertainty estimation. \n We propose three simple and scalable methods to analyze the variance of output from a trained network under tolerable perturbations: infer-transformation, infer-noise, and infer-dropout. They operate solely during inference, without the need to re-train, re-design, or fine-tune the model, as typically required by other state-of-the-art uncertainty estimation methods. Surprisingly, even without involving such perturbations in training, our methods produce comparable or even better uncertainty estimation when compared to other training-required state-of-the-art methods. Last but not least, we demonstrate that the uncertainty from our proposed methods can be used to improve the neural network training.\n", "output": [ "A set of methods to obtain uncertainty estimation of any given model without re-designing, re-training, or to fine-tuning it.", "Describes several approaches for measuring uncertainty in arbitrary neural networks when there is an absence of distortion during training." ] }, { "id": "task668-4353b0d029f4452c9807b85d13fa7e10", "input": " We introduce Doc2Dial, an end-to-end framework for generating conversational data grounded in business documents via crowdsourcing. Such data can be used to train automated dialogue agents performing customer care tasks for the enterprises or organizations. In particular, the framework takes the documents as input and generates the tasks for obtaining the annotations for simulating dialog flows. The dialog flows are used to guide the collection of utterances produced by crowd workers. The outcomes include dialogue data grounded in the given documents, as well as various types of annotations that help ensure the quality of the data and the flexibility to (re)composite dialogues.", "output": [ "We introduce Doc2Dial, an end-to-end framework for generating conversational data grounded in business documents via crowdsourcing for train automated dialogue agents" ] }, { "id": "task668-8cf45622214f43a6b8438be6860871ec", "input": "Plan recognition aims to look for target plans to best explain the observed actions based on plan libraries and/or domain models. Despite the success of previous approaches on plan recognition, they mostly rely on correct action observations. \n Recent advances in visual activity recognition have the potential of enabling applications such as automated video surveillance. Effective approaches for such problems would require the ability to recognize the plans of agents from video information. Traditional plan recognition algorithms rely on access to detailed planning domain models. One recent promising direction involves learning approximate (or shallow) domain models directly from the observed activity sequences. Such plan recognition approaches expect observed action sequences as inputs. However, visual inference results are often noisy and uncertain, typically represented as a distribution over possible actions. In this work, we develop a visual plan recognition framework that recognizes plans with an approximate domain model learned from uncertain visual data.", "output": [ "Handling Uncertainty in Visual Perception for Plan Recognition" ] }, { "id": "task668-adf574e9ab104958af0a825d36e616a2", "input": "Knowledge Distillation (KD) is a common method for transferring the ``knowledge'' learned by one machine learning model (the teacher) into another model (the student), where typically, the teacher has a greater capacity (e.g., more parameters or higher bit-widths). To our knowledge, existing methods overlook the fact that although the student absorbs extra knowledge from the teacher, both models share the same input data -- and this data is the only medium by which the teacher's knowledge can be demonstrated. Due to the difference in model capacities, the student may not benefit fully from the same data points on which the teacher is trained. On the other hand, a human teacher may demonstrate a piece of knowledge with individualized examples adapted to a particular student, for instance, in terms of her cultural background and interests. Inspired by this behavior, we design data augmentation agents with distinct roles to facilitate knowledge distillation. Our data augmentation agents generate distinct training data for the teacher and student, respectively. We focus specifically on KD when the teacher network has greater precision (bit-width) than the student network.\n\n We find empirically that specially tailored data points enable the teacher's knowledge to be demonstrated more effectively to the student. We compare our approach with existing KD methods on training popular neural architectures and demonstrate that role-wise data augmentation improves the effectiveness of KD over strong prior approaches. The code for reproducing our results will be made publicly available.", "output": [ "We study whether and how adaptive data augmentation and knowledge distillation can be leveraged simultaneously in a synergistic manner for better training student networks." ] }, { "id": "task668-f8c240ff434d457ebd344b4e96108f71", "input": "We propose a new output layer for deep neural networks that permits the use of logged contextual bandit feedback for training. Such contextual bandit feedback can be available in huge quantities (e.g., logs of search engines, recommender systems) at little cost, opening up a path for training deep networks on orders of magnitude more data. To this effect, we propose a Counterfactual Risk Minimization (CRM) approach for training deep networks using an equivariant empirical risk estimator with variance regularization, BanditNet, and show how the resulting objective can be decomposed in a way that allows Stochastic Gradient Descent (SGD) training. We empirically demonstrate the effectiveness of the method by showing how deep networks -- ResNets in particular -- can be trained for object recognition without conventionally labeled images.", "output": [ "The paper proposes a new output layer for deep networks that permits the use of logged contextual bandit feedback for training. " ] }, { "id": "task668-76c53087f012435092a0b43f94113ec6", "input": "A lot of the recent success in natural language processing (NLP) has been driven by distributed vector representations of words trained on large amounts of text in an unsupervised manner. These representations are typically used as general purpose features for words across a range of NLP problems. However, extending this success to learning representations of sequences of words, such as sentences, remains an open problem. Recent work has explored unsupervised as well as supervised learning techniques with different training objectives to learn general purpose fixed-length sentence representations. In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model. \n We train this model on several data sources with multiple training objectives on over 100 million sentences. Extensive experiments demonstrate that sharing a single recurrent sentence encoder across weakly related tasks leads to consistent improvements over previous methods. We present substantial improvements in the context of transfer learning and low-resource settings using our learned general-purpose representations.", "output": [ "A large-scale multi-task learning framework with diverse training objectives to learn fixed-length sentence representations", "This paper is about learning sentence embeddings by combining several training signals: skip-thought, predicting translation, classifying entailment relationships, and predicting the constituent parse." ] }, { "id": "task668-341836ffe72845c7b0c84e7b5721a8b9", "input": "We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in \"one shot\". The features may be both real-valued and categorical. Training of the model is performed by stochastic variational Bayes. The experimental evaluation on synthetic data, as well as feature imputation and image inpainting problems, shows the effectiveness of the proposed approach and diversity of the generated samples.", "output": [ "We propose an extension of conditional variational autoencoder that allows conditioning on an arbitrary subset of the features and sampling the remaining ones." ] }, { "id": "task668-2538faaf8d6e4799abca2c57b4b53d9b", "input": "Recurrent neural networks (RNNs) are difficult to train on sequence processing tasks, not only because input noise may be amplified through feedback, but also because any inaccuracy in the weights has similar consequences as input noise. We describe a method for denoising the hidden state during training to achieve more robust representations thereby improving generalization performance. Attractor dynamics are incorporated into the hidden state to `clean up' representations at each step of a sequence. The attractor dynamics are trained through an auxillary denoising loss to recover previously experienced hidden states from noisy versions of those states. This state-denoised recurrent neural network (SDRNN) performs multiple steps of internal processing for each external sequence step. On a range of tasks, we show that the SDRNN outperforms a generic RNN as well as a variant of the SDRNN with attractor dynamics on the hidden state but without the auxillary loss. We argue that attractor dynamics---and corresponding connectivity constraints---are an essential component of the deep learning arsenal and should be invoked not only for recurrent networks but also for improving deep feedforward nets and intertask transfer.", "output": [ "We propose a mechanism for denoising the internal state of an RNN to improve generalization performance." ] }, { "id": "task668-13f595eccc2740a0b424b303620c4a42", "input": "The weak contraction mapping is a self mapping that the range is always a subset of the domain, which admits a unique fixed-point. The iteration of weak contraction mapping is a Cauchy sequence that yields the unique fixed-point. A gradient-free optimization method as an application of weak contraction mapping is proposed to achieve global minimum convergence. The optimization method is robust to local minima and initial point position.", "output": [ "A gradient-free method is proposed for non-convex optimization problem " ] }, { "id": "task668-c4a54503fe9a49b9953df3064ecb3f7a", "input": "We introduce an analytic distance function for moderately sized point sets of known cardinality that is shown to have very desirable properties, both as a loss function as well as a regularizer for machine learning applications. We compare our novel construction to other point set distance functions and show proof of concept experiments for training neural networks end-to-end on point set prediction tasks such as object detection.", "output": [ "Permutation-invariant loss function for point set prediction.", "Proposes a new loss for points registration (aligning two point sets) with preferable permutation invariant property. ", "This paper introduces a novel distance function between point sets, applies two other permutation distances in an end-to-end object detection task, and shows that in two dimensions all local minima of the holographic loss are global minima.", "Proposes permutation invariant loss functions which depend on the distance of sets." ] }, { "id": "task668-6095dfd5f26f4585ad59501724124601", "input": "Generative Adversarial Networks (GANs) have proven to be a powerful framework for learning to draw samples from complex distributions. However, GANs are also notoriously difficult to train, with mode collapse and oscillations a common problem. We hypothesize that this is at least in part due to the evolution of the generator distribution and the catastrophic forgetting tendency of neural networks, which leads to the discriminator losing the ability to remember synthesized samples from previous instantiations of the generator. Recognizing this, our contributions are twofold. First, we show that GAN training makes for a more interesting and realistic benchmark for continual learning methods evaluation than some of the more canonical datasets. Second, we propose leveraging continual learning techniques to augment the discriminator, preserving its ability to recognize previous generator samples. We show that the resulting methods add only a light amount of computation, involve minimal changes to the model, and result in better overall performance on the examined image and text generation tasks.", "output": [ "Generative Adversarial Network Training is a Continual Learning Problem." ] }, { "id": "task668-80f02c3891b746b8b09df5f9f02cf3e1", "input": "Backdoor attacks aim to manipulate a subset of training data by injecting adversarial triggers such that machine learning models trained on the tampered dataset will make arbitrarily (targeted) incorrect prediction on the testset with the same trigger embedded. While federated learning (FL) is capable of aggregating information provided by different parties for training a better model, its distributed learning methodology and inherently heterogeneous data distribution across parties may bring new vulnerabilities. In addition to recent centralized backdoor attacks on FL where each party embeds the same global trigger during training, we propose the distributed backdoor attack (DBA) --- a novel threat assessment framework developed by fully exploiting the distributed nature of FL. DBA decomposes a global trigger pattern into separate local patterns and embed them into the training set of different adversarial parties respectively. Compared to standard centralized backdoors, we show that DBA is substantially more persistent and stealthy against FL on diverse datasets such as finance and image data. We conduct extensive experiments to show that the attack success rate of DBA is significantly higher than centralized backdoors under different settings. Moreover, we find that distributed attacks are indeed more insidious, as DBA can evade two state-of-the-art robust FL algorithms against centralized backdoors. We also provide explanations for the effectiveness of DBA via feature visual interpretation and feature importance ranking.\n To further explore the properties of DBA, we test the attack performance by varying different trigger factors, including local trigger variations (size, gap, and location), scaling factor in FL, data distribution, and poison ratio and interval. Our proposed DBA and thorough evaluation results shed lights on characterizing the robustness of FL.", "output": [ "We proposed a novel distributed backdoor attack on federated learning and show that it is not only more effective compared with standard centralized attacks, but also harder to be defended by existing robust FL methods" ] }, { "id": "task668-2fcdec6b697d437a88c9d13c82ca8e56", "input": "We propose a fast second-order method that can be used as a drop-in replacement for current deep learning solvers. Compared to stochastic gradient descent (SGD), it only requires two additional forward-mode automatic differentiation operations per iteration, which has a computational cost comparable to two standard forward passes and is easy to implement. Our method addresses long-standing issues with current second-order solvers, which invert an approximate Hessian matrix every iteration exactly or by conjugate-gradient methods, procedures that are much slower than a SGD step. Instead, we propose to keep a single estimate of the gradient projected by the inverse Hessian matrix, and update it once per iteration with just two passes over the network. This estimate has the same size and is similar to the momentum variable that is commonly used in SGD. No estimate of the Hessian is maintained.\n We first validate our method, called CurveBall, on small problems with known solutions (noisy Rosenbrock function and degenerate 2-layer linear networks), where current deep learning solvers struggle. We then train several large models on CIFAR and ImageNet, including ResNet and VGG-f networks, where we demonstrate faster convergence with no hyperparameter tuning. We also show our optimiser's generality by testing on a large set of randomly-generated architectures.", "output": [ "A fast second-order solver for deep learning that works on ImageNet-scale problems with no hyper-parameter tuning", "Choosing direction by using a single step of gradient descent \"towards Newton step\" from an original estimate, and then taking this direction instead of original gradient", "A new approximate second-order optimization method with low computational cost that replaces the computation of the Hessian matrix with a single gradient step and a warm start strategy." ] }, { "id": "task668-f6bcadc857d64ab2b7f183925c1f16ed", "input": "Deep convolutional networks often append additive constant (\"bias\") terms to their convolution operations, enabling a richer repertoire of functional mappings. Biases are also used to facilitate training, by subtracting mean response over batches of training images (a component of \"batch normalization\"). Recent state-of-the-art blind denoising methods seem to require these terms for their success. Here, however, we show that bias terms used in most CNNs (additive constants, including those used for batch normalization) interfere with the interpretability of these networks, do not help performance, and in fact prevent generalization of performance to noise levels not including in the training data. In particular, bias-free CNNs (BF-CNNs) are locally linear, and hence amenable to direct analysis with linear-algebraic tools. These analyses provide interpretations of network functionality in terms of projection onto a union of low-dimensional subspaces, connecting the learning-based method to more traditional denoising methodology. Additionally, BF-CNNs generalize robustly, achieving near-state-of-the-art performance at noise levels well beyond the range over which they have been trained.", "output": [ "We show that removing constant terms from CNN architectures provides interpretability of the denoising method via linear-algebra techniques and also boosts generalization performance across noise levels." ] }, { "id": "task668-e093c2392d20487ba64000ffc89c541c", "input": "Deep learning algorithms are increasingly used in modeling chemical processes. However, black box predictions without rationales have limited used in practical applications, such as drug design. To this end, we learn to identify molecular substructures -- rationales -- that are associated with the target chemical property (e.g., toxicity). The rationales are learned in an unsupervised fashion, requiring no additional information beyond the end-to-end task. We formulate this problem as a reinforcement learning problem over the molecular graph, parametrized by two convolution networks corresponding to the rationale selection and prediction based on it, where the latter induces the reward function. We evaluate the approach on two benchmark toxicity datasets. We demonstrate that our model sustains high performance under the additional constraint that predictions strictly follow the rationales. Additionally, we validate the extracted rationales through comparison against those described in chemical literature and through synthetic experiments.", "output": [ "We use a reinforcement learning over molecular graphs to generate rationales for interpretable molecular property prediction." ] }, { "id": "task668-0f4526a4b14a46acb5be4e194dbc721b", "input": "In this paper, we study the adversarial attack and defence problem in deep learning from the perspective of Fourier analysis. We first explicitly compute the Fourier transform of deep ReLU neural networks and show that there exist decaying but non-zero high frequency components in the Fourier spectrum of neural networks. We then demonstrate that the vulnerability of neural networks towards adversarial samples can be attributed to these insignificant but non-zero high frequency components. Based on this analysis, we propose to use a simple post-averaging technique to smooth out these high frequency components to improve the robustness of neural networks against adversarial attacks. Experimental results on the ImageNet and the CIFAR-10 datasets have shown that our proposed method is universally effective to defend many existing adversarial attacking methods proposed in the literature, including FGSM, PGD, DeepFool and C&W attacks. Our post-averaging method is simple since it does not require any re-training, and meanwhile it can successfully defend over 80-96% of the adversarial samples generated by these methods without introducing significant performance degradation (less than 2%) on the original clean images.", "output": [ "An insight into the reason of adversarial vulnerability, an effective defense method against adversarial attacks." ] }, { "id": "task668-d350efb5e5464ba2b3dc689912e86c7a", "input": "Sequential data often originates from diverse environments. Across them exist both shared regularities and environment specifics. To learn robust cross-environment descriptions of sequences we introduce disentangled state space models (DSSM). In the latent space of DSSM environment-invariant state dynamics is explicitly disentangled from environment-specific information governing that dynamics. We empirically show that such separation enables robust prediction, sequence manipulation and environment characterization. We also propose an unsupervised VAE-based training procedure to learn DSSM as Bayesian filters. In our experiments, we demonstrate state-of-the-art performance in controlled generation and prediction of bouncing ball video sequences across varying gravitational influences.", "output": [ "DISENTANGLED STATE SPACE MODELS", "The paper presents a generative state space model using a global latent variable E to capture environment-specific information." ] }, { "id": "task668-6548beee2b1243a7893805b88cf1aada", "input": "Most deep learning-based models for speech enhancement have mainly focused on estimating the magnitude of spectrogram while reusing the phase from noisy speech for reconstruction. This is due to the difficulty of estimating the phase of clean speech. To improve speech enhancement performance, we tackle the phase estimation problem in three ways. First, we propose Deep Complex U-Net, an advanced U-Net structured model incorporating well-defined complex-valued building blocks to deal with complex-valued spectrograms. Second, we propose a polar coordinate-wise complex-valued masking method to reflect the distribution of complex ideal ratio masks. Third, we define a novel loss function, weighted source-to-distortion ratio (wSDR) loss, which is designed to directly correlate with a quantitative evaluation measure. Our model was evaluated on a mixture of the Voice Bank corpus and DEMAND database, which has been widely used by many deep learning models for speech enhancement. Ablation experiments were conducted on the mixed dataset showing that all three proposed approaches are empirically valid. Experimental results show that the proposed method achieves state-of-the-art performance in all metrics, outperforming previous approaches by a large margin.", "output": [ "This paper proposes a novel complex masking method for speech enhancement along with a loss function for efficient phase estimation." ] }, { "id": "task668-14e9746a4d604a5dae4c89e2ed36b5ab", "input": "We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.", "output": [ "A new method for unsupervised representation learning on graphs, relying on maximizing mutual information between local and global representations in a graph. State-of-the-art results, competitive with supervised learning." ] }, { "id": "task668-6e0263c3c6e84c7689b25da2a46736d2", "input": "Imitation learning aims to inversely learn a policy from expert demonstrations, which has been extensively studied in the literature for both single-agent setting with Markov decision process (MDP) model, and multi-agent setting with Markov game (MG) model. However, existing approaches for general multi-agent Markov games are not applicable to multi-agent extensive Markov games, where agents make asynchronous decisions following a certain order, rather than simultaneous decisions. We propose a novel framework for asynchronous multi-agent generative adversarial imitation learning (AMAGAIL) under general extensive Markov game settings, and the learned expert policies are proven to guarantee subgame perfect equilibrium (SPE), a more general and stronger equilibrium than Nash equilibrium (NE). The experiment results demonstrate that compared to state-of-the-art baselines, our AMAGAIL model can better infer the policy of each expert agent using their demonstration data collected from asynchronous decision-making scenarios (i.e., extensive Markov games).", "output": [ "This paper extends the multi-agent generative adversarial imitation learning to extensive-form Markov games." ] }, { "id": "task668-fba8c1ea4dd64d79bfc5c0b8630cb9aa", "input": "We present Generative Adversarial Privacy and Fairness (GAPF), a data-driven framework for learning private and fair representations of the data. GAPF leverages recent advances in adversarial learning to allow a data holder to learn \"universal\" representations that decouple a set of sensitive attributes from the rest of the dataset. Under GAPF, finding the optimal decorrelation scheme is formulated as a constrained minimax game between a generative decorrelator and an adversary. We show that for appropriately chosen adversarial loss functions, GAPF provides privacy guarantees against strong information-theoretic adversaries and enforces demographic parity. We also evaluate the performance of GAPF on multi-dimensional Gaussian mixture models and real datasets, and show how a designer can certify that representations learned under an adversary with a fixed architecture perform well against more complex adversaries.", "output": [ "We present Generative Adversarial Privacy and Fairness (GAPF), a data-driven framework for learning private and fair representations with certified privacy/fairness guarantees", "This paper uses a GAN model to provide an overview of the related work to Private/Fair Representation Learning (PRL).", "This paper presents an adversarial-based approach for private and fair representations by learned distortion of data that minimises the dependency on sensitive variables while the degree of distortion is constrained.", "The authors describe a framework of how to learn a demographic parity representation that can be used to train certain classifiers." ] }, { "id": "task668-8068ed292ce1433d95652ee1ac410670", "input": "In the last few years, deep learning has been tremendously successful in many applications. However, our theoretical understanding of deep learning, and thus the ability of providing principled improvements, seems to lag behind. A theoretical puzzle concerns the ability of deep networks to predict well despite their intriguing apparent lack of generalization: their classification accuracy on the training set is not a proxy for their performance on a test set. How is it possible that training performance is independent of testing performance? Do indeed deep networks require a drastically new theory of generalization? Or are there measurements based on the training data that are predictive of the network performance on future data? Here we show that when performance is measured appropriately, the training performance is in fact predictive of expected performance, consistently with classical machine learning theory.", "output": [ "Contrary to previous beliefs, the training performance of deep networks, when measured appropriately, is predictive of test performance, consistent with classical machine learning theory." ] }, { "id": "task668-c7352bd92ffd44b294eb6e4ea7a44d4f", "input": "Learning long-term dependencies is a key long-standing challenge of recurrent neural networks (RNNs). Hierarchical recurrent neural networks (HRNNs) have been considered a promising approach as long-term dependencies are resolved through shortcuts up and down the hierarchy. Yet, the memory requirements of Truncated Backpropagation Through Time (TBPTT) still prevent training them on very long sequences. In this paper, we empirically show that in (deep) HRNNs, propagating gradients back from higher to lower levels can be replaced by locally computable losses, without harming the learning capability of the network, over a wide range of tasks. This decoupling by local losses reduces the memory requirements of training by a factor exponential in the depth of the hierarchy in comparison to standard TBPTT.", "output": [ "We replace some gradients paths in hierarchical RNN's by an auxiliary loss. We show that this can reduce the memory cost while preserving performance.", "The paper introduces a hierarchical RNN architecture that could be trained more memory efficiently.", "The proposed paper suggests to decouple the different layers of hierarchy in RNN using auxiliary losses." ] }, { "id": "task668-5bf451355e704cfb8523250aeeb17256", "input": "Progress in understanding how individual animals learn requires high-throughput standardized methods for behavioral training and ways of adapting training. During the course of training with hundreds or thousands of trials, an animal may change its underlying strategy abruptly, and capturing these changes requires real-time inference of the animal’s latent decision-making strategy. To address this challenge, we have developed an integrated platform for automated animal training, and an iterative decision-inference model that is able to infer the momentary decision-making policy, and predict the animal’s choice on each trial with an accuracy of ~80\\%, even when the animal is performing poorly. We also combined decision predictions at single-trial resolution with automated pose estimation to assess movement trajectories. Analysis of these features revealed categories of movement trajectories that associate with decision confidence.", "output": [ "Automated mice training for neuroscience with online iterative latent strategy inference for behavior prediction" ] }, { "id": "task668-8bc0bb1e44044c5598f56acb1438aaea", "input": "Recent efforts in Dialogue State Tracking (DST) for task-oriented dialogues have progressed toward open-vocabulary or generation-based approaches where the models can generate slot value candidates from the dialogue history itself. These approaches have shown good performance gain, especially in complicated dialogue domains with dynamic slot values. However, they fall short in two aspects: (1) they do not allow models to explicitly learn signals across domains and slots to detect potential dependencies among \\textit{(domain, slot)} pairs; and (2) existing models follow auto-regressive approaches which incur high time cost when the dialogue evolves over multiple domains and multiple turns. In this paper, we propose a novel framework of Non-Autoregressive Dialog State Tracking (NADST) which can factor in potential dependencies among domains and slots to optimize the models towards better prediction of dialogue states as a complete set rather than separate slots. In particular, the non-autoregressive nature of our method not only enables decoding in parallel to significantly reduce the latency of DST for real-time dialogue response generation, but also detect dependencies among slots at token level in addition to slot and domain level. Our empirical results show that our model achieves the state-of-the-art joint accuracy across all domains on the MultiWOZ 2.1 corpus, and the latency of our model is an order of magnitude lower than the previous state of the art as the dialogue history extends over time.", "output": [ "We propose the first non-autoregressive neural model for Dialogue State Tracking (DST), achieving the SOTA accuracy (49.04%) on MultiWOZ2.1 benchmark, and reducing inference latency by an order of magnitude.", "A new model for the DST task that reduces inference time complexity with a non-autoregressive decoder, obtains competitive DST accuracy, and shows improvements over other baselines.", "Proposal for a model that is capable of tracking dialogue states in a non-recursive fashion." ] }, { "id": "task668-981b395254c9414fafd840bf608287bf", "input": "In this paper, we ask for the main factors that determine a classifier's decision making and uncover such factors by studying latent codes produced by auto-encoding frameworks. To deliver an explanation of a classifier's behaviour, we propose a method that provides series of examples highlighting semantic differences between the classifier's decisions. We generate these examples through interpolations in latent space. We introduce and formalize the notion of a semantic stochastic path, as a suitable stochastic process defined in feature space via latent code interpolations. We then introduce the concept of semantic Lagrangians as a way to incorporate the desired classifier's behaviour and find that the solution of the associated variational problem allows for highlighting differences in the classifier decision.\n Very importantly, within our framework the classifier is used as a black-box, and only its evaluation is required.", "output": [ "We generate examples to explain a classifier desicion via interpolations in latent space. The variational auto encoder cost is extended with a functional of the classifier over the generated example path in data space." ] }, { "id": "task668-34812f05d5ba4667b7a43557a88de05f", "input": "Weight-sharing plays a significant role in the success of many deep neural networks, by increasing memory efficiency and incorporating useful inductive priors about the problem into the network. But understanding how weight-sharing can be used effectively in general is a topic that has not been studied extensively. Chen et al. (2015) proposed HashedNets, which augments a multi-layer perceptron with a hash table, as a method for neural network compression. We generalize this method into a framework (ArbNets) that allows for efficient arbitrary weight-sharing, and use it to study the role of weight-sharing in neural networks. We show that common neural networks can be expressed as ArbNets with different hash functions. We also present two novel hash functions, the Dirichlet hash and the Neighborhood hash, and use them to demonstrate experimentally that balanced and deterministic weight-sharing helps with the performance of a neural network.", "output": [ "Studied the role of weight sharing in neural networks using hash functions, found that a balanced and deterministic hash function helps network performance.", "Proposing ArbNets to study weight sharing in a more systematic way by defining the weight sharing function as a hash function." ] }, { "id": "task668-f2540a7960a246dea5ab7fec0058f0cf", "input": "Complex-value neural networks are not a new concept, however, the use of real-values has often been favoured over complex-values due to difficulties in training and accuracy of results. Existing literature ignores the number of parameters used. We compared complex- and real-valued neural networks using five activation functions. We found that when real and complex neural networks are compared using simple classification tasks, complex neural networks perform equal to or slightly worse than real-value neural networks. However, when specialised architecture is used, complex-valued neural networks outperform real-valued neural networks. Therefore, complex–valued neural networks should be used when the input data is also complex or it can be meaningfully to the complex plane, or when the network architecture uses the structure defined by using complex numbers.", "output": [ "Comparison of complex- and real-valued multi-layer perceptron with respect to the number of real-valued parameters." ] }, { "id": "task668-bbc86642289146e685807a7b908cbb87", "input": "Many computer vision applications require solving multiple tasks in real-time. A neural network can be trained to solve multiple tasks simultaneously using 'multi-task learning'. This saves computation at inference time as only a single network needs to be evaluated. Unfortunately, this often leads to inferior overall performance as task objectives compete, which consequently poses the question: which tasks should and should not be learned together in one network when employing multi-task learning? We systematically study task cooperation and competition and propose a framework for assigning tasks to a few neural networks such that cooperating tasks are computed by the same neural network, while competing tasks are computed by different networks. Our framework offers a time-accuracy trade-off and can produce better accuracy using less inference time than not only a single large multi-task neural network but also many single-task networks.\n", "output": [ "We analyze what tasks are best learned together in one network, and which are best to learn separately. " ] }, { "id": "task668-7e58620bbe5c415a907d8a6cebadf783", "input": "Long-term video prediction is highly challenging since it entails simultaneously capturing spatial and temporal information across a long range of image frames.Standard recurrent models are ineffective since they are prone to error propagation and cannot effectively capture higher-order correlations. A potential solution is to extend to higher-order spatio-temporal recurrent models. However, such a model requires a large number of parameters and operations, making it intractable to learn in practice and is prone to overfitting. In this work, we propose convolutional tensor-train LSTM (Conv-TT-LSTM), which learns higher-orderConvolutional LSTM (ConvLSTM) efficiently using convolutional tensor-train decomposition (CTTD). Our proposed model naturally incorporates higher-order spatio-temporal information at a small cost of memory and computation by using efficient low-rank tensor representations. We evaluate our model on Moving-MNIST and KTH datasets and show improvements over standard ConvLSTM and better/comparable results to other ConvLSTM-based approaches, but with much fewer parameters.", "output": [ "we propose convolutional tensor-train LSTM, which learns higher-order Convolutional LSTM efficiently using convolutional tensor-train decomposition. " ] }, { "id": "task668-7a9f5e656b2542ec917cf0891d41bf25", "input": "Convolutional neural networks (CNNs) in recent years have made a dramatic impact in science, technology and industry, yet the theoretical mechanism of CNN architecture design remains surprisingly vague. The CNN neurons, including its distinctive element, convolutional filters, are known to be learnable features, yet their individual role in producing the output is rather unclear. The thesis of this work is that not all neurons are equally important and some of them contain more useful information to perform a given task. Hence, we propose to quantify and rank neuron importance, and directly incorporate neuron importance in the objective function under two formulations: (1) a game theoretical approach based on Shapley value which computes the marginal contribution of each filter; and (2) a probabilistic approach based on what-we-call, the importance switch using variational inference. Using these two methods we confirm the general theory that some of the neurons are inherently more important than the others. Various experiments illustrate that learned ranks can be readily useable for structured network compression and interpretability of learned features.", "output": [ "We propose CNN neuron ranking with two different methods and show their consistency in producing the result which allows to interpret what network deems important and compress the network by keeping the most relevant nodes." ] }, { "id": "task668-afb9bf602af34eb5b6a529e98d1255f0", "input": "Imitation Learning (IL) is an appealing approach to learn desirable autonomous behavior. However, directing IL to achieve arbitrary goals is difficult. In contrast, planning-based algorithms use dynamics models and reward functions to achieve goals. Yet, reward functions that evoke desirable behavior are often difficult to specify. In this paper, we propose \"Imitative Models\" to combine the benefits of IL and goal-directed planning. Imitative Models are probabilistic predictive models of desirable behavior able to plan interpretable expert-like trajectories to achieve specified goals. We derive families of flexible goal objectives, including constrained goal regions, unconstrained goal sets, and energy-based goals. We show that our method can use these objectives to successfully direct behavior. Our method substantially outperforms six IL approaches and a planning-based approach in a dynamic simulated autonomous driving task, and is efficiently learned from expert demonstrations without online data collection. We also show our approach is robust to poorly-specified goals, such as goals on the wrong side of the road.", "output": [ "In this paper, we propose Imitative Models to combine the benefits of IL and goal-directed planning: probabilistic predictive models of desirable behavior able to plan interpretable expert-like trajectories to achieve specified goals." ] }, { "id": "task668-1a81957949b34a44a03011291a8519a8", "input": "Variational autoencoders (VAEs) have been successful at learning a low-dimensional manifold from high-dimensional data with complex dependencies. At their core, they consist of a powerful Bayesian probabilistic inference model, to capture the salient features of the data. In training, they exploit the power of variational inference, by optimizing a lower bound on the model evidence. The latent representation and the performance of VAEs are heavily influenced by the type of bound used as a cost function. Significant research work has been carried out into the development of tighter bounds than the original ELBO, to more accurately approximate the true log-likelihood. By leveraging the q-deformed logarithm in the traditional lower bounds, ELBO and IWAE, and the upper bound CUBO, we bring contributions to this direction of research. In this proof-of-concept study, we explore different ways of creating these q-deformed bounds that are tighter than the classical ones and we show improvements in the performance of such VAEs on the binarized MNIST dataset.\n", "output": [ "Using the q-deformed logarithm, we derive tighter bounds than IWAE, to train variational autoencoders." ] }, { "id": "task668-64620c6f278441ce9438dfde9456c4d9", "input": "Alternatives to recurrent neural networks, in particular, architectures based on attention or convolutions, have been gaining momentum for processing input sequences. In spite of their relevance, the computational properties of these alternatives have not yet been fully explored. We study the computational power of two of the most paradigmatic architectures exemplifying these mechanisms: the Transformer (Vaswani et al., 2017) and the Neural GPU (Kaiser & Sutskever, 2016). We show both models to be Turing complete exclusively based on their capacity to compute and access internal dense representations of the data. In particular, neither the Transformer nor the Neural GPU requires access to an external memory to become Turing complete. Our study also reveals some minimal sets of elements needed to obtain these completeness results.", "output": [ "We show that the Transformer architecture and the Neural GPU are Turing complete." ] }, { "id": "task668-a574e182948045e3a9a863c9ae6b7aca", "input": "LSTM-based language models exhibit compositionality in their representations, but how this behavior emerges over the course of training has not been explored. Analyzing synthetic data experiments with contextual decomposition, we find that LSTMs learn long-range dependencies compositionally by building them from shorter constituents during training.", "output": [ "LSTMs learn long-range dependencies compositionally by building them from shorter constituents over the course of training." ] }, { "id": "task668-b6c9170c127347c0907fc3df17386fbe", "input": "Learning communication via deep reinforcement learning has recently been shown to be an effective way to solve cooperative multi-agent tasks. However, learning which communicated information is beneficial for each agent's decision-making remains a challenging task. In order to address this problem, we introduce a fully differentiable framework for communication and reasoning, enabling agents to solve cooperative tasks in partially-observable environments. The framework is designed to facilitate explicit reasoning between agents, through a novel memory-based attention network that can learn selectively from its past memories. The model communicates through a series of reasoning steps that decompose each agent's intentions into learned representations that are used first to compute the relevance of communicated information, and second to extract information from memories given newly received information. By selectively interacting with new information, the model effectively learns a communication protocol directly, in an end-to-end manner. We empirically demonstrate the strength of our model in cooperative multi-agent tasks, where inter-agent communication and reasoning over prior information substantially improves performance compared to baselines.", "output": [ "Novel architecture of memory based attention mechanism for multi-agent communication." ] }, { "id": "task668-a9cb792ee3a04f678c693a493db3b2db", "input": "Domain transfer is a exciting and challenging branch of machine learning because models must learn to smoothly transfer between domains, preserving local variations and capturing many aspects of variation without labels. \n However, most successful applications to date require the two domains to be closely related (ex. image-to-image, video-video), \n utilizing similar or shared networks to transform domain specific properties like texture, coloring, and line shapes. \n Here, we demonstrate that it is possible to transfer across modalities (ex. image-to-audio) by first abstracting the data with latent generative models and then learning transformations between latent spaces. \n We find that a simple variational autoencoder is able to learn a shared latent space to bridge between two generative models in an unsupervised fashion, and even between different types of models (ex. variational autoencoder and a generative adversarial network). \n We can further impose desired semantic alignment of attributes with a linear classifier in the shared latent space. \n The proposed variation autoencoder enables preserving both locality and semantic alignment through the transfer process, as shown in the qualitative and quantitative evaluations.\n Finally, the hierarchical structure decouples the cost of training the base generative models and semantic alignments, enabling computationally efficient and data efficient retraining of personalized mapping functions.", "output": [ "Conditional VAE on top of latent spaces of pre-trained generative models that enables transfer between drastically different domains while preserving locality and semantic alignment." ] }, { "id": "task668-d46c0045eac041b9bce11d538bfed90e", "input": "Learning multimodal representations is a fundamentally complex research problem due to the presence of multiple heterogeneous sources of information. Although the presence of multiple modalities provides additional valuable information, there are two key challenges to address when learning from multimodal data: 1) models must learn the complex intra-modal and cross-modal interactions for prediction and 2) models must be robust to unexpected missing or noisy modalities during testing. In this paper, we propose to optimize for a joint generative-discriminative objective across multimodal data and labels. We introduce a model that factorizes representations into two sets of independent factors: multimodal discriminative and modality-specific generative factors. Multimodal discriminative factors are shared across all modalities and contain joint multimodal features required for discriminative tasks such as sentiment prediction. Modality-specific generative factors are unique for each modality and contain the information required for generating data. Experimental results show that our model is able to learn meaningful multimodal representations that achieve state-of-the-art or competitive performance on six multimodal datasets. Our model demonstrates flexible generative capabilities by conditioning on independent factors and can reconstruct missing modalities without significantly impacting performance. Lastly, we interpret our factorized representations to understand the interactions that influence multimodal learning.", "output": [ "We propose a model to learn factorized multimodal representations that are discriminative, generative, and interpretable.", "This paper presents 'Multimodal Factorization model' that factorizes representations into shared multimodal discriminative factors and modality specific generative factors. " ] }, { "id": "task668-2139212d7761480da20f17c9bc1aef52", "input": "Asking questions is an important ability for a chatbot. This paper focuses on question generation. Although there are existing works on question generation based on a piece of descriptive text, it remains to be a very challenging problem. In the paper, we propose a new question generation problem, which also requires the input of a target topic in addition to a piece of descriptive text. The key reason for proposing the new problem is that in practical applications, we found that useful questions need to be targeted toward some relevant topics. One almost never asks a random question in a conversation. Due to the fact that given a descriptive text, it is often possible to ask many types of questions, generating a question without knowing what it is about is of limited use. To solve the problem, we propose a novel neural network that is able to generate topic-specific questions. One major advantage of this model is that it can be trained directly using a question-answering corpus without requiring any additional annotations like annotating topics in the questions or answers. Experimental results show that our model outperforms the state-of-the-art baseline.", "output": [ "We propose a neural network that is able to generate topic-specific questions.", "Presents a neural network-based approach to generate topic-specific questions with the motivation that topical questions are more meaningful in practical applications.", "Proposes a topic-based generation method using an LSTM to extract topics using a two-stage encoding technique" ] }, { "id": "task668-341b1b426fac4c2b92eac6ce713c363c", "input": "Understanding the behavior of stochastic gradient descent (SGD) in the context of deep neural networks has raised lots of concerns recently. Along this line, we theoretically study a general form of gradient based optimization dynamics with unbiased noise, which unifies SGD and standard Langevin dynamics. Through investigating this general optimization dynamics, we analyze the behavior of SGD on escaping from minima and its regularization effects. A novel indicator is derived to characterize the efficiency of escaping from minima through measuring the alignment of noise covariance and the curvature of loss function. Based on this indicator, two conditions are established to show which type of noise structure is superior to isotropic noise in term of escaping efficiency. We further show that the anisotropic noise in SGD satisfies the two conditions, and thus helps to escape from sharp and poor minima effectively, towards more stable and flat minima that typically generalize well. We verify our understanding through comparing\n this anisotropic diffusion with full gradient descent plus isotropic diffusion (i.e. Langevin dynamics) and other types of position-dependent noise.", "output": [ "We provide theoretical and empirical analysis on the role of anisotropic noise introduced by stochastic gradient on escaping from minima." ] }, { "id": "task668-3d92e276e54e4c1da17bf04254eb7797", "input": "Separating mixed distributions is a long standing challenge for machine learning and signal processing. Applications include: single-channel multi-speaker separation (cocktail party problem), singing voice separation and separating reflections from images. Most current methods either rely on making strong assumptions on the source distributions (e.g. sparsity, low rank, repetitiveness) or rely on having training samples of each source in the mixture. In this work, we tackle the scenario of extracting an unobserved distribution additively mixed with a signal from an observed (arbitrary) distribution. We introduce a new method: Neural Egg Separation - an iterative method that learns to separate the known distribution from progressively finer estimates of the unknown distribution. In some settings, Neural Egg Separation is initialization sensitive, we therefore introduce GLO Masking which ensures a good initialization. Extensive experiments show that our method outperforms current methods that use the same level of supervision and often achieves similar performance to full supervision.", "output": [ "An iterative neural method for extracting signals that are only observed mixed with other signals" ] }, { "id": "task668-b8087ea9dc274ebb8796789898e9ebc2", "input": "Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e.g., following natural language instructions or dialog. However, existing methods tend to overfit training data in seen environments and fail to generalize well in previously unseen environments. In order to close the gap between seen and unseen environments, we aim at learning a generalizable navigation model from two novel perspectives:\n (1) we introduce a multitask navigation model that can be seamlessly trained on both Vision-Language Navigation (VLN) and Navigation from Dialog History (NDH) tasks, which benefits from richer natural language guidance and effectively transfers knowledge across tasks;\n (2) we propose to learn environment-agnostic representations for navigation policy that are invariant among environments, thus generalizing better on unseen environments.\n Extensive experiments show that our environment-agnostic multitask navigation model significantly reduces the performance gap between seen and unseen environments and outperforms the baselines on unseen environments by 16% (relative measure on success rate) on VLN and 120% (goal progress) on NDH, establishing the new state of the art for NDH task.", "output": [ "We propose to learn a more generalized policy for natural language grounded navigation tasks via environment-agnostic multitask learning." ] }, { "id": "task668-43b0e1f8d4cc4919a421f72e1493f6a5", "input": "Attention mechanisms have advanced the state of the art in several machine learning tasks. Despite significant empirical gains, there is a lack of theoretical analyses on understanding their effectiveness. In this paper, we address this problem by studying the landscape of population and empirical loss functions of attention-based neural networks. Our results show that, under mild assumptions, every local minimum of a two-layer global attention model has low prediction error, and attention models require lower sample complexity than models not employing attention. We then extend our analyses to the popular self-attention model, proving that they deliver consistent predictions with a more expressive class of functions. Additionally, our theoretical results provide several guidelines for designing attention mechanisms. Our findings are validated with satisfactory experimental results on MNIST and IMDB reviews dataset.", "output": [ "We analyze the loss landscape of neural networks with attention and explain why attention is helpful in training neural networks to achieve good performance.", "This paper proves from the theoretical perspective that attention networks can generalize better than non-attention baselines for fixed-attention (single-layer and multi-layer) and self-attention in the single layer setting." ] }, { "id": "task668-a815c5e1c11547328230b17d2a450ef4", "input": "We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters. The algorithm --- an iterative application of compressed sensing techniques for orthogonal polynomials --- requires only uniform sampling of the hyperparameters and is thus easily parallelizable.\n \n Experiments for training deep neural networks on Cifar-10 show that compared to state-of-the-art tools (e.g., Hyperband and Spearmint), our algorithm finds significantly improved solutions, in some cases better than what is attainable by hand-tuning. In terms of overall running time (i.e., time required to sample various settings of hyperparameters plus additional computation time), we are at least an order of magnitude faster than Hyperband and Bayesian Optimization. We also outperform Random Search $8\\times$.\n \nOur method is inspired by provably-efficient algorithms for learning decision trees using the discrete Fourier transform. We obtain improved sample-complexty bounds for learning decision trees while matching state-of-the-art bounds on running time (polynomial and quasipolynomial, respectively).", "output": [ "A hyperparameter tuning algorithm using discrete Fourier analysis and compressed sensing", "Investigates problem of optimizing hyperparameters under the assumption that the unknown function can be approximated, showing that the approximate minimization can be performed over the boolean hypercube.", "The paper explores hyperparameter optimization by assuming structure in the unknown function mapping hyperparameters to classification accuracy" ] }, { "id": "task668-ebec7ac3b1da4abe8a1c4d48b286320c", "input": "We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of computationally intensive matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training).", "output": [ "A novel approach to processing graph-structured data by neural networks, leveraging attention over a node's neighborhood. Achieves state-of-the-art results on transductive citation network tasks and an inductive protein-protein interaction task.", "This paper proposes a new method for classifying nodes of a graph, which can be used in semi-supervised scenarios and on a completely new graph. ", "The paper introduces a neural network architecture to operate on graph-structured data named Graph Attention Networks.", "Provides a fair and almost comprehensive discussion of the state of art approaches to learning vector representations for the nodes of a graph." ] }, { "id": "task668-a16b6d2f94ff41b393bba74117085251", "input": "We present a data driven approach to construct a library of feedback motion primitives for non-holonomic vehicles that guarantees bounded error in following arbitrarily long trajectories. This ensures that motion re-planning can be avoided as long as disturbances to the vehicle remain within a certain bound and also potentially when the obstacles are displaced within a certain bound. The library is constructed along local abstractions of the dynamics that enables addition of new motion primitives through abstraction refinement. We provide sufficient conditions for construction of such robust motion primitives for a large class of nonlinear dynamics, including commonly used models, such as the standard Reeds-Shepp model. The algorithm is applied for motion planning and control of a rover with slipping without its prior modelling.", "output": [ "We show that under some assumptions on vehicle dynamics and environment uncertainty it is possible to automatically synthesize motion primitives that do not accumulate error over time." ] }, { "id": "task668-18db21688cc2455b9b7cf3d2bbba405a", "input": "Deep models are state-of-the-art for many computer vision tasks including image classification and object detection. However, it has been shown that deep models are vulnerable to adversarial examples. We highlight how one-hot encoding directly contributes to this vulnerability and propose breaking away from this widely-used, but highly-vulnerable mapping. We demonstrate that by leveraging a different output encoding, multi-way encoding, we can make models more robust. Our approach makes it more difficult for adversaries to find useful gradients for generating adversarial attacks. We present state-of-the-art robustness results for black-box, white-box attacks, and achieve higher clean accuracy on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN when combined with adversarial training. The strength of our approach is also presented in the form of an attack for model watermarking, raising challenges in detecting stolen models.", "output": [ "We demonstrate that by leveraging a multi-way output encoding, rather than the widely used one-hot encoding, we can make deep models more robust to adversarial attacks.", "This paper proposes replacing the final cross-entropy layer trained on one-hot labels in classifiers by encoding each label as a high-dimensional vector and training the classifier to minimize L2 distance from the encoding of the correct class.", "Authors propose new method against adversarial attacks that shows significant amount of gains compared to baselines" ] }, { "id": "task668-3c4b8088dec548ec9c5adb84038acdf6", "input": "In recent years, softmax together with its fast approximations has become the de-facto loss function for deep neural networks with multiclass predictions. However, softmax is used in many problems that do not fully fit the multiclass framework and where the softmax assumption of mutually exclusive outcomes can lead to biased results. This is often the case for applications such as language modeling, next event prediction and matrix factorization, where many of the potential outcomes are not mutually exclusive, but are more likely to be independent conditionally on the state. To this end, for the set of problems with positive and unlabeled data, we propose a relaxation of the original softmax formulation, where, given the observed state, each of the outcomes are conditionally independent but share a common set of negatives. Since we operate in a regime where explicit negatives are missing, we create an adversarially-trained model of negatives and derive a new negative sampling and weighting scheme which we denote as Cooperative Importance Sampling (CIS). We show empirically the advantages of our newly introduced negative sampling scheme by pluging it in the Word2Vec algorithm and benching it extensively against other negative sampling schemes on both language modeling and matrix factorization tasks and show large lifts in performance.", "output": [ "Defining a partially mutual exclusive softmax loss for postive data and implementing a cooperative based sampling scheme", "This paper presents Cooperative Importance Sampling towards resolving the problem of the mutually exclusive assumption of traditional softmax being biased when negative samples are not explicitly defined", "This paper proposes PMES methods to relax the exclusive outcome assumption in softmax loss, demonstrating empirical merit in improving word2vec type of embedding models." ] }, { "id": "task668-73762f48eb7941dfafa61070cab7fd64", "input": "Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods. At the same time, there is a vast amount of existing functions that programmatically solve different tasks in a precise manner eliminating the need for training. In many cases, it is possible to decompose a task to a series of functions, of which for some we may prefer to use a neural network to learn the functionality, while for others the preferred method would be to use existing black-box functions. We propose a method for end-to-end training of a base neural network that integrates calls to existing black-box functions. We do so by approximating the black-box functionality with a differentiable neural network in a way that drives the base network to comply with the black-box function interface during the end-to-end optimization process. At inference time, we replace the differentiable estimator with its external black-box non-differentiable counterpart such that the base network output matches the input arguments of the black-box function. Using this ``Estimate and Replace'' paradigm, we train a neural network, end to end, to compute the input to black-box functionality while eliminating the need for intermediate labels. We show that by leveraging the existing precise black-box function during inference, the integrated model generalizes better than a fully differentiable model, and learns more efficiently compared to RL-based methods.", "output": [ "Training DNNs to interface w\\ black box functions w\\o intermediate labels by using an estimator sub-network that can be replaced with the black box after training" ] }, { "id": "task668-c034283d7f094fcba252ac0315243e14", "input": "Similar to humans and animals, deep artificial neural networks exhibit critical periods during which a temporary stimulus deficit can impair the development of a skill. The extent of the impairment depends on the onset and length of the deficit window, as in animal models, and on the size of the neural network. Deficits that do not affect low-level statistics, such as vertical flipping of the images, have no lasting effect on performance and can be overcome with further training. To better understand this phenomenon, we use the Fisher Information of the weights to measure the effective connectivity between layers of a network during training. Counterintuitively, information rises rapidly in the early phases of training, and then decreases, preventing redistribution of information resources in a phenomenon we refer to as a loss of \"Information Plasticity\". Our analysis suggests that the first few epochs are critical for the creation of strong connections that are optimal relative to the input data distribution. Once such strong connections are created, they do not appear to change during additional training. These findings suggest that the initial learning transient, under-scrutinized compared to asymptotic behavior, plays a key role in determining the outcome of the training process. Our findings, combined with recent theoretical results in the literature, also suggest that forgetting (decrease of information in the weights) is critical to achieving invariance and disentanglement in representation learning. Finally, critical periods are not restricted to biological systems, but can emerge naturally in learning systems, whether biological or artificial, due to fundamental constrains arising from learning dynamics and information processing.", "output": [ "Sensory deficits in early training phases can lead to irreversible performance loss in both artificial and neuronal networks, suggesting information phenomena as the common cause, and point to the importance of the initial transient and forgetting." ] }, { "id": "task668-426fad6a243841a4b700949053cfedcf", "input": "Deep neural networks are known to be vulnerable to adversarial perturbations. In this paper, we bridge adversarial robustness of neural nets with Lyapunov stability of dynamical systems. From this viewpoint, training neural nets is equivalent to finding an optimal control of the discrete dynamical system, which allows one to utilize methods of successive approximations, an optimal control algorithm based on Pontryagin's maximum principle, to train neural nets. This decoupled training method allows us to add constraints to the optimization, which makes the deep model more robust. The constrained optimization problem can be formulated as a semi-definite programming problem and hence can be solved efficiently. Experiments show that our method effectively improves deep model's adversarial robustness.", "output": [ "An adversarial defense method bridging robustness of deep neural nets with Lyapunov stability", "The authors formulate training NNs as finding an optimal controller for a discrete dynamical system, allowing them to use method of successive approximations to train a NN in a way to be more robust to adversarial attacks.", "This paper uses the theoretical view of a neural network as a discretized ODE to develop a robust control theory aimed at training the network while enforcing robustness." ] }, { "id": "task668-77593d50328c43d0a69c0f43dc13c426", "input": "Developing conditional generative models for text-to-video synthesis is an extremely challenging yet an important topic of research in machine learning. In this work, we address this problem by introducing Text-Filter conditioning Generative Adversarial Network (TFGAN), a GAN model with novel conditioning scheme that aids improving the text-video associations. With a combination of this conditioning scheme and a deep GAN architecture, TFGAN generates photo-realistic videos from text on very challenging real-world video datasets. In addition, we construct a benchmark synthetic dataset of moving shapes to systematically evaluate our conditioning scheme. Extensive experiments demonstrate that TFGAN significantly outperforms the existing approaches, and can also generate videos of novel categories not seen during training.\n", "output": [ "An effective text-conditioning GAN framework for generating videos from text", "This paper presents a GAN-based method for video generation conditioned on text description, with a new conditioning method that generates convolution filters from the encoded text, and uses them for a convolution in the discriminator.", "This paper proposes conditional GAN models for text-to-video synthesis: developing text-feature-conditioned CNN filters and constructing moving-shape dataset with improved performance on video/image generation." ] }, { "id": "task668-ceedd7d836d743488ac85efbc8baee51", "input": "Cortical neurons process and integrate information on multiple timescales. In addition, these timescales or temporal receptive fields display functional and hierarchical organization. For instance, areas important for working memory (WM), such as prefrontal cortex, utilize neurons with stable temporal receptive fields and long timescales to support reliable representations of stimuli. Despite of the recent advances in experimental techniques, the underlying mechanisms for the emergence of neuronal timescales long enough to support WM are unclear and challenging to investigate experimentally. Here, we demonstrate that spiking recurrent neural networks (RNNs) designed to perform a WM task reproduce previously observed experimental findings and that these models could be utilized in the future to study how neuronal timescales specific to WM emerge.", "output": [ "Spiking recurrent neural networks performing a working memory task utilize long heterogeneous timescales, strikingly similar to those observed in prefrontal cortex." ] }, { "id": "task668-d0846a89265d40cb947bbc44417819ee", "input": "Most deep latent factor models choose simple priors for simplicity, tractability\n or not knowing what prior to use. Recent studies show that the choice of\n the prior may have a profound effect on the expressiveness of the model,\n especially when its generative network has limited capacity. In this paper, we propose to learn a proper prior from data for adversarial autoencoders\n (AAEs). We introduce the notion of code generators to transform manually selected\n simple priors into ones that can better characterize the data distribution. Experimental results show that the proposed model can generate better image quality and learn better disentangled representations than\n AAEs in both supervised and unsupervised settings. Lastly, we present its\n ability to do cross-domain translation in a text-to-image synthesis task.", "output": [ "Learning Priors for Adversarial Autoencoders", "Proposes a simple extension of adversarial auto-encoders for conditional image generation.", "Focuses on adversarial autoencoders and introduces a code generator network to transform a simple prior into one that together with the generator can better fit the data distribution" ] }, { "id": "task668-3b05d163dd6f4b88b61e621eca28a366", "input": "Deep learning, a rebranding of deep neural network research works, has achieved a remarkable success in recent years. With multiple hidden layers, deep learning models aim at computing the hierarchical feature representations of the observational data. Meanwhile, due to its severe disadvantages in data consumption, computational resources, parameter tuning costs and the lack of result explainability, deep learning has also suffered from lots of criticism. In this paper, we will introduce a new representation learning model, namely “Sample-Ensemble Genetic Evolutionary Network” (SEGEN), which can serve as an alternative approach to deep learning models. Instead of building one single deep model, based on a set of sampled sub-instances, SEGEN adopts a genetic-evolutionary learning strategy to build a group of unit models generations by generations. The unit models incorporated in SEGEN can be either traditional machine learning models or the recent deep learning models with a much “narrower” and “shallower” architecture. The learning results of each instance at the final generation will be effectively combined from each unit model via diffusive propagation and ensemble learning strategies. From the computational perspective, SEGEN requires far less data, fewer computational resources and parameter tuning efforts, but has sound theoretic interpretability of the learning process and results. Extensive experiments have been done on several different real-world benchmark datasets, and the experimental results obtained by SEGEN have demonstrated its advantages over the state-of-the-art representation learning models.", "output": [ "We introduce a new representation learning model, namely “Sample-Ensemble Genetic Evolutionary Network” (SEGEN), which can serve as an alternative approach to deep learning models." ] }, { "id": "task668-80f5bb3d5b9c4c91aa136e4cbe07b6d4", "input": "Within-class variation in a high-dimensional dataset can be modeled as being on a low-dimensional manifold due to the constraints of the physical processes producing that variation (e.g., translation, illumination, etc.). We desire a method for learning a representation of the manifolds induced by identity-preserving transformations that can be used to increase robustness, reduce the training burden, and encourage interpretability in machine learning tasks. In particular, what is needed is a representation of the transformation manifold that can robustly capture the shape of the manifold from the input data, generate new points on the manifold, and extend transformations outside of the training domain without significantly increasing the error. Previous work has proposed algorithms to efficiently learn analytic operators (called transport operators) that define the process of transporting one data point on a manifold to another. The main contribution of this paper is to define two transfer learning methods that use this generative manifold representation to learn natural transformations and incorporate them into new data. The first method uses this representation in a novel randomized approach to transfer learning that employs the learned generative model to map out unseen regions of the data space. These results are shown through demonstrations of transfer learning in a data augmentation task for few-shot image classification. The second method use of transport operators for injecting specific transformations into new data examples which allows for realistic image animation and informed data augmentation. These results are shown on stylized constructions using the classic swiss roll data structure and in demonstrations of transfer learning in a data augmentation task for few-shot image classification. We also propose the use of transport operators for injecting transformations into new data examples which allows for realistic image animation.", "output": [ "Learning transport operators on manifolds forms a valuable representation for doing tasks like transfer learning.", "Uses a dictionary learning framework to learn manifold transport operators on augmented USPS digits.", "The paper considers the framework of manifold transport operator learning of Culpepper and Olshausen (2009), and interprets it as obtaining a MAP estimate under a probabilistic generative model." ] }, { "id": "task668-b7254601c8bd48d08b5088b53dbd8469", "input": "Learning in environments with large state and action spaces, and sparse rewards, can hinder a Reinforcement Learning (RL) agent’s learning through trial-and-error. For instance, following natural language instructions on the Web (such as booking a flight ticket) leads to RL settings where input vocabulary and number of actionable elements on a page can grow very large. Even though recent approaches improve the success rate on relatively simple environments with the help of human demonstrations to guide the exploration, they still fail in environments where the set of possible instructions can reach millions. We approach the aforementioned problems from a different perspective and propose guided RL approaches that can generate unbounded amount of experience for an agent to learn from. Instead of learning from a complicated instruction with a large vocabulary, we decompose it into multiple sub-instructions and schedule a curriculum in which an agent is tasked with a gradually increasing subset of these relatively easier sub-instructions. In addition, when the expert demonstrations are not available, we propose a novel meta-learning framework that generates new instruction following tasks and trains the agent more effectively. We train DQN, deep reinforcement learning agent, with Q-value function approximated with a novel QWeb neural network architecture on these smaller, synthetic instructions. We evaluate the ability of our agent to generalize to new instructions onWorld of Bits benchmark, on forms with up to 100 elements, supporting 14 million possible instructions. The QWeb agent outperforms the baseline without using any human demonstration achieving 100% success rate on several difficult environments.", "output": [ "We train reinforcement learning policies using reward augmentation, curriculum learning, and meta-learning to successfully navigate web pages.", "Develops a curriculum learning method for training an RL agent to navigate a web, based on the idea of decomposing an instruction in to multiple sub-instructions." ] }, { "id": "task668-3b51a2cfcec6494e8bb987b3231d4e07", "input": "In artificial neural networks trained with gradient descent, the weights used for processing stimuli are also used during backward passes to calculate gradients. For the real brain to approximate gradients, gradient information would have to be propagated separately, such that one set of synaptic weights is used for processing and another set is used for backward passes. This produces the so-called \"weight transport problem\" for biological models of learning, where the backward weights used to calculate gradients need to mirror the forward weights used to process stimuli. This weight transport problem has been considered so hard that popular proposals for biological learning assume that the backward weights are simply random, as in the feedback alignment algorithm. However, such random weights do not appear to work well for large networks. Here we show how the discontinuity introduced in a spiking system can lead to a solution to this problem. The resulting algorithm is a special case of an estimator used for causal inference in econometrics, regression discontinuity design. We show empirically that this algorithm rapidly makes the backward weights approximate the forward weights. As the backward weights become correct, this improves learning performance over feedback alignment on tasks such as Fashion-MNIST and CIFAR-10. Our results demonstrate that a simple learning rule in a spiking network can allow neurons to produce the right backward connections and thus solve the weight transport problem.", "output": [ "We present a learning rule for feedback weights in a spiking neural network that addresses the weight transport problem." ] }, { "id": "task668-dcd8b9030c7f4737b771ecde2eec523e", "input": "We address two challenges of probabilistic topic modelling in order to better estimate\n the probability of a word in a given context, i.e., P(wordjcontext) : (1) No\n Language Structure in Context: Probabilistic topic models ignore word order by\n summarizing a given context as a “bag-of-word” and consequently the semantics\n of words in the context is lost. In this work, we incorporate language structure\n by combining a neural autoregressive topic model (TM) with a LSTM based language\n model (LSTM-LM) in a single probabilistic framework. The LSTM-LM\n learns a vector-space representation of each word by accounting for word order\n in local collocation patterns, while the TM simultaneously learns a latent representation\n from the entire document. In addition, the LSTM-LM models complex\n characteristics of language (e.g., syntax and semantics), while the TM discovers\n the underlying thematic structure in a collection of documents. We unite two complementary\n paradigms of learning the meaning of word occurrences by combining\n a topic model and a language model in a unified probabilistic framework, named\n as ctx-DocNADE. (2) Limited Context and/or Smaller training corpus of documents:\n In settings with a small number of word occurrences (i.e., lack of context)\n in short text or data sparsity in a corpus of few documents, the application of TMs\n is challenging. We address this challenge by incorporating external knowledge\n into neural autoregressive topic models via a language modelling approach: we\n use word embeddings as input of a LSTM-LM with the aim to improve the wordtopic\n mapping on a smaller and/or short-text corpus. The proposed DocNADE\n extension is named as ctx-DocNADEe.\n\n We present novel neural autoregressive topic model variants coupled with neural\n language models and embeddings priors that consistently outperform state-of-theart\n generative topic models in terms of generalization (perplexity), interpretability\n (topic coherence) and applicability (retrieval and classification) over 6 long-text\n and 8 short-text datasets from diverse domains.", "output": [ "Unified neural model of topic and language modeling to introduce language structure in topic models for contextualized topic vectors " ] }, { "id": "task668-07fafa7c2ee048feb4a22b1ece8250cf", "input": "We consider tackling a single-agent RL problem by distributing it to $n$ learners. These learners, called advisors, endeavour to solve the problem from a different focus. Their advice, taking the form of action values, is then communicated to an aggregator, which is in control of the system. We show that the local planning method for the advisors is critical and that none of the ones found in the literature is flawless: the \\textit{egocentric} planning overestimates values of states where the other advisors disagree, and the \\textit{agnostic} planning is inefficient around danger zones. We introduce a novel approach called \\textit{empathic} and discuss its theoretical aspects. We empirically examine and validate our theoretical findings on a fruit collection task.", "output": [ "We consider tackling a single-agent RL problem by distributing it to $n$ learners." ] }, { "id": "task668-e759dec0faef4842a772263952efe167", "input": "In multi-agent systems, complex interacting behaviors arise due to the high correlations among agents. However, previous work on modeling multi-agent interactions from demonstrations is primarily constrained by assuming the independence among policies and their reward structures. \n In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents’ policies, which can recover agents' policies that can regenerate similar interactions. Consequently, we develop a Decentralized Adversarial Imitation Learning algorithm with Correlated policies (CoDAIL), which allows for decentralized training and execution. Various experiments demonstrate that CoDAIL can better regenerate complex interactions close to the demonstrators and outperforms state-of-the-art multi-agent imitation learning methods. Our code is available at \\url{https://github.com/apexrl/CoDAIL}.", "output": [ "Modeling complex multi-agent interactions under multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents’ policies. " ] }, { "id": "task668-e6b418b9a9814f02ab093daf61c5f6ba", "input": "We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE). Capturing attribute-neighborhood relationships over multiple scales is useful for a diverse range of applications, including latent feature identification across disconnected networks with similar attributes. We prove theoretically that matrices of node-feature pointwise mutual information are implicitly factorized by the embeddings. Experiments show that our algorithms are robust, computationally efficient and outperform comparable models on social, web and citation network datasets.", "output": [ "We develop efficient multi-scale approximate attributed network embedding procedures with provable properties." ] }, { "id": "task668-28eedc9d72e948fb9808debdc9cdaead", "input": "People with high-frequency hearing loss rely on hearing aids that employ frequency lowering algorithms. These algorithms shift some of the sounds from the high frequency band to the lower frequency band where the sounds become more perceptible for the people with the condition. Fricative phonemes have an important part of their content concentrated in high frequency bands. It is important that the frequency lowering algorithm is activated exactly for the duration of a fricative phoneme, and kept off at all other times. Therefore, timely (with zero delay) and accurate fricative phoneme detection is a key problem for high quality hearing aids. In this paper we present a deep learning based fricative phoneme detection algorithm that has zero detection delay and achieves state-of-the-art fricative phoneme detection accuracy on the TIMIT Speech Corpus. All reported results are reproducible and come with easy to use code that could serve as a baseline for future research.\n", "output": [ "A deep learning based approach for zero delay fricative phoneme detection", "This paper apples supervised deep learning methods to detect exact duration of a fricative phoneme in order to improve practical frequency lowering algorithm." ] }, { "id": "task668-e55d05fee2674802bd679360f0d01f05", "input": "We propose a novel method for incorporating conditional information into a generative adversarial network (GAN) for structured prediction tasks. This method is based on fusing features from the generated and conditional information in feature space and allows the discriminator to better capture higher-order statistics from the data. This method also increases the strength of the signals passed through the network where the real or generated data and the conditional data agree. The proposed method is conceptually simpler than the joint convolutional neural network - conditional Markov random field (CNN-CRF) models and enforces higher-order consistency without being limited to a very specific class of high-order potentials. Experimental results demonstrate that this method leads to improvement on a variety of different structured prediction tasks including image synthesis, semantic segmentation, and depth estimation.", "output": [ "We propose a novel way to incorporate conditional image information into the discriminator of GANs using feature fusion that can be used for structured prediction tasks." ] }, { "id": "task668-05bf44f7b5b649bf97227707671ae256", "input": "One of the unresolved questions in deep learning is the nature of the solutions that are being discovered. We investigate the collection of solutions reached by the same network architecture, with different random initialization of weights and random mini-batches. These solutions are shown to be rather similar - more often than not, each train and test example is either classified correctly by all the networks, or by none at all. Surprisingly, all the network instances seem to share the same learning dynamics, whereby initially the same train and test examples are correctly recognized by the learned model, followed by other examples which are learned in roughly the same order. When extending the investigation to heterogeneous collections of neural network architectures, once again examples are seen to be learned in the same order irrespective of architecture, although the more powerful architecture may continue to learn and thus achieve higher accuracy. This pattern of results remains true even when the composition of classes in the test set is unrelated to the train set, for example, when using out of sample natural images or even artificial images. To show the robustness of these phenomena we provide an extensive summary of our empirical study, which includes hundreds of graphs describing tens of thousands of networks with varying NN architectures, hyper-parameters and domains. We also discuss cases where this pattern of similarity breaks down, which show that the reported similarity is not an artifact of optimization by gradient descent. Rather, the observed pattern of similarity is characteristic of learning complex problems with big networks. Finally, we show that this pattern of similarity seems to be strongly correlated with effective generalization.", "output": [ "Most neural networks approximate the same classification function, even across architectures, through all stages of learning." ] }, { "id": "task668-a77c1209f4d24298be4215e9b8288cd5", "input": "There has been an increasing use of neural networks for music information retrieval tasks. In this paper, we empirically investigate different ways of improving the performance of convolutional neural networks (CNNs) on spectral audio features. More specifically, we explore three aspects of CNN design: depth of the network, the use of residual blocks along with the use of grouped convolution, and global aggregation over time. The application context is singer classification and singing performance embedding and we believe the conclusions extend to other types of music analysis using convolutional neural networks. The results show that global time aggregation helps to improve the performance of CNNs the most. Another contribution of this paper is the release of a singing recording dataset that can be used for training and evaluation.", "output": [ "Using deep learning techniques on singing voice related tasks." ] }, { "id": "task668-8698d4e80a2040f094c4dc71358bcc5c", "input": "The quality of the features used in visual recognition is of fundamental importance for the overall system. For a long time, low-level hand-designed feature algorithms as SIFT and HOG have obtained the best results on image recognition. Visual features have recently been extracted from trained convolutional neural networks. Despite the high-quality results, one of the main drawbacks of this approach, when compared with hand-designed features, is the training time required during the learning process. In this paper, we propose a simple and fast way to train supervised convolutional models to feature extraction while still maintaining its high-quality. This methodology is evaluated on different datasets and compared with state-of-the-art approaches.", "output": [ "A simple fast method for extracting visual features from convolutional neural networks", "Proposes a fast way to learn convolutional features that later can be used with any classifier by using reduced numbers of training epocs and specific schedule delays of learning rate", "Use a learning rate decay scheme that is fixed relative to the number of epochs used in training and extract the penultimate layer output as features to train a conventional classifier." ] }, { "id": "task668-1e8d276b5ba74490a88e599f2c14680b", "input": "Weight decay is one of the standard tricks in the neural network toolbox, but the reasons for its regularization effect are poorly understood, and recent results have cast doubt on the traditional interpretation in terms of $L_2$ regularization.\n Literal weight decay has been shown to outperform $L_2$ regularization for optimizers for which they differ. \n We empirically investigate weight decay for three optimization algorithms (SGD, Adam, and K-FAC) and a variety of network architectures. We identify three distinct mechanisms by which weight decay exerts a regularization effect, depending on the particular optimization algorithm and architecture: (1) increasing the effective learning rate, (2) approximately regularizing the input-output Jacobian norm, and (3) reducing the effective damping coefficient for second-order optimization. \n Our results provide insight into how to improve the regularization of neural networks.", "output": [ "We investigate weight decay regularization for different optimizers and identify three distinct mechanisms by which weight decay improves generalization.", "Discusses the effect of weight decay on the training of deep network models with and without batch normalization and when using first/second order optimization methods and hypothesizes that a larger learning rate has a regularization effect." ] }, { "id": "task668-6d05cc0a92a34a7a893498970073cb56", "input": "Most deep learning models rely on expressive high-dimensional representations to achieve good performance on tasks such as classification. However, the high dimensionality of these representations makes them difficult to interpret and prone to over-fitting. We propose a simple, intuitive and scalable dimension reduction framework that takes into account the soft probabilistic interpretation of standard deep models for classification. When applying our framework to visualization, our representations more accurately reflect inter-class distances than standard visualization techniques such as t-SNE. We show experimentally that our framework improves generalization performance to unseen categories in zero-shot learning. We also provide a finite sample error upper bound guarantee for the method.", "output": [ "dimensionality reduction for cases where examples can be represented as soft probability distributions" ] }, { "id": "task668-b9ac26b668ca469c9072ce3dde304d43", "input": "Referential games offer a grounded learning environment for neural agents which accounts for the fact that language is functionally used to communicate. However, they do not take into account a second constraint considered to be fundamental for the shape of human language: that it must be learnable by new language learners and thus has to overcome a transmission bottleneck.\n In this work, we insert such a bottleneck in a referential game, by introducing a changing population of agents in which new agents learn by playing with more experienced agents. We show that mere cultural transmission results in a substantial improvement in language efficiency and communicative success, measured in convergence speed, degree of structure in the emerged languages and within-population consistency of the language. However, as our core contribution, we show that the optimal situation is to co-evolve language and agents. When we allow the agent population to evolve through genotypical evolution, we achieve across the board improvements on all considered metrics. These results stress that for language emergence studies cultural evolution is important, but also the suitability of the architecture itself should be considered.", "output": [ "We enable both the cultural evolution of language and the genetic evolution of agents in a referential game, using a new Language Transmission Engine." ] }, { "id": "task668-01880c13082945c5a4331f0fede09495", "input": "Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems. Recently, critical aspects such as experimental design and image priors are optimized through deep neural networks formed by the unrolled iterations of classical physics-based reconstructions (termed physics-based networks). However, for real-world large-scale systems, computing gradients via backpropagation restricts learning due to memory limitations of graphical processing units. In this work, we propose a memory-efficient learning procedure that exploits the reversibility of the network’s layers to enable data-driven design for large-scale computational imaging. We demonstrate our methods practicality on two large-scale systems: super-resolution optical microscopy and multi-channel magnetic resonance imaging.", "output": [ "We propose a memory-efficient learning procedure that exploits the reversibility of the network’s layers to enable data-driven design for large-scale computational imaging." ] }, { "id": "task668-4e9fe31909874761889ac874e620aa0c", "input": "In contrast to the monolithic deep architectures used in deep learning today for computer vision, the visual cortex processes retinal images via two functionally distinct but interconnected networks: the ventral pathway for processing object-related information and the dorsal pathway for processing motion and transformations. Inspired by this cortical division of labor and properties of the magno- and parvocellular systems, we explore an unsupervised approach to feature learning that jointly learns object features and their transformations from natural videos. We propose a new convolutional bilinear sparse coding model that (1) allows independent feature transformations and (2) is capable of processing large images. Our learning procedure leverages smooth motion in natural videos. Our results show that our model can learn groups of features and their transformations directly from natural videos in a completely unsupervised manner. The learned \"dynamic filters\" exhibit certain equivariance properties, resemble cortical spatiotemporal filters, and capture the statistics of transitions between video frames. Our model can be viewed as one of the first approaches to demonstrate unsupervised learning of primary \"capsules\" (proposed by Hinton and colleagues for supervised learning) and has strong connections to the Lie group approach to visual perception.", "output": [ "We extend bilinear sparse coding and leverage video sequences to learn dynamic filters." ] }, { "id": "task668-da8f3dec97e4439396d8a3c9e2042565", "input": "The Wasserstein probability metric has received much attention from the machine learning community. Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the Wasserstein metric reflects the underlying geometry between outcomes. The value of being sensitive to this geometry has been demonstrated, among others, in ordinal regression and generative modelling, and most recently in reinforcement learning. In this paper we describe three natural properties of probability divergences that we believe reflect requirements from machine learning: sum invariance, scale sensitivity, and unbiased sample gradients. The Wasserstein metric possesses the first two properties but, unlike the Kullback-Leibler divergence, does not possess the third. We provide empirical evidence suggesting this is a serious issue in practice. Leveraging insights from probabilistic forecasting we propose an alternative to the Wasserstein metric, the Cramér distance. We show that the Cramér distance possesses all three desired properties, combining the best of the Wasserstein and Kullback-Leibler divergences. We give empirical results on a number of domains comparing these three divergences. To illustrate the practical relevance of the Cramér distance we design a new algorithm, the Cramér Generative Adversarial Network (GAN), and show that it has a number of desirable properties over the related Wasserstein GAN.\n", "output": [ "The Wasserstein distance is hard to minimize with stochastic gradient descent, while the Cramer distance can be optimized easily and works just as well.", "The manuscript proposes to use the Cramer distance to act as a loss when optimizing an objective function using stochastic gradient descent because it has unbiased sample gradients.", "The contribution of the article is related to performance criteria, in particular to the Wasserstein/Mallows metric" ] }, { "id": "task668-b9d8459e24954f70a949c1f6f1a3136d", "input": "Model-agnostic meta-learning (MAML) is known as a powerful meta-learning method. However, MAML is notorious for being hard to train because of the existence of two learning rates. Therefore, in this paper, we derive the conditions that inner learning rate $\\alpha$ and meta-learning rate $\\beta$ must satisfy for MAML to converge to minima with some simplifications. We find that the upper bound of $\\beta$ depends on $ \\alpha$, in contrast to the case of using the normal gradient descent method. Moreover, we show that the threshold of $\\beta$ increases as $\\alpha$ approaches its own upper bound. This result is verified by experiments on various few-shot tasks and architectures; specifically, we perform sinusoid regression and classification of Omniglot and MiniImagenet datasets with a multilayer perceptron and a convolutional neural network. Based on this outcome, we present a guideline for determining the learning rates: first, search for the largest possible $\\alpha$; next, tune $\\beta$ based on the chosen value of $\\alpha$.", "output": [ "We analyzed the role of two learning rates in model-agnostic meta-learning in convergence.", "The authors tackled the optimization instability problem in MAML by investigating the two learning rates.", "This paper studies a method to help tune the two learning rates used in the MAML training algorithm." ] }, { "id": "task668-d3b86da4ce9a44daaa80230dd8d1874b", "input": "Stochastic Gradient Descent (SGD) methods using randomly selected batches are widely-used to train neural network (NN) models. Performing design exploration to find the best NN for a particular task often requires extensive training with different models on a large dataset, which is very computationally expensive. The most straightforward method to accelerate this computation is to distribute the batch of SGD over multiple processors. However, large batch training often times leads to degradation in accuracy, poor generalization, and even poor robustness to adversarial attacks. Existing solutions for large batch training either do not work or require massive hyper-parameter tuning. To address this issue, we propose a novel large batch training method which combines recent results in adversarial training (to regularize against ``sharp minima'') and second order optimization (to use curvature information to change batch size adaptively during training). We extensively evaluate our method on Cifar-10/100, SVHN, TinyImageNet, and ImageNet datasets, using multiple NNs, including residual networks as well as compressed networks such as SqueezeNext. Our new approach exceeds the performance of the existing solutions in terms of both accuracy and the number of SGD iterations (up to 1\\% and $3\\times$, respectively). We emphasize that this is achieved without any additional hyper-parameter tuning to tailor our method to any of these experiments.\n", "output": [ "Large batch size training using adversarial training and second order information" ] }, { "id": "task668-f06328bf28194764a05988481bfecee0", "input": "Designing a convolution for a spherical neural network requires a delicate tradeoff between efficiency and rotation equivariance. DeepSphere, a method based on a graph representation of the discretized sphere, strikes a controllable balance between these two desiderata. This contribution is twofold. First, we study both theoretically and empirically how equivariance is affected by the underlying graph with respect to the number of pixels and neighbors. Second, we evaluate DeepSphere on relevant problems. Experiments show state-of-the-art performance and demonstrates the efficiency and flexibility of this formulation. Perhaps surprisingly, comparison with previous work suggests that anisotropic filters might be an unnecessary price to pay.", "output": [ "A graph-based spherical CNN that strikes an interesting balance of trade-offs for a wide variety of applications.", "Combines existing CNN frameworks based on the discretization of a sphere as a graph to show a convergence result which is related to the rotation equivalence on a sphere.", "The authors use the existing graph CNN formulation and a pooling strategy that exploits hierarchical pixelations of the sphere to learn from the discretized sphere." ] }, { "id": "task668-43c28de372dc42109c2aaf48f07aee7d", "input": "Deep generative models have advanced the state-of-the-art in semi-supervised classification, however their capacity for deriving useful discriminative features in a completely unsupervised fashion for classification in difficult real-world data sets, where adequate manifold separation is required has not been adequately explored. Most methods rely on defining a pipeline of deriving features via generative modeling and then applying clustering algorithms, separating the modeling and discriminative processes. We propose a deep hierarchical generative model which uses a mixture of discrete and continuous distributions to learn to effectively separate the different data manifolds and is trainable end-to-end. We show that by specifying the form of the discrete variable distribution we are imposing a specific structure on the model's latent representations. We test our model's discriminative performance on the task of CLL diagnosis against baselines from the field of computational FC, as well as the Variational Autoencoder literature.", "output": [ "Unsupervised classification via deep generative modeling with controllable feature learning evaluated in a difficult real world task" ] }, { "id": "task668-73f2f91709d441108d9636ff3188bd56", "input": "To improve how neural networks function it is crucial to understand their learning process. The information bottleneck theory of deep learning proposes that neural networks achieve good generalization by compressing their representations to disregard information that is not relevant to the task. However, empirical evidence for this theory is conflicting, as compression was only observed when networks used saturating activation functions. In contrast, networks with non-saturating activation functions achieved comparable levels of task performance but did not show compression. In this paper we developed more robust mutual information estimation techniques, that adapt to hidden activity of neural networks and produce more sensitive measurements of activations from all functions, especially unbounded functions. Using these adaptive estimation techniques, we explored compression in networks with a range of different activation functions. With two improved methods of estimation, firstly, we show that saturation of the activation function is not required for compression, and the amount of compression varies between different activation functions. We also find that there is a large amount of variation in compression between different network initializations. Secondary, we see that L2 regularization leads to significantly increased compression, while preventing overfitting. Finally, we show that only compression of the last layer is positively correlated with generalization.", "output": [ "We developed robust mutual information estimates for DNNs and used them to observe compression in networks with non-saturating activation functions", "This paper studied the popular belief that deep neural networks do information compression for supervised tasks", "This paper proposes a method for the estimation of mutual information for networks with unbounded activation functions and the use of L2 regularization to induce more compression." ] }, { "id": "task668-20488c396aa54b6381d6b98c4696f6fd", "input": "We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs. We use Bayesian optimization (BO) to specifically\n cater to scenarios involving low query budgets to develop query efficient adversarial attacks. We alleviate the issues surrounding BO in regards to optimizing high dimensional deep learning models by effective dimension upsampling techniques. Our proposed approach achieves performance comparable to the state of the art black-box adversarial attacks albeit with a much lower average query count. In particular, in low query budget regimes, our proposed method reduces the query count up to 80% with respect to the state of the art methods.", "output": [ "We show that a relatively simple black-box adversarial attack scheme using Bayesian optimization and dimension upsampling is preferable to existing methods when the number of available queries is very low." ] }, { "id": "task668-8365fed3444148b398e34a499d6eb552", "input": "We propose Stochastic Weight Averaging in Parallel (SWAP), an algorithm to accelerate DNN training. Our algorithm uses large mini-batches to compute an approximate solution quickly and then refines it by averaging the weights of multiple models computed independently and in parallel. The resulting models generalize equally well as those trained with small mini-batches but are produced in a substantially shorter time. We demonstrate the reduction in training time and the good generalization performance of the resulting models on the computer vision datasets CIFAR10, CIFAR100, and ImageNet.", "output": [ "We propose SWAP, a distributed algorithm for large-batch training of neural networks." ] }, { "id": "task668-315c099f9d844319af48cda1d31fb2d8", "input": "Humans are experts at high-fidelity imitation -- closely mimicking a demonstration, often in one attempt. Humans use this ability to quickly solve a task instance, and to bootstrap learning of new tasks. Achieving these abilities in autonomous agents is an open problem. In this paper, we introduce an off-policy RL algorithm (MetaMimic) to narrow this gap. MetaMimic can learn both (i) policies for high-fidelity one-shot imitation of diverse novel skills, and (ii) policies that enable the agent to solve tasks more efficiently than the demonstrators. MetaMimic relies on the principle of storing all experiences in a memory and replaying these to learn massive deep neural network policies by off-policy RL. This paper introduces, to the best of our knowledge, the largest existing neural networks for deep RL and shows that larger networks with normalization are needed to achieve one-shot high-fidelity imitation on a challenging manipulation task.\n The results also show that both types of policy can be learned from vision, in spite of the task rewards being sparse, and without access to demonstrator actions.", "output": [ "We present MetaMimic, an algorithm that takes as input a demonstration dataset and outputs (i) a one-shot high-fidelity imitation policy (ii) an unconditional task policy.", "The paper looks at the problem of one-shot imitation with high accuracy of imitation, extending DDPGfD to use only state trajectories.", "This paper proposes an approach for one-shot imitation with high accuracy, and addresses the common problem of exploration in imitation learning.", "Presents an RL method for learning from video demonstration without access to expert actions" ] }, { "id": "task668-162f17399d2849a7b4c8ca9e40c81e30", "input": "Whereas it is believed that techniques such as Adam, batch normalization and, more recently, SeLU nonlinearities ``solve'' the exploding gradient problem, we show that this is not the case and that in a range of popular MLP architectures, exploding gradients exist and that they limit the depth to which networks can be effectively trained, both in theory and in practice. We explain why exploding gradients occur and highlight the {\\it collapsing domain problem}, which can arise in architectures that avoid exploding gradients. \n\n ResNets have significantly lower gradients and thus can circumvent the exploding gradient problem, enabling the effective training of much deeper networks, which we show is a consequence of a surprising mathematical property. By noticing that {\\it any neural network is a residual network}, we devise the {\\it residual trick}, which reveals that introducing skip connections simplifies the network mathematically, and that this simplicity may be the major cause for their success.", "output": [ "We show that in contras to popular wisdom, the exploding gradient problem has not been solved and that it limits the depth to which MLPs can be effectively trained. We show why gradients explode and how ResNet handles them." ] }, { "id": "task668-cbbe69e7a9cb4aefba7ece9e1869f6c2", "input": "Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images. Following their successful application in image processing and representation learning, an important next step is to consider videos. Learning generative models of video is a much harder task, requiring a model to capture the temporal dynamics of a scene, in addition to the visual presentation of objects. While recent generative models of video have had some success, current progress is hampered by the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples. To this extent we propose Fréchet Video Distance (FVD), a new metric for generative models of video based on FID. We contribute a large-scale human study, which confirms that FVD correlates well with qualitative human judgment of generated videos.", "output": [ "We propose FVD: a new metric for generative models of video based on FID. A large-scale human study confirms that FVD correlates well with qualitative human judgment of generated videos." ] }, { "id": "task668-b312a01c432b49d68cb156566d8b2361", "input": "Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity. Current regularization-based continual learning algorithms need an external representation and extra computation to measure the parameters' \\textit{importance}. In contrast, we propose Uncertainty-guided Continual Bayesian Neural Networks (UCB) where the learning rate adapts according to the uncertainty defined in the probability distribution of the weights in networks. Uncertainty is a natural way to identify \\textit{what to remember} and \\textit{what to change} as we continually learn, and thus mitigate catastrophic forgetting. We also show a variant of our model, which uses uncertainty for weight pruning \n and retains task performance after pruning by saving binary masks per tasks. We evaluate our UCB approach extensively on diverse object classification datasets with short and long sequences of tasks and report superior or on-par performance compared to existing approaches. Additionally, we show that our model does not necessarily need task information at test time, i.e.~it does not presume knowledge of which task a sample belongs to.", "output": [ "A regularization-based approach for continual learning using Bayesian neural networks to predict parameters' importance" ] }, { "id": "task668-a3cce0c0fb8040f1bdcd8856f8c7090a", "input": "In order to choose a neural network architecture that will be effective for a particular modeling problem, one must understand the limitations imposed by each of the potential options. These limitations are typically described in terms of information theoretic bounds, or by comparing the relative complexity needed to approximate example functions between different architectures. In this paper, we examine the topological constraints that the architecture of a neural network imposes on the level sets of all the functions that it is able to approximate. This approach is novel for both the nature of the limitations and the fact that they are independent of network depth for a broad family of activation functions.", "output": [ "This paper proves that skinny neural networks cannot approximate certain functions, no matter how deep they are." ] }, { "id": "task668-443d4acc7fc74cd69c9c85b9f3e981ab", "input": "Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system. However, likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significantly differ from training data. In this paper, we pose that this problem is due to the excessive influence that input complexity has in generative models' likelihoods. We report a set of experiments supporting this hypothesis, and use an estimate of input complexity to derive an efficient and parameter-free OOD score, which can be seen as a likelihood-ratio, akin to Bayesian model comparison. We find such score to perform comparably to, or even better than, existing OOD detection approaches under a wide range of data sets, models, model sizes, and complexity estimates.", "output": [ "We pose that generative models' likelihoods are excessively influenced by the input's complexity, and propose a way to compensate it when detecting out-of-distribution inputs" ] }, { "id": "task668-adc51dbaa5314b0280f86461081842ca", "input": " Analysis methods which enable us to better understand the\n representations and functioning of neural models of language are\n increasingly needed as deep learning becomes the dominant approach\n in NLP. Here we present two methods based on Representational\n Similarity Analysis (RSA) and Tree Kernels (TK) which allow us to\n directly quantify how strongly the information encoded in neural\n activation patterns corresponds to information represented by\n symbolic structures such as syntax trees. We first validate our\n methods on the case of a simple synthetic language for arithmetic\n expressions with clearly defined syntax and semantics, and show that\n they exhibit the expected pattern of results. We then apply our methods to\n correlate neural representations of English sentences with their\n constituency parse trees.", "output": [ "Two methods based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which directly quantify how strongly information encoded in neural activation patterns corresponds to information represented by symbolic structures." ] }, { "id": "task668-d72640745ea64ce0accdb53b56bfd950", "input": "We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. The learning objective is achieved by providing signal to the latent encoding/embedding in VAE without changing its main backbone architecture, hence retaining the desirable properties of the VAE. We design an unsupervised and a supervised strategy in Guided-VAE and observe enhanced modeling and controlling capability over the vanilla VAE. In the unsupervised strategy, we guide the VAE learning by introducing a lightweight decoder that learns latent geometric transformation and principal components; in the supervised strategy, we use an adversarial excitation and inhibition mechanism to encourage the disentanglement of the latent variables. Guided-VAE enjoys its transparency and simplicity for the general representation learning task, as well as disentanglement learning. On a number of experiments for representation learning, improved synthesis/sampling, better disentanglement for classification, and reduced classification errors in meta learning have been observed.", "output": [ "Learning a controllable generative model by performing latent representation disentanglement learning." ] }, { "id": "task668-8cb3954d22f5452f879b060213480df3", "input": "Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications. Recent works have developed accurate MI estimators through provably low-bias approximations and tight variational lower bounds assuming abundant supply of samples, but require an unrealistic number of samples to guarantee statistical significance of the estimation. In this work, we focus on improving data efficiency and propose a Data-Efficient MINE Estimator (DEMINE) that can provide a tight lower confident interval of MI under limited data, through adding cross-validation to the MINE lower bound (Belghazi et al., 2018). Hyperparameter search is employed and a novel meta-learning approach with task augmentation is developed to increase robustness to hyperparamters, reduce overfitting and improve accuracy. With improved data-efficiency, our DEMINE estimator enables statistical testing of dependency at practical dataset sizes. We demonstrate the effectiveness of DEMINE on synthetic benchmarks and a real world fMRI dataset, with application of inter-subject correlation analysis.", "output": [ "A new & practical statistical test of dependency using neural networks, benchmarked on synthetic and a real fMRI datasets.", "Proposes a neural-network-based estimation of mutal information which can reliably work with small datasets, reducing the sample complexity by decoupling the network learning problem and the estimation problem." ] }, { "id": "task668-75f66a01e8ef47acbc361485740bd808", "input": "Deep Convolutional Neural Networks (CNNs) have been repeatedly shown to perform well on image classification tasks, successfully recognizing a broad array of objects when given sufficient training data. Methods for object localization, however, are still in need of substantial improvement. Common approaches to this problem involve the use of a sliding window, sometimes at multiple scales, providing input to a deep CNN trained to classify the contents of the window. In general, these approaches are time consuming, requiring many classification calculations. In this paper, we offer a fundamentally different approach to the localization of recognized objects in images. Our method is predicated on the idea that a deep CNN capable of recognizing an object must implicitly contain knowledge about object location in its connection weights. We provide a simple method to interpret classifier weights in the context of individual classified images. This method involves the calculation of the derivative of network generated activation patterns, such as the activation of output class label units, with regard to each in- put pixel, performing a sensitivity analysis that identifies the pixels that, in a local sense, have the greatest influence on internal representations and object recognition. These derivatives can be efficiently computed using a single backward pass through the deep CNN classifier, producing a sensitivity map of the image. We demonstrate that a simple linear mapping can be learned from sensitivity maps to bounding box coordinates, localizing the recognized object. Our experimental results, using real-world data sets for which ground truth localization information is known, reveal competitive accuracy from our fast technique.", "output": [ "Proposing a novel object localization(detection) approach based on interpreting the deep CNN using internal representation and network's thoughts" ] }, { "id": "task668-2c3cb89e1d404245b937259aa1200f49", "input": "Convolutional Neural Networks (CNN) have been successful in processing data signals that are uniformly sampled in the spatial domain (e.g., images). However, most data signals do not natively exist on a grid, and in the process of being sampled onto a uniform physical grid suffer significant aliasing error and information loss. Moreover, signals can exist in different topological structures as, for example, points, lines, surfaces and volumes. It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh). To this end, we develop mathematical formulations for Non-Uniform Fourier Transforms (NUFT) to directly, and optimally, sample nonuniform data signals of different topologies defined on a simplex mesh into the spectral domain with no spatial sampling error. The spectral transform is performed in the Euclidean space, which removes the translation ambiguity from works on the graph spectrum. Our representation has four distinct advantages: (1) the process causes no spatial sampling error during initial sampling, (2) the generality of this approach provides a unified framework for using CNNs to analyze signals of mixed topologies, (3) it allows us to leverage state-of-the-art backbone CNN architectures for effective learning without having to design a particular architecture for a particular data structure in an ad-hoc fashion, and (4) the representation allows weighted meshes where each element has a different weight (i.e., texture) indicating local properties. We achieve good results on-par with state-of-the-art for 3D shape retrieval task, and new state-of-the-art for point cloud to surface reconstruction task.", "output": [ "We use non-Euclidean Fourier Transformation of shapes defined by a simplicial complex for deep learning, achieving significantly better results than point-based sampling techiques used in current 3D learning literature." ] }, { "id": "task668-2b17c61e28c1467fbbced4da59ed6a0c", "input": "We review the limitations of BLEU and ROUGE -- the most popular metrics used to assess reference summaries against hypothesis summaries, and introduce JAUNE: a set of criteria for what a good metric should behave like and propose concrete ways to use recent Transformers-based Language Models to assess reference summaries against hypothesis summaries.\n\n", "output": [ "Introduces JAUNE: a methodology to replace BLEU and ROUGE score with multidimensional, model-based evaluators for assessing summaries", "This paper proposes a new JAUNE metric for the evaluation of machine translation and text summarization systems, showing that their model corresponds better to ground truth similarity labels than BLEU." ] }, { "id": "task668-a2885e51be614af688d17be202dd870f", "input": "There were many attempts to explain the trade-off between accuracy and adversarial robustness. However, there was no clear understanding of the behaviors of a robust classifier which has human-like robustness.\n We argue (1) why we need to consider adversarial robustness against varying magnitudes of perturbations not only focusing on a fixed perturbation threshold, (2) why we need to use different method to generate adversarially perturbed samples that can be used to train a robust classifier and measure the robustness of classifiers and (3) why we need to prioritize adversarial accuracies with different magnitudes.\n We introduce Lexicographical Genuine Robustness (LGR) of classifiers that combines the above requirements. We also suggest a candidate oracle classifier called \"Optimal Lexicographically Genuinely Robust Classifier (OLGRC)\" that prioritizes accuracy on meaningful adversarially perturbed examples generated by smaller magnitude perturbations. The training algorithm for estimating OLGRC requires lexicographical optimization unlike existing adversarial training methods. To apply lexicographical optimization to neural network, we utilize Gradient Episodic Memory (GEM) which was originally developed for continual learning by preventing catastrophic forgetting.", "output": [ "We try to design and train a classifier whose adversarial robustness is more resemblance to robustness of human." ] }, { "id": "task668-2cbf4f0da19843c8b9a9317881cdd7c4", "input": "We propose Regularized Learning under Label shifts (RLLS), a principled and a practical domain-adaptation algorithm to correct for shifts in the label distribution between a source and a target domain. We first estimate importance weights using labeled source data and unlabeled target data, and then train a classifier on the weighted source samples. We derive a generalization bound for the classifier on the target domain which is independent of the (ambient) data dimensions, and instead only depends on the complexity of the function class. To the best of our knowledge, this is the first generalization bound for the label-shift problem where the labels in the target domain are not available. Based on this bound, we propose a regularized estimator for the small-sample regime which accounts for the uncertainty in the estimated weights. Experiments on the CIFAR-10 and MNIST datasets show that RLLS improves classification accuracy, especially in the low sample and large-shift regimes, compared to previous methods.", "output": [ "A practical and provably guaranteed approach for training efficiently classifiers in the presence of label shifts between Source and Target data sets", "The authors propose a new algorithm for improving the stability of class importance weighting estimation procedure with a two-step procedure.", "The authors consider the problem of learning under label shifts, where label proportions differ while conditionals are equal, and propose an improved estimator with regularization." ] }, { "id": "task668-9d3513141493419389aec554e85894cf", "input": "Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet. However, such models require many thousands of gradient-based weight updates and unique image examples for training. Ideally, the models would rapidly learn visual concepts from only a handful of examples, similar to the manner in which humans learns across many vision tasks. In this paper, we show how 1) neural attention and 2) meta learning techniques can be used in combination with autoregressive models to enable effective few-shot density estimation. Our proposed modifications to PixelCNN result in state-of-the art few-shot density estimation on the Omniglot dataset. Furthermore, we visualize the learned attention policy and find that it learns intuitive algorithms for simple tasks such as image mirroring on ImageNet and handwriting on Omniglot without supervision. Finally, we extend the model to natural images and demonstrate few-shot image generation on the Stanford Online Products dataset.", "output": [ "Few-shot learning PixelCNN", "The paper proposes on using density estimation when the availability of training data is low by using a meta-learning model.", "This paper considers the problem of one/few-shot density estimation, using metalearning techniques that have been applied to one/few-shot supervised learning", "The paper focuses on few shot learning with autoregressive density estimation and improves PixelCNN with neural attention and meta learning techniques." ] }, { "id": "task668-640df445ba1f48dcaaf1fe11cb982e0e", "input": "The ability of overparameterized deep networks to generalize well has been linked to the fact that stochastic gradient descent (SGD) finds solutions that lie in flat, wide minima in the training loss -- minima where the output of the network is resilient to small random noise added to its parameters. \n So far this observation has been used to provide generalization guarantees only for neural networks whose parameters are either \\textit{stochastic} or \\textit{compressed}. In this work, we present a general PAC-Bayesian framework that leverages this observation to provide a bound on the original network learned -- a network that is deterministic and uncompressed. What enables us to do this is a key novelty in our approach: our framework allows us to show that if on training data, the interactions between the weight matrices satisfy certain conditions that imply a wide training loss minimum, these conditions themselves {\\em generalize} to the interactions between the matrices on test data, thereby implying a wide test loss minimum. We then apply our general framework in a setup where we assume that the pre-activation values of the network are not too small (although we assume this only on the training data). In this setup, we provide a generalization guarantee for the original (deterministic, uncompressed) network, that does not scale with product of the spectral norms of the weight matrices -- a guarantee that would not have been possible with prior approaches.", "output": [ "We provide a PAC-Bayes based generalization guarantee for uncompressed, deterministic deep networks by generalizing noise-resilience of the network on the training data to the test data." ] }, { "id": "task668-c879d1204c824058b6804e0d89a1e5a8", "input": "Learning rich and compact representations is an open topic in many fields such as word embedding, visual question-answering, object recognition or image retrieval. Although deep neural networks (convolutional or not) have made a major breakthrough during the last few years by providing hierarchical, semantic and abstract representations for all of these tasks, these representations are not necessary as rich as needed nor as compact as expected. Models using higher order statistics, such as bilinear pooling, provide richer representations at the cost of higher dimensional features. Factorization schemes have been proposed but without being able to reach the original compactness of first order models, or at a heavy loss in performances. This paper addresses these two points by extending factorization schemes to codebook strategies, allowing compact representations with the same dimensionality as first order representations, but with second order performances. Moreover, we extend this framework with a joint codebook and factorization scheme, granting a reduction both in terms of parameters and computation cost. This formulation leads to state-of-the-art results and compact second-order models with few additional parameters and intermediate representations with a dimension similar to that of first-order statistics.", "output": [ "We propose a joint codebook and factorization scheme to improve second order pooling.", "This paper presents a way to combine existing factorized second order representations with a codebook style hard assignment.", "Proposal for a novel bilinear representation based on a codebook model, and an efficient formulation in which codebook-based projections are factorized via shared projection to further reduce parameter size." ] }, { "id": "task668-d1634d6e16894ad09fe53522c3dbb158", "input": "Counterfactual Regret Minimization (CFR) is the most successful algorithm for finding approximate Nash equilibria in imperfect information games. However, CFR's reliance on full game-tree traversals limits its scalability and generality. Therefore, the game's state- and action-space is often abstracted (i.e. simplified) for CFR, and the resulting strategy is then mapped back to the full game. This requires extensive expert-knowledge, is not practical in many games outside of poker, and often converges to highly exploitable policies. A recently proposed method, Deep CFR, applies deep learning directly to CFR, allowing the agent to intrinsically abstract and generalize over the state-space from samples, without requiring expert knowledge. In this paper, we introduce Single Deep CFR (SD-CFR), a variant of Deep CFR that has a lower overall approximation error by avoiding the training of an average strategy network. We show that SD-CFR is more attractive from a theoretical perspective and empirically outperforms Deep CFR with respect to exploitability and one-on-one play in poker.", "output": [ "Better Deep Reinforcement Learning algorithm to approximate Counterfactual Regret Minimization" ] }, { "id": "task668-58a6a4d0de0b4995ab7089ad22ce0761", "input": "Improved generative adversarial network (Improved GAN) is a successful method of using generative adversarial models to solve the problem of semi-supervised learning. However, it suffers from the problem of unstable training. In this paper, we found that the instability is mostly due to the vanishing gradients on the generator. To remedy this issue, we propose a new method to use collaborative training to improve the stability of semi-supervised GAN with the combination of Wasserstein GAN. The experiments have shown that our proposed method is more stable than the original Improved GAN and achieves comparable classification accuracy on different data sets.", "output": [ "Improve Training Stability of Semi-supervised Generative Adversarial Networks with Collaborative Training" ] }, { "id": "task668-b6367f5e6450438fa2d2b710282e9518", "input": "Dense word vectors have proven their values in many downstream NLP tasks over the past few years. However, the dimensions of such embeddings are not easily interpretable. Out of the d-dimensions in a word vector, we would not be able to understand what high or low values mean. Previous approaches addressing this issue have mainly focused on either training sparse/non-negative constrained word embeddings, or post-processing standard pre-trained word embeddings. On the other hand, we analyze conventional word embeddings trained with Singular Value Decomposition, and reveal similar interpretability. We use a novel eigenvector analysis method inspired from Random Matrix Theory and show that semantically coherent groups not only form in the row space, but also the column space. This allows us to view individual word vector dimensions as human-interpretable semantic features.", "output": [ "Without requiring any constraints or post-processing, we show that the salient dimensions of word vectors can be interpreted as semantic features. " ] }, { "id": "task668-de005326753e46818b38f6915cc854fc", "input": "It is well-known that many machine learning models are susceptible to adversarial attacks, in which an attacker evades a classifier by making small perturbations to inputs. This paper discusses how industrial copyright detection tools, which serve a central role on the web, are susceptible to adversarial attacks. We discuss a range of copyright detection systems, and why they are particularly vulnerable to attacks. These vulnerabilities are especially apparent for neural network based systems. As proof of concept, we describe a well-known music identification method and implement this system in the form of a neural net. We then attack this system using simple gradient methods. Adversarial music created this way successfully fools industrial systems, including the AudioTag copyright detector and YouTube's Content ID system. Our goal is to raise awareness of the threats posed by adversarial examples in this space and to highlight the importance of hardening copyright detection systems to attacks.", "output": [ "Adversarial examples can fool YouTube's copyright detection system" ] }, { "id": "task668-c8d7d6048bff4468890562cb6b11a406", "input": "Interpreting generative adversarial network (GAN) training as approximate divergence minimization has been theoretically insightful, has spurred discussion, and has lead to theoretically and practically interesting extensions such as f-GANs and Wasserstein GANs. For both classic GANs and f-GANs, there is an original variant of training and a \"non-saturating\" variant which uses an alternative form of generator update. The original variant is theoretically easier to study, but the alternative variant frequently performs better and is recommended for use in practice. The alternative generator update is often regarded as a simple modification to deal with optimization issues, and it appears to be a common misconception that the two variants minimize the same divergence. In this short note we derive the divergences approximately minimized by the original and alternative variants of GAN and f-GAN training. This highlights important differences between the two variants. For example, we show that the alternative variant of KL-GAN training actually minimizes the reverse KL divergence, and that the alternative variant of conventional GAN training minimizes a \"softened\" version of the reverse KL. We hope these results may help to clarify some of the theoretical discussion surrounding the divergence minimization view of GAN training.", "output": [ "Typical GAN training doesn't optimize Jensen-Shannon, but something like a reverse KL divergence." ] }, { "id": "task668-588d7e9b65f0464784bd998f16d4c6b9", "input": "Parameter pruning is a promising approach for CNN compression and acceleration by eliminating redundant model parameters with tolerable performance loss. Despite its effectiveness, existing regularization-based parameter pruning methods usually drive weights towards zero with large and constant regularization factors, which neglects the fact that the expressiveness of CNNs is fragile and needs a more gentle way of regularization for the networks to adapt during pruning. To solve this problem, we propose a new regularization-based pruning method (named IncReg) to incrementally assign different regularization factors to different weight groups based on their relative importance, whose effectiveness is proved on popular CNNs compared with state-of-the-art methods.", "output": [ " we propose a new regularization-based pruning method (named IncReg) to incrementally assign different regularization factors to different weight groups based on their relative importance.", "This paper proposes a regularization-based pruning method to incrementally assign different regularization factors to different weight groups based on their relative importance." ] }, { "id": "task668-597a25c2924a455f9f8809cd7bf68981", "input": "Recent studies show that convolutional neural networks (CNNs) are vulnerable under various settings, including adversarial examples, backdoor attacks, and distribution shifting. Motivated by the findings that human visual system pays more attention to global structure (e.g., shape) for recognition while CNNs are biased towards local texture features in images, we propose a unified framework EdgeGANRob based on robust edge features to improve the robustness of CNNs in general, which first explicitly extracts shape/structure features from a given image and then reconstructs a new image by refilling the texture information with a trained generative adversarial network (GAN). In addition, to reduce the sensitivity of edge detection algorithm to adversarial perturbation, we propose a robust edge detection approach Robust Canny based on the vanilla Canny algorithm. To gain more insights, we also compare EdgeGANRob with its simplified backbone procedure EdgeNetRob, which performs learning tasks directly on the extracted robust edge features. We find that EdgeNetRob can help boost model robustness significantly but at the cost of the clean model accuracy. EdgeGANRob, on the other hand, is able to improve clean model accuracy compared with EdgeNetRob and without losing the robustness benefits introduced by EdgeNetRob. Extensive experiments show that EdgeGANRob is resilient in different learning tasks under diverse settings.\n", "output": [ "A unified model to improve model robustness against multiple tasks" ] }, { "id": "task668-becf691bbe8c43e087afa769bbb424a1", "input": "The ability to decompose complex multi-object scenes into meaningful abstractions like objects is fundamental to achieve higher-level cognition. Previous approaches for unsupervised object-oriented scene representation learning are either based on spatial-attention or scene-mixture approaches and limited in scalability which is a main obstacle towards modeling real-world scenes. In this paper, we propose a generative latent variable model, called SPACE, that provides a unified probabilistic modeling framework that combines the best of spatial-attention and scene-mixture approaches. SPACE can explicitly provide factorized object representations for foreground objects while also decomposing background segments of complex morphology. Previous models are good at either of these, but not both. SPACE also resolves the scalability problems of previous methods by incorporating parallel spatial-attention and thus is applicable to scenes with a large number of objects without performance degradations. We show through experiments on Atari and 3D-Rooms that SPACE achieves the above properties consistently in comparison to SPAIR, IODINE, and GENESIS. Results of our experiments can be found on our project website: https://sites.google.com/view/space-project-page", "output": [ "We propose a generative latent variable model for unsupervised scene decomposition that provides factorized object representation per foreground object while also decomposing background segments of complex morphology." ] }, { "id": "task668-52cd4705d912478a9aa6be45ee8c823a", "input": "We demonstrate a low effort method that unsupervisedly constructs task-optimized embeddings from existing word embeddings to gain performance on a supervised end-task. This avoids additional labeling or building more complex model architectures by instead providing specialized embeddings better fit for the end-task(s). Furthermore, the method can be used to roughly estimate whether a specific kind of end-task(s) can be learned form, or is represented in, a given unlabeled dataset, e.g. using publicly available probing tasks. We evaluate our method for diverse word embedding probing tasks and by size of embedding training corpus -- i.e. to explore its use in reduced (pretraining-resource) settings.", "output": [ "Morty refits pretrained word embeddings to either: (a) improve overall embedding performance (for Multi-task settings) or improve Single-task performance, while requiring only minimal effort." ] }, { "id": "task668-5d01baa872514d548159d0a7e479e3df", "input": "Generative adversarial networks (GANs) are a powerful framework for generative tasks. However, they are difficult to train and tend to miss modes of the true data generation process. Although GANs can learn a rich representation of the covered modes of the data in their latent space, the framework misses an inverse mapping from data to this latent space. We propose Invariant Encoding Generative Adversarial Networks (IVE-GANs), a novel GAN framework that introduces such a mapping for individual samples from the data by utilizing features in the data which are invariant to certain transformations. Since the model maps individual samples to the latent space, it naturally encourages the generator to cover all modes. We demonstrate the effectiveness of our approach in terms of generative performance and learning rich representations on several datasets including common benchmark image generation tasks.", "output": [ "A noval GAN framework that utilizes transformation-invariant features to learn rich representations and strong generators.", "Proposes a modified GAN objective consisting of a classic GAN term and an invariant encoding term.", "This paper presents the IVE-GAN, a model that introduces en encoder to the Generative Adversarial Network framework." ] }, { "id": "task668-ffcc2ccacc5a4dc6b77b8d1ac4b8f1d1", "input": "We consider the problem of topic modeling in a weakly semi-supervised setting. In this scenario, we assume that the user knows a priori a subset of the topics she wants the model to learn and is able to provide a few exemplar documents for those topics. In addition, while each document may typically consist of multiple topics, we do not assume that the user will identify all its topics exhaustively.\n \n Recent state-of-the-art topic models such as NVDM, referred to herein as Neural Topic Models (NTMs), fall under the variational autoencoder framework. We extend NTMs to the weakly semi-supervised setting by using informative priors in the training objective. After analyzing the effect of informative priors, we propose a simple modification of the NVDM model using a logit-normal posterior that we show achieves better alignment to user-desired topics versus other NTM models.", "output": [ "We propose supervising VAE-style topic models by intelligently adjusting the prior on a per document basis. We find a logit-normal posterior provides the best performance.", "A flexible method of weakly supervising a topic model to achieve better alignment with user intuition." ] }, { "id": "task668-8831870159e24ab2a80b51d9790d024e", "input": "Recently, there has been a surge in interest in safe and robust techniques within reinforcement learning (RL). \n Current notions of risk in RL fail to capture the potential for systemic failures such as abrupt stoppages from system failures or surpassing of safety thresholds and the appropriate responsive controls in such instances. We propose a novel approach to fault-tolerance within RL in which the controller learns a policy can cope with adversarial attacks and random stoppages that lead to failures of the system subcomponents. The results of the paper also cover fault-tolerant (FT) control so that the controller learns to avoid states that carry risk of system failures. By demonstrating that the class of problems is represented by a variant of SGs, we prove the existence of a solution which is a unique fixed point equilibrium of the game and characterise the optimal controller behaviour. We then introduce a value function approximation algorithm that converges to the solution through simulation in unknown environments.", "output": [ "The paper tackles fault-tolerance under random and adversarial stoppages." ] }, { "id": "task668-61ac7baf39a64f7b9b90972fedb45779", "input": "The fields of artificial intelligence and neuroscience have a long history of fertile bi-directional interactions. On the one hand, important inspiration for the development of artificial intelligence systems has come from the study of natural systems of intelligence, the mammalian neocortex in particular. On the other, important inspiration for models and theories of the brain have emerged from artificial intelligence research. A central question at the intersection of these two areas is concerned with the processes by which neocortex learns, and the extent to which they are analogous to the back-propagation training algorithm of deep networks. Matching the data efficiency, transfer and generalisation properties of neocortical learning remains an area of active research in the field of deep learning. Recent advances in our understanding of neuronal, synaptic and dendritic physiology of the neocortex suggest new approaches for unsupervised representation learning, perhaps through a new class of objective functions, which could act alongside or in lieu of back-propagation. Such local learning rules have implicit rather than explicit objectives with respect to the training data, facilitating domain adaptation and generalisation. Incorporating them into deep networks for representation learning could better leverage unlabelled datasets to offer significant improvements in data efficiency of downstream supervised readout learning, and reduce susceptibility to adversarial perturbations, at the cost of a more restricted domain of applicability.\n", "output": [ "Inspiration from local dendritic processes of neocortical learning to make unsupervised learning great again." ] }, { "id": "task668-fe92ec3f9e0c444ea8f5a17d9e0c3cc3", "input": "Deep neural networks are usually huge, which significantly limits the deployment on low-end devices. In recent years, many\n weight-quantized models have been proposed. They have small storage and fast inference, but training can still be time-consuming. This can be improved with distributed learning. To reduce the high communication cost due to worker-server synchronization, recently gradient quantization has also been proposed to train deep networks with full-precision weights. \n In this paper, we theoretically study how the combination of both weight and gradient quantization affects convergence.\n We show that (i) weight-quantized models converge to an error related to the weight quantization resolution and weight dimension; (ii) quantizing gradients slows convergence by a factor related to the gradient quantization resolution and dimension; and (iii) clipping the gradient before quantization renders this factor dimension-free, thus allowing the use of fewer bits for gradient quantization. Empirical experiments confirm the theoretical convergence results, and demonstrate that quantized networks can speed up training and have comparable performance as full-precision networks.", "output": [ "In this paper, we studied efficient training of loss-aware weight-quantized networks with quantized gradient in a distributed environment, both theoretically and empirically.", "This paper studies convergence properties of loss-aware weight quantization with different gradient precisions in the distributed environment, and provides convergence analysis for weight quantization with full-precision, quantized and quantized clipped gradients.", "The authors proposes an analysis of the effect of simultaneously quantizing the weights and gradients in training a parametrized model in a fully-synchronized distributed environment." ] }, { "id": "task668-f98d0d17a5b74a6ba429a2d9868f6ef5", "input": "The backpropagation algorithm is the most popular algorithm training neural networks nowadays. However, it suffers from the forward locking, backward locking and update locking problems, especially when a neural network is so large that its layers are distributed across multiple devices. Existing solutions either can only handle one locking problem or lead to severe accuracy loss or memory inefficiency. Moreover, none of them consider the straggler problem among devices. In this paper, we propose \\textbf{Layer-wise Staleness} and a novel efficient training algorithm, \\textbf{Diversely Stale Parameters} (DSP), which can address all these challenges without loss of accuracy nor memory issue. We also analyze the convergence of DSP with two popular gradient-based methods and prove that both of them are guaranteed to converge to critical points for non-convex problems. Finally, extensive experimental results on training deep convolutional neural networks demonstrate that our proposed DSP algorithm can achieve significant training speedup with stronger robustness and better generalization than compared methods.", "output": [ "We propose Diversely Stale Parameters to break lockings of the backpropoagation algorithm and train a CNN in parallel." ] }, { "id": "task668-bd13189814b64470963e2bf1ec158357", "input": "We study discrete time dynamical systems governed by the state equation $h_{t+1}=ϕ(Ah_t+Bu_t)$. Here A,B are weight matrices, ϕ is an activation function, and $u_t$ is the input data. This relation is the backbone of recurrent neural networks (e.g. LSTMs) which have broad applications in sequential learning tasks. We utilize stochastic gradient descent to learn the weight matrices from a finite input/state trajectory $(u_t,h_t)_{t=0}^N$. We prove that SGD estimate linearly converges to the ground truth weights while using near-optimal sample size. Our results apply to increasing activations whose derivatives are bounded away from zero. The analysis is based on i) an SGD convergence result with nonlinear activations and ii) careful statistical characterization of the state vector. Numerical experiments verify the fast convergence of SGD on ReLU and leaky ReLU in consistence with our theory.", "output": [ "We study the state equation of a recurrent neural network. We show that SGD can efficiently learn the unknown dynamics from few input/output observations under proper assumptions.", "The paper studies discrete-time dynamical systems with a non-linear state equation, proving that running SGD on a fixed-length trajectory gives logarithmic convergence.", "This work considers the problem of learning a non-linear dynamical system in which the output equals the state. ", "\nThis paper studies the ability of SGD to learn dynamics of a linear system and non-linear activation." ] }, { "id": "task668-aa82ba151ef249e8ac2152051c57550d", "input": "Neural architecture search (NAS) has made rapid progress incomputervision,wherebynewstate-of-the-artresultshave beenachievedinaseriesoftaskswithautomaticallysearched neural network (NN) architectures. In contrast, NAS has not made comparable advances in natural language understanding (NLU). Corresponding to encoder-aggregator meta architecture of typical neural networks models for NLU tasks (Gong et al. 2018), we re-define the search space, by splittingitinto twoparts:encodersearchspace,andaggregator search space. Encoder search space contains basic operations such as convolutions, RNNs, multi-head attention and its sparse variants, star-transformers. Dynamic routing is included in the aggregator search space, along with max (avg) pooling and self-attention pooling. Our search algorithm is then fulfilled via DARTS, a differentiable neural architecture search framework. We progressively reduce the search space every few epochs, which further reduces the search time and resource costs. Experiments on five benchmark data-sets show that, the new neural networks we generate can achieve performances comparable to the state-of-the-art models that does not involve language model pre-training.\n", "output": [ "Neural Architecture Search for a series of Natural Language Understanding tasks. Design the search space for NLU tasks. And Apply differentiable architecture search to discover new models" ] }, { "id": "task668-ec7926b5a3fd4fe698eeb915805492b4", "input": "We present a framework for building unsupervised representations of entities and their compositions, where each entity is viewed as a probability distribution rather than a fixed length vector. In particular, this distribution is supported over the contexts which co-occur with the entity and are embedded in a suitable low-dimensional space. This enables us to consider the problem of representation learning with a perspective from Optimal Transport and take advantage of its numerous tools such as Wasserstein distance and Wasserstein barycenters. We elaborate how the method can be applied for obtaining unsupervised representations of text and illustrate the performance quantitatively as well as qualitatively on tasks such as measuring sentence similarity and word entailment, where we empirically observe significant gains (e.g., 4.1% relative improvement over Sent2vec and GenSen).\n\n The key benefits of the proposed approach include: (a) capturing uncertainty and polysemy via modeling the entities as distributions, (b) utilizing the underlying geometry of the particular task (with the ground cost), (c) simultaneously providing interpretability with the notion of optimal transport between contexts and (d) easy applicability on top of existing point embedding methods. In essence, the framework can be useful for any unsupervised or supervised problem (on text or other modalities); and only requires a co-occurrence structure inherent to many problems. The code, as well as pre-built histograms, are available under https://github.com/context-mover.", "output": [ "Represent each entity as a probability distribution over contexts embedded in a ground space.", "Proposes to construct word embeddings from a histogram over context words, instead of as point vectors, which allows for measuring distances between two words in terms of optimal transport between the histograms through a method that augments representation of an entity from standard \"point in a vector space\" to a histogram with bins located at some points in that vector space. " ] }, { "id": "task668-06f2a8ac6a994a14954fbd80e98ef377", "input": "State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to process.\n In this paper, we train Transformer models which can make output predictions at different stages of the network and we investigate different ways to predict how much computation is required for a particular sequence.\n Unlike dynamic computation in Universal Transformers, which applies the same set of layers iteratively, we apply different layers at every step to adjust both the amount of computation as well as the model capacity.\n On IWSLT German-English translation our approach matches the accuracy of a well tuned baseline Transformer while using less than a quarter of the decoder layers.", "output": [ "Sequence model that dynamically adjusts the amount of computation for each input." ] }, { "id": "task668-2481c8f058d64cf1bfad3d6f956162a5", "input": "Convolutional Neural Networks (CNNs) are computationally intensive, which limits their application on mobile devices. Their energy is dominated by the number of multiplies needed to perform the convolutions. Winograd’s minimal filtering algorithm (Lavin, 2015) and network pruning (Han et al., 2015) can reduce the operation count, but these two methods cannot be straightforwardly combined — applying the Winograd transform fills in the sparsity in both the weights and the activations. We propose two modifications to Winograd-based CNNs to enable these methods to exploit sparsity. First, we move the ReLU operation into the Winograd domain to increase the sparsity of the transformed activations. Second, we prune the weights in the Winograd domain to exploit static weight sparsity. For models on CIFAR-10, CIFAR-100 and ImageNet datasets, our method reduces the number of multiplications by 10.4x, 6.8x and 10.8x respectively with loss of accuracy less than 0.1%, outperforming previous baselines by 2.0x-3.0x. We also show that moving ReLU to the Winograd domain allows more aggressive pruning.", "output": [ "Prune and ReLU in Winograd domain for efficient convolutional neural network" ] }, { "id": "task668-43bd37a550984c7599935ffa0b9dabea", "input": "In this paper, we explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks. However, directly applying this approach to text is challenging–lexical features highly informative for one task maybe insignificant for another. Thus, rather than learning solely from words, our model also leverages their distributional signatures, which encode pertinent word occurrence patterns. Our model is trained within a meta-learning framework to map these signatures into attention scores, which are then used to weight the lexical representations of words. We demonstrate that our model consistently outperforms prototypical networks learned on lexical knowledge (Snell et al., 2017) in both few-shot text classification and relation classification by a significant margin across six benchmark datasets (19.96% on average in 1-shot classification).", "output": [ "Meta-learning methods used for vision, directly applied to NLP, perform worse than nearest neighbors on new classes; we can do better with distributional signatures." ] }, { "id": "task668-8004bf79c2ea4ac8bad9d53b21b0cdb0", "input": "Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples. Developing efficient exploration strategies capable of finding the most useful samples becomes critical in such settings. Existing approaches to finding efficient exploration strategies add auxiliary objectives to promote exploration by the pre-update policy, however, this makes the adaptation using a few gradient steps difficult as the pre-update (exploration) and post-update (exploitation) policies are quite different. Instead, we propose to explicitly model a separate exploration policy for the task distribution. Having two different policies gives more flexibility in training the exploration policy and also makes adaptation to any specific task easier. We show that using self-supervised or supervised learning objectives for adaptation stabilizes the training process and also demonstrate the superior performance of our model compared to prior works in this domain.", "output": [ "We propose to use a separate exploration policy to collect the pre-adaptation trajectories in MAML. We also show that using a self-supervised objective in the inner loop leads to more stable training and much better performance." ] }, { "id": "task668-2f72614137d14692a159333022822c90", "input": "We address the problem of learning structured policies for continuous control. In traditional reinforcement learning, policies of agents are learned by MLPs which take the concatenation of all observations from the environment as input for predicting actions. In this work, we propose NerveNet to explicitly model the structure of an agent, which naturally takes the form of a graph. Specifically, serving as the agent's policy network, NerveNet first propagates information over the structure of the agent and then predict actions for different parts of the agent. In the experiments, we first show that our NerveNet is comparable to state-of-the-art methods on standard MuJoCo environments. We further propose our customized reinforcement learning environments for benchmarking two types of structure transfer learning tasks, i.e., size and disability transfer. We demonstrate that policies learned by NerveNet are significantly better than policies learned by other models and are able to transfer even in a zero-shot setting.\n", "output": [ "using graph neural network to model structural information of the agents to improve policy and transferability ", "A method for representing and learning structured policy for continuous control tasks using Graph Neural Networks", "The submission proposes incorporation of additional structure into reinforcement learning problems, particularly the structure of the agent's morphology", "Propose an application of Graph Neural Networks to learning policies for controlling \"centipede\" robots of different lengths." ] }, { "id": "task668-067816147c91457f9b5d9d39f631085b", "input": "One of the key challenges of session-based recommender systems is to enhance users’ purchase intentions. In this paper, we formulate the sequential interactions between user sessions and a recommender agent as a Markov Decision Process (MDP). In practice, the purchase reward is delayed and sparse, and may be buried by clicks, making it an impoverished signal for policy learning. Inspired by the prediction error minimization (PEM) and embodied cognition, we propose a simple architecture to augment reward, namely Imagination Reconstruction Network (IRN). Specifically, IRN enables the agent to explore its environment and learn predictive representations via three key components. The imagination core generates predicted trajectories, i.e., imagined items that users may purchase. The trajectory manager controls the granularity of imagined trajectories using the planning strategies, which balances the long-term rewards and short-term rewards. To optimize the action policy, the imagination-augmented executor minimizes the intrinsic imagination error of simulated trajectories by self-supervised reconstruction, while maximizing the extrinsic reward using model-free algorithms. Empirically, IRN promotes quicker adaptation to user interest, and shows improved robustness to the cold-start scenario and ultimately higher purchase performance compared to several baselines. Somewhat surprisingly, IRN using only the purchase reward achieves excellent next-click prediction performance, demonstrating that the agent can \"guess what you like\" via internal planning.", "output": [ "We propose the IRN architecture to augment sparse and delayed purchase reward for session-based recommendation.", "The paper proposes improving the performance of recommendation systems through reinforcement learning by using an Imagination Reconstruction Network.", "The paper presents a session-based recommendation approach by focusing on user purchases instead of clicks. " ] }, { "id": "task668-03d1c81644dc48eabf5f4c11a405b913", "input": "Recent research has shown that one can train a neural network with binary weights and activations at train time by augmenting the weights with a high-precision continuous latent variable that accumulates small changes from stochastic gradient descent. However, there is a dearth of work to explain why one can effectively capture the features in data with binary weights and activations. Our main result is that the neural networks with binary weights and activations trained using the method of Courbariaux, Hubara et al. (2016) work because of the high-dimensional geometry of binary vectors. In particular, the ideal continuous vectors that extract out features in the intermediate representations of these BNNs are well-approximated by binary vectors in the sense that dot products are approximately preserved. Compared to previous research that demonstrated good classification performance with BNNs, our work explains why these BNNs work in terms of HD geometry. Furthermore, the results and analysis used on BNNs are shown to generalize to neural networks with ternary weights and activations. Our theory serves as a foundation for understanding not only BNNs but a variety of methods that seek to compress traditional neural networks. Furthermore, a better understanding of multilayer binary neural networks serves as a starting point for generalizing BNNs to other neural network architectures such as recurrent neural networks.", "output": [ "Recent successes of Binary Neural Networks can be understood based on the geometry of high-dimensional binary vectors", "Investigates numerically and theoretically the reasons behind the empirical success of binarized neural networks.", "This paper analyzes the effectiveness of binary neural networks and why binarization is able to preserve model performance." ] }, { "id": "task668-6e71460a7092439cbd9c85f5f4d9272e", "input": "In compressed sensing, a primary problem to solve is to reconstruct a high dimensional sparse signal from a small number of observations. In this work, we develop a new sparse signal recovery algorithm using reinforcement learning (RL) and Monte CarloTree Search (MCTS). Similarly to orthogonal matching pursuit (OMP), our RL+MCTS algorithm chooses the support of the signal sequentially. The key novelty is that the proposed algorithm learns how to choose the next support as opposed to following a pre-designed rule as in OMP. Empirical results are provided to demonstrate the superior performance of the proposed RL+MCTS algorithm over existing sparse signal recovery algorithms.", "output": [ "Formulating sparse signal recovery as a sequential decision making problem, we develop a method based on RL and MCTS that learns a policy to discover the support of the sparse signal. " ] }, { "id": "task668-5332251bdc5344a892fe112dcb6af850", "input": "Deep generative models seek to recover the process with which the observed data was generated. They may be used to synthesize new samples or to subsequently extract representations. Successful approaches in the domain of images are driven by several core inductive biases. However, a bias to account for the compositional way in which humans structure a visual scene in terms of objects has frequently been overlooked. In this work we propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition. This provides a way to efficiently learn a more accurate generative model of real-world images, and serves as an initial step towards learning corresponding object representations. We evaluate our approach on several multi-object image datasets, and find that the generator learns to identify and disentangle information corresponding to different objects at a representational level. A human study reveals that the resulting generative model is better at generating images that are more faithful to the reference distribution.", "output": [ "We propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition" ] }, { "id": "task668-55d157cfe43943f8824ed8564cec58b4", "input": "Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e.g. factorised) approximate posteriors in which sampling is an efficient computation that fully exploits GPU parallelism. However, such simple approximate posteriors are often insufficient, as they eliminate statistical dependencies in the posterior. While it is possible to use normalizing flow approximate posteriors for continuous latents, there is nothing analogous for discrete latents. The most natural approach to model discrete dependencies is an autoregressive distribution, but sampling from such distributions is inherently sequential and thus slow. We develop a fast, parallel sampling procedure for autoregressive distributions based on fixed-point iterations which enables efficient and accurate variational inference in discrete state-space models. To optimize the variational bound, we considered two ways to evaluate probabilities: inserting the relaxed samples directly into the pmf for the discrete distribution, or converting to continuous logistic latent variables and interpreting the K-step fixed-point iterations as a normalizing flow. We found that converting to continuous latent variables gave considerable additional scope for mismatch between the true and approximate posteriors, which resulted in biased inferences, we thus used the former approach. We tested our approach on the neuroscience problem of inferring discrete spiking activity from noisy calcium-imaging data, and found that it gave accurate connectivity estimates in an order of magnitude less time.", "output": [ "We give a fast normalising-flow like sampling procedure for discrete latent variable models.", "This paper uses an autoregressive filtering variational approximation for parameter estimation in discrete dynamical systems by using fixed point iterations.", "The authors posit a general autoregressive posterior family for discrete variables or their continuous relaxations. ", "This paper has two main contributions: it extends normalizing flows to discrete settings and presents an approximate fixed-point update rule for autoregressive time-series that can exploit GPU parallelism. " ] }, { "id": "task668-a20b2cec69664cffb62cca6ab03f38e5", "input": "Applying reinforcement learning (RL) to real-world problems will require reasoning about action-reward correlation over long time horizons. Hierarchical reinforcement learning (HRL) methods handle this by dividing the task into hierarchies, often with hand-tuned network structure or pre-defined subgoals. We propose a novel HRL framework TAIC, which learns the temporal abstraction from past experience or expert demonstrations without task-specific knowledge. We formulate the temporal abstraction problem as learning latent representations of action sequences and present a novel approach of regularizing the latent space by adding information-theoretic constraints. Specifically, we maximize the mutual information between the latent variables and the state changes.\n A visualization of the latent space demonstrates that our algorithm learns an effective abstraction of the long action sequences. The learned abstraction allows us to learn new tasks on higher level more efficiently. We convey a significant speedup in convergence over benchmark learning problems. These results demonstrate that learning temporal abstractions is an effective technique in increasing the convergence rate and sample efficiency of RL algorithms.", "output": [ "We propose a novel HRL framework, in which we formulate the temporal abstraction problem as learning a latent representation of action sequence." ] }, { "id": "task668-e118960606bb4042957d2e0c79b22f01", "input": "Design of reliable systems must guarantee stability against input perturbations. In machine learning, such guarantee entails preventing overfitting and ensuring robustness of models against corruption of input data. In order to maximize stability, we analyze and develop a computationally efficient implementation of Jacobian regularization that increases classification margins of neural networks. The stabilizing effect of the Jacobian regularizer leads to significant improvements in robustness, as measured against both random and adversarial input perturbations, without severely degrading generalization properties on clean data.", "output": [ "We analyze and develop a computationally efficient implementation of Jacobian regularization that increases the classification margins of neural networks." ] }, { "id": "task668-8c2905fe659546d4b087bcb84ba4a523", "input": "Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day. This wealth of data can help to learn models that can improve the user experience on each device. However, the scale and heterogeneity of federated data presents new challenges in research areas such as federated learning, meta-learning, and multi-task learning. As the machine learning community begins to tackle these challenges, we are at a critical time to ensure that developments made in these areas are grounded with realistic benchmarks. To this end, we propose Leaf, a modular benchmarking framework for learning in federated settings. Leaf includes a suite of open-source federated datasets, a rigorous evaluation framework, and a set of reference implementations, all geared towards capturing the obstacles and intricacies of practical federated environments.", "output": [ "We present Leaf, a modular benchmarking framework for learning in federated data, with applications to learning paradigms such as federated learning, meta-learning, and multi-task learning." ] }, { "id": "task668-cc24faaed57746c3b352ccba268ddf6c", "input": " As people learn to navigate the world, autonomic nervous system (e.g., ``fight or flight) responses provide intrinsic feedback about the potential consequence of action choices (e.g., becoming nervous when close to a cliff edge or driving fast around a bend.) Physiological changes are correlated with these biological preparations to protect one-self from danger. We present a novel approach to reinforcement learning that leverages a task-independent intrinsic reward function trained on peripheral pulse measurements that are correlated with human autonomic nervous system responses. Our hypothesis is that such reward functions can circumvent the challenges associated with sparse and skewed rewards in reinforcement learning settings and can help improve sample efficiency. We test this in a simulated driving environment and show that it can increase the speed of learning and reduce the number of collisions during the learning stage.", "output": [ "We present a novel approach to reinforcement learning that leverages a task-independent intrinsic reward function trained on peripheral pulse measurements that are correlated with human autonomic nervous system responses. ", "Proposes a reinforcement learning framework based on human emotional reaction in the context of autonomous driving.", "The authors propose to use signals, such as basic autonomic visceral responses that influence decision-making, within the RL framework by augmenting RL reward functions with a model learned directly from human nervous system responses.", "Proposes to use physiological signals to improve performance of reinforcement learning algorithms and build an intrinsic reward function that is less sparse by measuring heart pulse amplitude" ] }, { "id": "task668-9b4820c069c245129a329e0eb9c51f1a", "input": "In natural language inference, the semantics of some words do not affect the inference. Such information is considered superficial and brings overfitting. How can we represent and discard such superficial information? In this paper, we use first order logic (FOL) - a classic technique from meaning representation language – to explain what information is superficial for a given sentence pair. Such explanation also suggests two inductive biases according to its properties. We proposed a neural network-based approach that utilizes the two inductive biases. We obtain substantial improvements over extensive experiments.", "output": [ "We use neural networks to project superficial information out for natural language inference by defining and identifying the superficial information from the perspective of first-order logic.", "This paper tries to reduce superficial information in natural language inference to prevent overfitting, and introduces a graph neural network to model relation between premise and hypothesis. ", "An approach to treat natural language inference using first-order logic and to infuse NLI models with logical information to be more robust at inference." ] }, { "id": "task668-db42485ad7234270ba7c1e1e8c3205ff", "input": "GANs provide a framework for training generative models which mimic a data distribution. However, in many cases we wish to train a generative model to optimize some auxiliary objective function within the data it generates, such as making more aesthetically pleasing images. In some cases, these objective functions are difficult to evaluate, e.g. they may require human interaction. Here, we develop a system for efficiently training a GAN to increase a generic rate of positive user interactions, for example aesthetic ratings. To do this, we build a model of human behavior in the targeted domain from a relatively small set of interactions, and then use this behavioral model as an auxiliary loss function to improve the generative model. As a proof of concept, we demonstrate that this system is successful at improving positive interaction rates simulated from a variety of objectives, and characterize s", "output": [ "We describe how to improve an image generative model according to a slow- or difficult-to-evaluate objective, such as human feedback, which could have many applications, like making more aesthetic images." ] }, { "id": "task668-05cfc6d5240c4af39dfd4f9fe33f9a25", "input": "Modern neural networks are highly overparameterized, with capacity to substantially overfit to training data. Nevertheless, these networks often generalize well in practice. It has also been observed that trained networks can often be ``compressed to much smaller representations. The purpose of this paper is to connect these two empirical observations. Our main technical result is a generalization bound for compressed networks based on the compressed size that, combined with off-the-shelf compression algorithms, leads to state-of-the-art generalization guarantees. In particular, we provide the first non-vacuous generalization guarantees for realistic architectures applied to the ImageNet classification problem. Additionally, we show that compressibility of models that tend to overfit is limited. Empirical results show that an increase in overfitting increases the number of bits required to describe a trained network.", "output": [ "We obtain non-vacuous generalization bounds on ImageNet-scale deep neural networks by combining an original PAC-Bayes bound and an off-the-shelf neural network compression method." ] }, { "id": "task668-7ad7f962b99b42539611962b4156dd5f", "input": "Expressive efficiency refers to the relation between two architectures A and B, whereby any function realized by B could be replicated by A, but there exists functions realized by A, which cannot be replicated by B unless its size grows significantly larger. For example, it is known that deep networks are exponentially efficient with respect to shallow networks, in the sense that a shallow network must grow exponentially large in order to approximate the functions represented by a deep network of polynomial size. In this work, we extend the study of expressive efficiency to the attribute of network connectivity and in particular to the effect of \"overlaps\" in the convolutional process, i.e., when the stride of the convolution is smaller than its filter size (receptive field).\n To theoretically analyze this aspect of network's design, we focus on a well-established surrogate for ConvNets called Convolutional Arithmetic Circuits (ConvACs), and then demonstrate empirically that our results hold for standard ConvNets as well. Specifically, our analysis shows that having overlapping local receptive fields, and more broadly denser connectivity, results in an exponential increase in the expressive capacity of neural networks. Moreover, while denser connectivity can increase the expressive capacity, we show that the most common types of modern architectures already exhibit exponential increase in expressivity, without relying on fully-connected layers.", "output": [ "We analyze how the degree of overlaps between the receptive fields of a convolutional network affects its expressive power.", "The paper studies the expressive power provided by \"overlap\" in convolution layers of DNNs by considering linear activations with product pooling.", "This paper analyzes the expressivity of convolutional arithmetic circuits and shows that an exponentialy large number of non-overlapping ConvACs are required to approximate the grid tensor of an overlapping ConvACs." ] }, { "id": "task668-56167808820a4086ae0a4c6887e22cdb", "input": "While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage requirements. Consequently, model size reduction has become an utmost goal in deep learning. Following the classical bits-back argument, we encode the network weights using a random sample, requiring only a number of bits corresponding to the Kullback-Leibler divergence between the sampled variational distribution and the encoding distribution. By imposing a constraint on the Kullback-Leibler divergence, we are able to explicitly control the compression rate, while optimizing the expected loss on the training set. The employed encoding scheme can be shown to be close to the optimal information-theoretical lower bound, with respect to the employed variational family. On benchmarks LeNet-5/MNIST and VGG-16/CIFAR-10, our approach yields the best test performance for a fixed memory budget, and vice versa, it achieves the highest compression rates for a fixed test performance.\n", "output": [ "This paper proposes an effective coding scheme for neural networks that encodes a random set of weights from a variational distribution." ] }, { "id": "task668-349881745c574dbbaf91b5895f3c6f2f", "input": "Multilingual Neural Machine Translation (NMT) systems are capable of translating between multiple source and target languages within a single system. An important indicator of generalization within these systems is the quality of zero-shot translation - translating between language pairs that the system has never seen during training. However, until now, the zero-shot performance of multilingual models has lagged far behind the quality that can be achieved by using a two step translation process that pivots through an intermediate language (usually English). In this work, we diagnose why multilingual models under-perform in zero shot settings. We propose explicit language invariance losses that guide an NMT encoder towards learning language agnostic representations. Our proposed strategies significantly improve zero-shot translation performance on WMT English-French-German and on the IWSLT 2017 shared task, and for the first time, match the performance of pivoting approaches while maintaining performance on supervised directions.", "output": [ "Simple similarity constraints on top of multilingual NMT enables high quality translation between unseen language pairs for the first time." ] }, { "id": "task668-2fc61eb895f2494f9a63c51f4961eb72", "input": "Recent advances in conditional image generation tasks, such as image-to-image translation and image inpainting, are largely accounted to the success of conditional GAN models, which are often optimized by the joint use of the GAN loss with the reconstruction loss. However, we reveal that this training recipe shared by almost all existing methods causes one critical side effect: lack of diversity in output samples. In order to accomplish both training stability and multimodal output generation, we propose novel training schemes with a new set of losses named moment reconstruction losses that simply replace the reconstruction loss. We show that our approach is applicable to any conditional generation tasks by performing thorough experiments on image-to-image translation, super-resolution and image inpainting using Cityscapes and CelebA dataset. Quantitative evaluations also confirm that our methods achieve a great diversity in outputs while retaining or even improving the visual fidelity of generated samples.", "output": [ "We prove that the mode collapse in conditional GANs is largely attributed to a mismatch between reconstruction loss and GAN loss and introduce a set of novel loss functions as alternatives for reconstruction loss.", "The paper proposes a modification to the traditional conditional GAN objective in order to promote diverse, multimodal generation of images. ", "This paper proposes an alternative to L1/L2 errors that are used to augment adversarial losses when training conditional GANs." ] }, { "id": "task668-7b34bd2469ab4d61af0c40da6995a352", "input": "What would be learned by variational autoencoder(VAE) and what influence the disentanglement of VAE? This paper tries to preliminarily address VAE's intrinsic dimension, real factor, disentanglement and indicator issues theoretically in the idealistic situation and implementation issue practically through noise modeling perspective in the realistic case. On intrinsic dimension issue, due to information conservation, the idealistic VAE learns and only learns intrinsic factor dimension. Besides, suggested by mutual information separation property, the constraint induced by Gaussian prior to the VAE objective encourages the information sparsity in dimension. On disentanglement issue, subsequently, inspired by information conservation theorem the clarification on disentanglement in this paper is made. On real factor issue, due to factor equivalence, the idealistic VAE possibly learns any factor set in the equivalence class. On indicator issue, the behavior of current disentanglement metric is discussed, and several performance indicators regarding the disentanglement and generating influence are subsequently raised to evaluate the performance of VAE model and to supervise the used factors. On implementation issue, the experiments under noise modeling and constraints empirically testify the theoretical analysis and also show their own characteristic in pursuing disentanglement.", "output": [ "This paper tries to preliminarily address the disentanglement theoretically in the idealistic situation and practically through noise modelling perspective in the realistic case.", "Studies the importance of the noise modelling in Gaussian VAE and proposes to train the noise using Empirical-Bayes like fashion.", "Modifying how noise factors are treated when developing VAE models" ] }, { "id": "task668-94ab30e31b81465fb8913df0958c5a9f", "input": "A Synaptic Neural Network (SynaNN) consists of synapses and neurons. Inspired by the synapse research of neuroscience, we built a synapse model with a nonlinear synapse function of excitatory and inhibitory channel probabilities. Introduced the concept of surprisal space and constructed a commutative diagram, we proved that the inhibitory probability function -log(1-exp(-x)) in surprisal space is the topologically conjugate function of the inhibitory complementary probability 1-x in probability space. Furthermore, we found that the derivative of the synapse over the parameter in the surprisal space is equal to the negative Bose-Einstein distribution. In addition, we constructed a fully connected synapse graph (tensor) as a synapse block of a synaptic neural network. Moreover, we proved the gradient formula of a cross-entropy loss function over parameters, so synapse learning can work with the gradient descent and backpropagation algorithms. In the proof-of-concept experiment, we performed an MNIST training and testing on the MLP model with synapse network as hidden layers.", "output": [ "A synaptic neural network with synapse graph and learning that has the feature of topological conjugation and Bose-Einstein distribution in surprisal space. ", "The authors propose a hybrid neural nework composed of a synapse graph that can be embedded into a standard neural network", "Presents a biologically-inspired neural network model based on the excitatory and inhibitory ion channels in the membranes of real cells" ] }, { "id": "task668-025d3c3bc83644f096474ac73dd96d08", "input": "Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their representational properties and limitations. Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures. Our results characterize the discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures. We then develop a simple architecture that is provably the most expressive among the class of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism test. We empirically validate our theoretical findings on a number of graph classification benchmarks, and demonstrate that our model achieves state-of-the-art performance.", "output": [ "We develop theoretical foundations for the expressive power of GNNs and design a provably most powerful GNN." ] }, { "id": "task668-89469429101c428091b4604a7d16b610", "input": "The process of designing neural architectures requires expert knowledge and extensive trial and error.\n While automated architecture search may simplify these requirements, the recurrent neural network (RNN) architectures generated by existing methods are limited in both flexibility and components.\n We propose a domain-specific language (DSL) for use in automated architecture search which can produce novel RNNs of arbitrary depth and width.\n The DSL is flexible enough to define standard architectures such as the Gated Recurrent Unit and Long Short Term Memory and allows the introduction of non-standard RNN components such as trigonometric curves and layer normalization. Using two different candidate generation techniques, random search with a ranking function and reinforcement learning, \nwe explore the novel architectures produced by the RNN DSL for language modeling and machine translation domains.\n The resulting architectures do not follow human intuition yet perform well on their targeted tasks, suggesting the space of usable RNN architectures is far larger than previously assumed.", "output": [ "We define a flexible DSL for RNN architecture generation that allows RNNs of varying size and complexity and propose a ranking function that represents RNNs as recursive neural networks, simulating their performance to decide on the most promising architectures.", "Introduces a new method to generate RNNs architectures using a domain-specific language for two types of generators (random and RL-based) together with a ranking function and evaluator.", "This paper casts the search of good RNN Cell architectures as a black-box optimization problem where examples are represented as an operator tree and scored based on learnt functions or generated by a RL agent.", "This paper investigates meta-learning strategy for automated architecture search in the context of RNN by using a DSL that specifies RNN recurrent operations." ] }, { "id": "task668-e3f19349b54c4d9dbce012127b82bc94", "input": "Generative adversarial networks (GANs) are one of the most popular approaches when it comes to training generative models, among which variants of Wasserstein GANs are considered superior to the standard GAN formulation in terms of learning stability and sample quality. However, Wasserstein GANs require the critic to be 1-Lipschitz, which is often enforced implicitly by penalizing the norm of its gradient, or by globally restricting its Lipschitz constant via weight normalization techniques. Training with a regularization term penalizing the violation of the Lipschitz constraint explicitly, instead of through the norm of the gradient, was found to be practically infeasible in most situations. Inspired by Virtual Adversarial Training, we propose a method called Adversarial Lipschitz Regularization, and show that using an explicit Lipschitz penalty is indeed viable and leads to competitive performance when applied to Wasserstein GANs, highlighting an important connection between Lipschitz regularization and adversarial training.", "output": [ "alternative to gradient penalty" ] }, { "id": "task668-4b0a955db9474088aa4558e36f17eef9", "input": "In anomaly detection (AD), one seeks to identify whether a test sample is abnormal, given a data set of normal samples. A recent and promising approach to AD relies on deep generative models, such as variational autoencoders (VAEs),for unsupervised learning of the normal data distribution. In semi-supervised AD (SSAD), the data also includes a small sample of labeled anomalies. In this work,we propose two variational methods for training VAEs for SSAD. The intuitive idea in both methods is to train the encoder to ‘separate’ between latent vectors for normal and outlier data. We show that this idea can be derived from principled probabilistic formulations of the problem, and propose simple and effective algorithms. Our methods can be applied to various data types, as we demonstrate on SSAD datasets ranging from natural images to astronomy and medicine, and can be combined with any VAE model architecture. When comparing to state-of-the-art SSAD methods that are not specific to particular data types, we obtain marked improvement in outlier detection.", "output": [ "We proposed two VAE modifications that account for negative data examples, and used them for semi-supervised anomaly detection.", "The papers propose two methods of VAE-like approaches for semi-supervised novelty detection, MML-VAE and DP-VAE." ] }, { "id": "task668-b15e0443474947238a96c0996617d2ba", "input": "Generalization error (also known as the out-of-sample error) measures how well the hypothesis learned from training data generalizes to previously unseen data. Proving tight generalization error bounds is a central question in statistical learning theory. In this paper, we obtain generalization error bounds for learning general non-convex objectives, which has attracted significant attention in recent years. We develop a new framework, termed Bayes-Stability, for proving algorithm-dependent generalization error bounds. The new framework combines ideas from both the PAC-Bayesian theory and the notion of algorithmic stability. Applying the Bayes-Stability method, we obtain new data-dependent generalization bounds for stochastic gradient Langevin dynamics (SGLD) and several other noisy gradient methods (e.g., with momentum, mini-batch and acceleration, Entropy-SGD). Our result recovers (and is typically tighter than) a recent result in Mou et al. (2018) and improves upon the results in Pensia et al. (2018). Our experiments demonstrate that our data-dependent bounds can distinguish randomly labelled data from normal data, which provides an explanation to the intriguing phenomena observed in Zhang et al. (2017a). We also study the setting where the total loss is the sum of a bounded loss and an additiona l`2 regularization term. We obtain new generalization bounds for the continuous Langevin dynamic in this setting by developing a new Log-Sobolev inequality for the parameter distribution at any time. Our new bounds are more desirable when the noise level of the processis not very small, and do not become vacuous even when T tends to infinity.", "output": [ "We give some generalization error bounds of noisy gradient methods such as SGLD, Langevin dynamics, noisy momentum and so forth." ] }, { "id": "task668-7673e11797f14fbaabb2d4100dcc7674", "input": "Providing transparency of AI planning systems is crucial for their success in practical applications. In order to create a transparent system, a user must be able to query it for explanations about its outputs. We argue that a key underlying principle for this is the use of causality within a planning model, and that argumentation frameworks provide an intuitive representation of such causality. In this paper, we discuss how argumentation can aid in extracting causalities in plans and models, and how they can create explanations from them.", "output": [ "Argumentation frameworks are used to represent causality of plans/models to be utilized for explanations." ] }, { "id": "task668-70f3470d427d4325b06471027b57ca9d", "input": "Modern deep neural networks have a large amount of weights, which make them difficult to deploy on computation constrained devices such as mobile phones. One common approach to reduce the model size and computational cost is to use low-rank factorization to approximate a weight matrix. However, performing standard low-rank factorization with a small rank can hurt the model expressiveness and significantly decrease the performance. In this work, we propose to use a mixture of multiple low-rank factorizations to model a large weight matrix, and the mixture coefficients are computed dynamically depending on its input. We demonstrate the effectiveness of the proposed approach on both language modeling and image classification tasks. Experiments show that our method not only improves the computation efficiency but also maintains (sometimes outperforms) its accuracy compared with the full-rank counterparts.", "output": [ "A simple modification to low-rank factorization that improves performances (in both image and language tasks) while still being compact." ] }, { "id": "task668-15df390d3fc246a581b1f9061f33c91d", "input": "This paper introduces the concept of continuous convolution to neural networks and deep learning applications in general. Rather than directly using discretized information, input data is first projected into a high-dimensional Reproducing Kernel Hilbert Space (RKHS), where it can be modeled as a continuous function using a series of kernel bases. We then proceed to derive a closed-form solution to the continuous convolution operation between two arbitrary functions operating in different RKHS. Within this framework, convolutional filters also take the form of continuous functions, and the training procedure involves learning the RKHS to which each of these filters is projected, alongside their weight parameters. This results in much more expressive filters, that do not require spatial discretization and benefit from properties such as adaptive support and non-stationarity. Experiments on image classification are performed, using classical datasets, with results indicating that the proposed continuous convolutional neural network is able to achieve competitive accuracy rates with far fewer parameters and a faster convergence rate.", "output": [ "This paper proposes a novel convolutional layer that operates in a continuous Reproducing Kernel Hilbert Space.", "Projecting examples into an RK Hilbert space and performing convolution and filtering into that space.", "This paper formulates a variant of convolutional neural networks which models both activations and filters as continuous functions composed from kernel bases" ] }, { "id": "task668-72d2e782b8fd46d792b9be39c1ded121", "input": "Gated recurrent units (GRUs) were inspired by the common gated recurrent unit, long short-term memory (LSTM), as a means of capturing temporal structure with less complex memory unit architecture. Despite their incredible success in tasks such as natural and artificial language processing, speech, video, and polyphonic music, very little is understood about the specific dynamic features representable in a GRU network. As a result, it is difficult to know a priori how successful a GRU-RNN will perform on a given data set. In this paper, we develop a new theoretical framework to analyze one and two dimensional GRUs as a continuous dynamical system, and classify the dynamical features obtainable with such system.\n We found rich repertoire that includes stable limit cycles over time (nonlinear oscillations), multi-stable state transitions with various topologies, and homoclinic orbits. In addition, we show that any finite dimensional GRU cannot precisely replicate the dynamics of a ring attractor, or more generally, any continuous attractor, and is limited to finitely many isolated fixed points in theory. These findings were then experimentally verified in two dimensions by means of time series prediction.", "output": [ "We classify the the dynamical features one and two GRU cells can and cannot capture in continuous time, and verify our findings experimentally with k-step time series prediction. ", "The authors analyse GRUs with hidden sizes of one and two as continuous-time dynamical systems, claiming that the expressive power of the hidden state representation can provide prior knowledge on how well a GRU will perform on a given dataset", "This paper analyzes GRUs from a dynamical systems perspective, and shows that 2d GRUs can be trained to adopt a variety of fixed points and can approximate line attractors, but cannot mimic a ring attractor.", "Converts GRU equations into continuous time and uses theory and experiemnts to study 1- and 2-dimensional GRU networks and showcase every variety of dynamical topology available in these systems" ] }, { "id": "task668-4982024d2c134ff28ca5933316f56984", "input": "Reinforcement learning (RL) agents improve through trial-and-error, but when reward is sparse and the agent cannot discover successful action sequences, learning stagnates. This has been a notable problem in training deep RL agents to perform web-based tasks, such as booking flights or replying to emails, where a single mistake can ruin the entire sequence of actions. A common remedy is to \"warm-start\" the agent by pre-training it to mimic expert demonstrations, but this is prone to overfitting. Instead, we propose to constrain exploration using demonstrations. From each demonstration, we induce high-level \"workflows\" which constrain the allowable actions at each time step to be similar to those in the demonstration (e.g., \"Step 1: click on a textbox; Step 2: enter some text\"). Our exploration policy then learns to identify successful workflows and samples actions that satisfy these workflows. Workflows prune out bad exploration directions and accelerate the agent’s ability to discover rewards. We use our approach to train a novel neural policy designed to handle the semi-structured nature of websites, and evaluate on a suite of web tasks, including the recent World of Bits benchmark. We achieve new state-of-the-art results, and show that workflow-guided exploration improves sample efficiency over behavioral cloning by more than 100x.", "output": [ "We solve the sparse rewards problem on web UI tasks using exploration guided by demonstrations" ] }, { "id": "task668-3d2c6ac405c14c789d3aaf12f156bc39", "input": "Human world knowledge is both structured and flexible. When people see an object, they represent it not as a pixel array but as a meaningful arrangement of semantic parts. Moreover, when people refer to an object, they provide descriptions that are not merely true but also relevant in the current context. Here, we combine these two observations in order to learn fine-grained correspondences between language and contextually relevant geometric properties of 3D objects. To do this, we employed an interactive communication task with human participants to construct a large dataset containing natural utterances referring to 3D objects from ShapeNet in a wide variety of contexts. Using this dataset, we developed neural listener and speaker models with strong capacity for generalization. By performing targeted lesions of visual and linguistic input, we discovered that the neural listener depends heavily on part-related words and associates these words correctly with the corresponding geometric properties of objects, suggesting that it has learned task-relevant structure linking the two input modalities. We further show that a neural speaker that is `listener-aware' --- that plans its utterances according to how an imagined listener would interpret its words in context --- produces more discriminative referring expressions than an `listener-unaware' speaker, as measured by human performance in identifying the correct object.", "output": [ "How to build neural-speakers/listeners that learn fine-grained characteristics of 3D objects, from referential language.", "The authors provide a study on learning to refer to 3D objects, collecting a dataset of referential expressions and training several models by experimenting with a number of architectural choices" ] }, { "id": "task668-feaa37349b1941cca84d5500e6e769c1", "input": "We introduce a novel method for converting text data into abstract image representations, which allows image-based processing techniques (e.g. image classification networks) to be applied to text-based comparison problems. We apply the technique to entity disambiguation of inventor names in US patents. The method involves converting text from each pairwise comparison between two inventor name records into a 2D RGB (stacked) image representation. We then train an image classification neural network to discriminate between such pairwise comparison images, and use the trained network to label each pair of records as either matched (same inventor) or non-matched (different inventors), obtaining highly accurate results (F1: 99.09%, precision: 99.41%, recall: 98.76%). Our new text-to-image representation method could potentially be used more broadly for other NLP comparison problems, such as disambiguation of academic publications, or for problems that require simultaneous classification of both text and images.", "output": [ "We introduce a novel text representation method which enables image classifiers to be applied to text classification problems, and apply the method to inventor name disambiguation.", "A method to map a pair of textual information into a 2D RGB image that can be fed to 2D convoutional neural networks (image classifiers).", "The authors consider the problem of names disambiguisation for patent names inventors and propose to build an image page representation of the two name strings to compare and to apply an image classifier." ] }, { "id": "task668-56731155b66b43cd8b8d8f30cf4fece4", "input": "Recent work has studied the emergence of language among deep reinforcement learning agents that must collaborate to solve a task. Of particular interest are the factors that cause language to be compositional---i.e., express meaning by combining words which themselves have meaning. Evolutionary linguists have found that in addition to structural priors like those already studied in deep learning, the dynamics of transmitting language from generation to generation contribute significantly to the emergence of compositionality. In this paper, we introduce these cultural evolutionary dynamics into language emergence by periodically replacing agents in a population to create a knowledge gap, implicitly inducing cultural transmission of language. We show that this implicit cultural transmission encourages the resulting languages to exhibit better compositional generalization.", "output": [ "We use cultural transmission to encourage compositionality in languages that emerge from interactions between neural agents." ] }, { "id": "task668-cd99062f5dd84232ad772777f29ff200", "input": "Numerous machine reading comprehension (MRC) datasets often involve manual annotation, requiring enormous human effort, and hence the size of the dataset remains significantly smaller than the size of the data available for unsupervised learning. Recently, researchers proposed a model for generating synthetic question-and-answer data from large corpora such as Wikipedia. This model is utilized to generate synthetic data for training an MRC model before fine-tuning it using the original MRC dataset. This technique shows better performance than other general pre-training techniques such as language modeling, because the characteristics of the generated data are similar to those of the downstream MRC data. However, it is difficult to have high-quality synthetic data comparable to human-annotated MRC datasets. To address this issue, we propose Answer-containing Sentence Generation (ASGen), a novel pre-training method for generating synthetic data involving two advanced techniques, (1) dynamically determining K answers and (2) pre-training the question generator on the answer-containing sentence generation task. We evaluate the question generation capability of our method by comparing the BLEU score with existing methods and test our method by fine-tuning the MRC model on the downstream MRC data after training on synthetic data. Experimental results show that our approach outperforms existing generation methods and increases the performance of the state-of-the-art MRC models across a range of MRC datasets such as SQuAD-v1.1, SQuAD-v2.0, KorQuAD and QUASAR-T without any architectural modifications to the original MRC model.", "output": [ "We propose Answer-containing Sentence Generation (ASGen), a novel pre-training method for generating synthetic data for machine reading comprehension." ] }, { "id": "task668-ffcf6e34f7514949bc43cadebc53dfb0", "input": "There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domain in the presence of a domain-shift. Existing domain adaptation (DA) approaches are not equipped for practical DA scenarios as a result of their reliance on the knowledge of source-target label-set relationship (e.g. Closed-set, Open-set or Partial DA). Furthermore, almost all the prior unsupervised DA works require coexistence of source and target samples even during deployment, making them unsuitable for incremental, real-time adaptation. Devoid of such highly impractical assumptions, we propose a novel two-stage learning process. Initially, in the procurement-stage, the objective is to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift. To achieve this, we enhance the model’s ability to reject out-of-source distribution samples by leveraging the available source data, in a novel generative classifier framework. Subsequently, in the deployment-stage, the objective is to design a unified adaptation algorithm capable of operating across a wide range of category-gaps, with no access to the previously seen source samples. To achieve this, in contrast to the usage of complex adversarial training regimes, we define a simple yet effective source-free adaptation objective by utilizing a novel instance-level weighing mechanism, named as Source Similarity Metric (SSM). A thorough evaluation shows the practical usability of the proposed learning framework with superior DA performance even over state-of-the-art source-dependent approaches.", "output": [ "A novel unsupervised domain adaptation paradigm - performing adaptation without accessing the source data ('source-free') and without any assumption about the source-target category-gap ('universal')." ] }, { "id": "task668-d81a50449b54468e93fb67162f77cb79", "input": "The fast generation and refinement of protein backbones would constitute a major advancement to current methodology for the design and development of de novo proteins. In this study, we train Generative Adversarial Networks (GANs) to generate fixed-length full-atom protein backbones, with the goal of sampling from the distribution of realistic 3-D backbone fragments. We represent protein structures by pairwise distances between all backbone atoms, and present a method for directly recovering and refining the corresponding backbone coordinates in a differentiable manner. We show that interpolations in the latent space of the generator correspond to smooth deformations of the output backbones, and that test set structures not seen by the generator during training exist in its image. Finally, we perform sequence design, relaxation, and ab initio folding of a subset of generated structures, and show that in some cases we can recover the generated folds after forward-folding. Together, these results suggest a mechanism for fast protein structure refinement and folding using external energy functions.", "output": [ "We train a GAN to generate and recover full-atom protein backbones , and we show that in select cases we can recover the generated proteins after sequence design and ab initio forward-folding.", "A generative model for protein backbone which uses a GAN, autoencoder-like network, and refinement process, and a set of qualitative evaluations suggesting positive results.", "This paper presents an end-to-end approach for generating protein backbones using generative adversarial networks." ] }, { "id": "task668-8cff2518c08744b2888ae0920e0accbe", "input": "In most real-world scenarios, training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion. In this paper, we explore a novel yet simple way to alleviate this issue via synthesizing less-frequent classes with adversarial examples of other classes. Surprisingly, we found this counter-intuitive method can effectively learn generalizable features of minority classes by transferring and leveraging the diversity of the majority information. Our experimental results on various types of class-imbalanced datasets in image classification and natural language processing show that the proposed method not only improves the generalization of minority classes significantly compared to other re-sampling or re-weighting methods, but also surpasses other methods of state-of-art level for the class-imbalanced classification.", "output": [ "We develop a new method for imbalanced classification using adversarial examples", "Proposes a new optimization objective that generates synthetic samples by over-sampling the majority classes instead of minority classes, solving the problem of overfitting minority classes.", "The authors propose to tackle imbalance classification using re-sampling methods, showing that adversarial examples in the minority class would help to train a new model that generalizes better." ] }, { "id": "task668-708b459251d740839550e0d05cd66618", "input": "In this paper, we propose the use of in-training matrix factorization to reduce the model size for neural machine translation. Using in-training matrix factorization, parameter matrices may be decomposed into the products of smaller matrices, which can compress large machine translation architectures by vastly reducing the number of learnable parameters. We apply in-training matrix factorization to different layers of standard neural architectures and show that in-training factorization is capable of reducing nearly 50% of learnable parameters without any associated loss in BLEU score. Further, we find that in-training matrix factorization is especially powerful on embedding layers, providing a simple and effective method to curtail the number of parameters with minimal impact on model performance, and, at times, an increase in performance.", "output": [ "This paper proposes using matrix factorization at training time for neural machine translation, which can reduce model size and decrease training time without harming performance.", "This paper proposes to compress models using matrix factorization during training for deep neural networks of machine translation." ] }, { "id": "task668-64d9548affb44cb5a32bd4f5694a83d6", "input": "Training activation quantized neural networks involves minimizing a piecewise constant training loss whose gradient vanishes almost everywhere, which is undesirable for the standard back-propagation or chain rule. An empirical way around this issue is to use a straight-through estimator (STE) (Bengio et al., 2013) in the backward pass only, so that the \"gradient\" through the modified chain rule becomes non-trivial. Since this unusual \"gradient\" is certainly not the gradient of loss function, the following question arises: why searching in its negative direction minimizes the training loss? In this paper, we provide the theoretical justification of the concept of STE by answering this question. We consider the problem of learning a two-linear-layer network with binarized ReLU activation and Gaussian input data. We shall refer to the unusual \"gradient\" given by the STE-modifed chain rule as coarse gradient. The choice of STE is not unique. We prove that if the STE is properly chosen, the expected coarse gradient correlates positively with the population gradient (not available for the training), and its negation is a descent direction for minimizing the population loss. We further show the associated coarse gradient descent algorithm converges to a critical point of the population loss minimization problem. Moreover, we show that a poor choice of STE leads to instability of the training algorithm near certain local minima, which is verified with CIFAR-10 experiments.", "output": [ "We make theoretical justification for the concept of straight-through estimator." ] }, { "id": "task668-0de4eb6782cd4bbd8fba3f10f8ba8265", "input": "Deep Neural Networks (DNNs) are vulnerable to adversarial attacks, especially white-box targeted attacks. This paper studies the problem of how aggressive white-box targeted attacks can be to go beyond widely used Top-1 attacks. We propose to learn ordered Top-k attacks (k>=1), which enforce the Top-k predicted labels of an adversarial example to be the k (randomly) selected and ordered labels (the ground-truth label is exclusive). Two methods are presented. First, we extend the vanilla Carlini-Wagner (C&W) method and use it as a strong baseline. Second, we present an adversarial distillation framework consisting of two components: (i) Computing an adversarial probability distribution for any given ordered Top-$k$ targeted labels. (ii) Learning adversarial examples by minimizing the Kullback-Leibler (KL) divergence between the adversarial distribution and the predicted distribution, together with the perturbation energy penalty. In computing adversarial distributions, we explore how to leverage label semantic similarities, leading to knowledge-oriented attacks. In experiments, we test Top-k (k=1,2,5,10) attacks in the ImageNet-1000 val dataset using two popular DNNs trained with the clean ImageNet-1000 train dataset, ResNet-50 and DenseNet-121. Overall, the adversarial distillation approach obtains the best results, especially by large margin when computation budget is limited.. It reduces the perturbation energy consistently with the same attack success rate on all the four k's, and improve the attack success rate by large margin against the modified C&W method for k=10. ", "output": [ "ordered Top-k adversarial attacks" ] }, { "id": "task668-3f9e888c05e94db689fcb6cc29b144d9", "input": "The task of visually grounded dialog involves learning goal-oriented cooperative dialog between autonomous agents who exchange information about a scene through several rounds of questions and answers. We posit that requiring agents to adhere to rules of human language while also maximizing information exchange is an ill-posed problem, and observe that humans do not stray from a common language, because they are social creatures and have to communicate with many people everyday, and it is far easier to stick to a common language even at the cost of some efficiency loss. Using this as inspiration, we propose and evaluate a multi-agent dialog framework where each agent interacts with, and learns from, multiple agents, and show that this results in more relevant and coherent dialog (as judged by human evaluators) without sacrificing task performance (as judged by quantitative metrics).", "output": [ "Social agents learn to talk to each other in natural language towards a goal" ] }, { "id": "task668-398a06bc5a424ab6b43cb501cd104bd1", "input": "We study the Cross-Entropy Method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant (DCEM) that enables us to differentiate the output of CEM with respect to the objective function's parameters. In the machine learning setting this brings CEM inside of the end-to-end learning pipeline in cases this has otherwise been impossible. We show applications in a synthetic energy-based structured prediction task and in non-convex continuous control. In the control setting we show on the simulated cheetah and walker tasks that we can embed their optimal action sequences with DCEM and then use policy optimization to fine-tune components of the controller as a step towards combining model-based and model-free RL.", "output": [ "DCEM learns latent domains for optimization problems and helps bridge the gap between model-based and model-free RL --- we create a differentiable controller and fine-tune parts of it with PPO" ] }, { "id": "task668-047d9b0c2123448eb288dd8af41913ef", "input": "Image segmentation aims at grouping pixels that belong to the same object or region. At the heart of image segmentation lies the problem of determining whether a pixel is inside or outside a region, which we denote as the \"insideness\" problem. Many Deep Neural Networks (DNNs) variants excel in segmentation benchmarks, but regarding insideness, they have not been well visualized or understood: What representations do DNNs use to address the long-range relationships of insideness? How do architectural choices affect the learning of these representations? In this paper, we take the reductionist approach by analyzing DNNs solving the insideness problem in isolation, i.e. determining the inside of closed (Jordan) curves. We demonstrate analytically that state-of-the-art feed-forward and recurrent architectures can implement solutions of the insideness problem for any given curve. Yet, only recurrent networks could learn these general solutions when the training enforced a specific \"routine\" capable of breaking down the long-range relationships. Our results highlights the need for new training strategies that decompose the learning into appropriate stages, and that lead to the general class of solutions necessary for DNNs to understand insideness.", "output": [ "DNNs for image segmentation can implement solutions for the insideness problem but only some recurrent nets could learn them with a specific type of supervision.", "This paper introduces insideness to study semantic segmentation in deep learning era, and the results can help models generalize better." ] }, { "id": "task668-4fece803545341c38200f923050641a3", "input": "Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution. In this work, we propose a new type of prior distributions for convolutional neural networks, deep weight prior (DWP), that exploit generative models to encourage a specific structure of trained convolutional filters e.g., spatial correlations of weights. We define DWP in the form of an implicit distribution and propose a method for variational inference with such type of implicit priors. In experiments, we show that DWP improves the performance of Bayesian neural networks when training data are limited, and initialization of weights with samples from DWP accelerates training of conventional convolutional neural networks.\n", "output": [ "The generative model for kernels of convolutional neural networks, that acts as a prior distribution while training on new datasets.", "A method for modeling convolutional neural networks using a Bayes method.", "Proposes the 'deep weight prior': the idea is to elicit a prior on an auxilary dataset and then use that prior over the CNN filters to jump start inference for a data set of interest.", "This paper explores learning informative priors for convolutional neural network models with similar problem domains by using autoencoders to obtain an expressive prior on the filtered weights of the trained networks." ] }, { "id": "task668-cb253f0c68d749ee8d519684866e71e7", "input": "Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure. Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently. Notably, models with multi-head attention such as Transformer have demonstrated extreme effectiveness in capturing long-term dependencies in a variety of sequence modeling tasks. Despite their success, however, these models lack necessary components to model local structures in sequences and heavily rely on position embeddings that have limited effects and require a considerable amount of design efforts. In this paper, we propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks. The proposed model can effectively capture both local structures and global long-term dependencies in sequences without any use of position embeddings. We evaluate R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks.", "output": [ "This paper proposes an effective generic sequence model which leverages the strengths of both RNNs and Multi-head attention." ] }, { "id": "task668-bc55dc8f976a48a4bed6882600d943c9", "input": "The importance-weighted autoencoder (IWAE) approach of Burda et al. defines a sequence of increasingly tighter bounds on the marginal likelihood of latent variable models. Recently, Cremer et al. reinterpreted the IWAE bounds as ordinary variational evidence lower bounds (ELBO) applied to increasingly accurate variational distributions. In this work, we provide yet another perspective on the IWAE bounds. We interpret each IWAE bound as a biased estimator of the true marginal likelihood where for the bound defined on $K$ samples we show the bias to be of order O(1/K). In our theoretical analysis of the IWAE objective we derive asymptotic bias and variance expressions. Based on this analysis we develop jackknife variational inference (JVI),\n a family of bias-reduced estimators reducing the bias to $O(K^{-(m+1)})$ for any given m < K while retaining computational efficiency. Finally, we demonstrate that JVI leads to improved evidence estimates in variational autoencoders. We also report first results on applying JVI to learning variational autoencoders.\n\n Our implementation is available at https://github.com/Microsoft/jackknife-variational-inference", "output": [ "Variational inference is biased, let's debias it.", "Introduces jackknife variational inference, a method for debiasing Monte Carlo objectives such as the importance weighted auto-encoder.", "The authors analyze the bias and variance of the IWAE bound and derive a jacknife approach to estimate moments as a way to debias IWAE for finite importance weighted samples." ] }, { "id": "task668-2859817f2a4f459696f26c838459832b", "input": "Convolutional Neural Networks (CNN) are very popular in many fields including computer vision, speech recognition, natural language processing, to name a few. Though deep learning leads to groundbreaking performance in these domains, the networks used are very demanding computationally and are far from real-time even on a GPU, which is not power efficient and therefore does not suit low power systems such as mobile devices. To overcome this challenge, some solutions have been proposed for quantizing the weights and activations of these networks, which accelerate the runtime significantly. Yet, this acceleration comes at the cost of a larger error. The NICE method proposed in this work trains quantized neural networks by noise injection and a learned clamping, which improve the accuracy. This leads to state-of-the-art results on various regression and classification tasks, e.g., ImageNet classification with architectures such as ResNet-18/34/50 with low as 3-bit weights and 3 -bit activations. We implement the proposed solution on an FPGA to demonstrate its applicability for low power real-time applications.", "output": [ "Combine noise injection, gradual quantization and activation clamping learning to achieve state-of-the-art 3,4 and 5 bit quantization", "Proposes to inject noise during training and clamp parameter values in a layer as well as activation output in neural network quantization.", "A method for quantization of deep neural networks for classification and regression, using noise injection, clamping with learned maximum activations, and gradual block quantization to perform on-par or better than state-of-the-art methods." ] }, { "id": "task668-4565dc39e855464daf95d5ea0a64f69c", "input": "Neural Architecture Search (NAS) is an exciting new field which promises to be as much as a game-changer as Convolutional Neural Networks were in 2012. Despite many great works leading to substantial improvements on a variety of tasks, comparison between different methods is still very much an open issue. While most algorithms are tested on the same datasets, there is no shared experimental protocol followed by all. As such, and due to the under-use of ablation studies, there is a lack of clarity regarding why certain methods are more effective than others. Our first contribution is a benchmark of 8 NAS methods on 5 datasets. To overcome the hurdle of comparing methods with different search spaces, we propose using a method’s relative improvement over the randomly sampled average architecture, which effectively removes advantages arising from expertly engineered search spaces or training protocols. Surprisingly, we find that many NAS techniques struggle to significantly beat the average architecture baseline. We perform further experiments with the commonly used DARTS search space in order to understand the contribution of each component in the NAS pipeline. These experiments highlight that: (i) the use of tricks in the evaluation protocol has a predominant impact on the reported performance of architectures; (ii) the cell-based search space has a very narrow accuracy range, such that the seed has a considerable impact on architecture rankings; (iii) the hand-designed macrostructure (cells) is more important than the searched micro-structure (operations); and (iv) the depth-gap is a real phenomenon, evidenced by the change in rankings between 8 and 20 cell architectures. To conclude, we suggest best practices, that we hope will prove useful for the community and help mitigate current NAS pitfalls, e.g. difficulties in reproducibility and comparison of search methods. The\n code used is available at https://github.com/antoyang/NAS-Benchmark.", "output": [ "A study of how different components in the NAS pipeline contribute to the final accuracy. Also, a benchmark of 8 methods on 5 datasets." ] }, { "id": "task668-855f5f8cc58c434d9187ee1ce4011c9e", "input": "Solving tasks in Reinforcement Learning is no easy feat. As the goal of the agent is to maximize the accumulated reward, it often learns to exploit loopholes and misspecifications in the reward signal resulting in unwanted behavior. While constraints may solve this issue, there is no closed form solution for general constraints. In this work we present a novel multi-timescale approach for constrained policy optimization, called `Reward Constrained Policy Optimization' (RCPO), which uses an alternative penalty signal to guide the policy towards a constraint satisfying one. We prove the convergence of our approach and provide empirical evidence of its ability to train constraint satisfying policies.", "output": [ "For complex constraints in which it is not easy to estimate the gradient, we use the discounted penalty as a guiding signal. We prove that under certain assumptions it converges to a feasible solution." ] }, { "id": "task668-a370592584fa46f8ad45e4520e80e144", "input": "State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high-resolution facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and struggle to deal with facial images that exhibit large pose variation and misalignment. In this paper, we propose a novel face super-resolution method that explicitly incorporates 3D facial priors which grasp the sharp facial structures. Firstly, the 3D face rendering branch is set up to obtain 3D priors of salient facial structures and identity knowledge. Secondly, the Spatial Attention Mechanism is used to better exploit this hierarchical information (i.e. intensity similarity, 3D facial structure, identity content) for the super-resolution problem. Extensive experiments demonstrate that the proposed algorithm achieves superior face super-resolution results and outperforms the state-of-the-art.", "output": [ "We propose a novel face super resolution method that explicitly incorporates 3D facial priors which grasp the sharp facial structures." ] }, { "id": "task668-73109f4375b24bee8c0111cb8b0f04fa", "input": "We were approached by a group of healthcare providers who are involved in the care of chronic patients looking for potential technologies to facilitate the process of reviewing patient-generated data during clinical visits. Aiming at understanding the healthcare providers' attitudes towards reviewing patient-generated data, we (1) conducted a focus group with a mixed group of healthcare providers. Next, to gain the patients' perspectives, we (2) interviewed eight chronic patients, collected a sample of their data and designed a series of visualizations representing patient data we collected. Last, we (3) sought feedback on the visualization designs from healthcare providers who requested this exploration. We found four factors shaping patient-generated data: data & context, patient's motivation, patient's time commitment, and patient's support circle. Informed by the results of our studies, we discussed the importance of designing patient-generated visualizations for individuals by considering both patient and healthcare provider rather than designing with the purpose of generalization and provided guidelines for designing future patient-generated data visualizations.", "output": [ "We explored the visualization designs that can support chronic patients to present and review their health data with healthcare providers during clinical visits." ] }, { "id": "task668-c76b44d03a6b4e6abdc379ed2972f7ed", "input": "We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks.\n We model the order stream as a stochastic process with finite history dependence, and employ a conditional Wasserstein GAN to capture history dependence of orders in a stock market. \n We test our approach with actual market and synthetic data on a number of different statistics, and find the generated data to be close to real data.", "output": [ "We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks." ] }, { "id": "task668-3577ec7fd8974823801852472690932e", "input": "This paper puts forward a new text to tensor representation that relies on information compression techniques to assign shorter codes to the most frequently used characters. This representation is language-independent with no need of pretraining and produces an encoding with no information loss. It provides an adequate description of the morphology of text, as it is able to represent prefixes, declensions, and inflections with similar vectors and are able to represent even unseen words on the training dataset. Similarly, as it is compact yet sparse, is ideal for speed up training times using tensor processing libraries. As part of this paper, we show that this technique is especially effective when coupled with convolutional neural networks (CNNs) for text classification at character-level. We apply two variants of CNN coupled with it. Experimental results show that it drastically reduces the number of parameters to be optimized, resulting in competitive classification accuracy values in only a fraction of the time spent by one-hot encoding representations, thus enabling training in commodity hardware.", "output": [ "Using Compressing tecniques to Encoding of Words is a possibility for faster training of CNN and dimensionality reduction of representation" ] }, { "id": "task668-81b596ca9a8f4e8f82813cf88ada07c6", "input": "The variational autoencoder (VAE) is a popular combination of deep latent variable model and accompanying variational learning technique. By using a neural inference network to approximate the model's posterior on latent variables, VAEs efficiently parameterize a lower bound on marginal data likelihood that can be optimized directly via gradient methods. In practice, however, VAE training often results in a degenerate local optimum known as \"posterior collapse\" where the model learns to ignore the latent variable and the approximate posterior mimics the prior. In this paper, we investigate posterior collapse from the perspective of training dynamics. We find that during the initial stages of training the inference network fails to approximate the model's true posterior, which is a moving target. As a result, the model is encouraged to ignore the latent encoding and posterior collapse occurs. Based on this observation, we propose an extremely simple modification to VAE training to reduce inference lag: depending on the model's current mutual information between latent variable and observation, we aggressively optimize the inference network before performing each model update. Despite introducing neither new model components nor significant complexity over basic VAE, our approach is able to avoid the problem of collapse that has plagued a large amount of previous work. Empirically, our approach outperforms strong autoregressive baselines on text and image benchmarks in terms of held-out likelihood, and is competitive with more complex techniques for avoiding collapse while being substantially faster.", "output": [ "To address posterior collapse in VAEs, we propose a novel yet simple training procedure that aggressively optimizes inference network with more updates. This new training procedure mitigates posterior collapse and leads to a better VAE model. ", "Looks into the phenomenon of posterior collapse, showing that increased training of the inference network can reduce the problem and lead to better optima.", "Authors propose changing the training procedure of VAEs only as a solution to posterior collapse, leaving the model and objective untouched." ] }, { "id": "task668-2eb03587ab1f4d85a31651cef6622438", "input": "Understanding how people represent categories is a core problem in cognitive science, with the flexibility of human learning remaining a gold standard to which modern artificial intelligence and machine learning aspire. Decades of psychological research have yielded a variety of formal theories of categories, yet validating these theories with naturalistic stimuli remains a challenge. The problem is that human category representations cannot be directly observed and running informative experiments with naturalistic stimuli such as images requires having a workable representation of these stimuli. Deep neural networks have recently been successful in a range of computer vision tasks and provide a way to represent the features of images. In this paper, we introduce a method for estimating the structure of human categories that draws on ideas from both cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep representation learners. We provide qualitative and quantitative results as a proof of concept for the feasibility of the method. Samples drawn from human distributions rival the quality of current state-of-the-art generative models and outperform alternative methods for estimating the structure of human categories.", "output": [ "using deep neural networks and clever algorithms to capture human mental visual concepts" ] }, { "id": "task668-94ccfcadb80949c7b87dbbbaf02cd1cc", "input": "Transfer learning through fine-tuning a pre-trained neural network with an extremely large dataset, such as ImageNet, can significantly accelerate training while the accuracy is frequently bottlenecked by the limited dataset size of the new target task. To solve the problem, some regularization methods, constraining the outer layer weights of the target network using the starting point as references (SPAR), have been studied. In this paper, we propose a novel regularized transfer learning framework DELTA, namely DEep Learning Transfer using Feature Map with Attention. Instead of constraining the weights of neural network, DELTA aims to preserve the outer layer outputs of the target network. Specifically, in addition to minimizing the empirical loss, DELTA intends to align the outer layer outputs of two networks, through constraining a subset of feature maps that are precisely selected by attention that has been learned in an supervised learning manner. We evaluate DELTA with the state-of-the-art algorithms, including L2 and L2-SP. The experiment results show that our proposed method outperforms these baselines with higher accuracy for new tasks.", "output": [ "improving deep transfer learning with regularization using attention based feature maps" ] }, { "id": "task668-e084b6f20a704c239da0bac68cb16a52", "input": "Generative modeling of high dimensional data like images is a notoriously difficult and ill-defined problem. In particular, how to evaluate a learned generative model is unclear.\n In this paper, we argue that *adversarial learning*, pioneered with generative adversarial networks (GANs), provides an interesting framework to implicitly define more meaningful task losses for unsupervised tasks, such as for generating \"visually realistic\" images. By relating GANs and structured prediction under the framework of statistical decision theory, we put into light links between recent advances in structured prediction theory and the choice of the divergence in GANs. We argue that the insights about the notions of \"hard\" and \"easy\" to learn losses can be analogously extended to adversarial divergences. We also discuss the attractive properties of parametric adversarial divergences for generative modeling, and perform experiments to show the importance of choosing a divergence that reflects the final task.", "output": [ "Parametric adversarial divergences implicitly define more meaningful task losses for generative modeling, we make parallels with structured prediction to study the properties of these divergences and their ability to encode the task of interest." ] }, { "id": "task668-c877988560e346acbe6aeb04f48f3c99", "input": "We present a 3D capsule architecture for processing of point clouds that is equivariant with respect to the SO(3) rotation group, translation and permutation of the unordered input sets. The network operates on a sparse set of local reference frames, computed from an input point cloud and establishes end-to-end equivariance through a novel 3D quaternion group capsule layer, including an equivariant dynamic routing procedure. The capsule layer enables us to disentangle geometry from pose, paving the way for more informative descriptions and a structured latent space. In the process, we theoretically connect the process of dynamic routing between capsules to the well-known Weiszfeld algorithm, a scheme for solving iterative re-weighted least squares (IRLS) problems with provable convergence properties, enabling robust pose estimation between capsule layers. Due to the sparse equivariant quaternion capsules, our architecture allows joint object classification and orientation estimation, which we validate empirically on common benchmark datasets. \n\n", "output": [ "Deep architectures for 3D point clouds that are equivariant to SO(3) rotations, as well as translations and permutations. " ] }, { "id": "task668-f93304b1cbf947efbea4da95a8b281de", "input": "Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn strong supervised models like convolutional neural networks. However, these models trained on one data domain may not generalize well to other domains unequipped with annotations for model finetuning. To avoid the labor-intensive process of annotation, we develop a domain adaptation method to adapt the source data to the unlabeled target domain. To this end, we propose to learn discriminative feature representations of patches based on label histograms in the source domain, through the construction of a disentangled space. With such representations as guidance, we then use an adversarial learning scheme to push the feature representations in target patches to the closer distributions in source ones. In addition, we show that our framework can integrate a global alignment process with the proposed patch-level alignment and achieve state-of-the-art performance on semantic segmentation. Extensive ablation studies and experiments are conducted on numerous benchmark datasets with various settings, such as synthetic-to-real and cross-city scenarios.", "output": [ "A domain adaptation method for structured output via learning patch-level discriminative feature representations" ] }, { "id": "task668-e7c8c0c5e4344adebb149d16a58e7db3", "input": "The soundness and optimality of a plan depends on the correctness of the domain model. In real-world applications, specifying complete domain models is difficult as the interactions between the agent and its environment can be quite complex. We propose a framework to learn a PPDDL representation of the model incrementally over multiple planning problems using only experiences from the current planning problem, which suits non-stationary environments. We introduce the novel concept of reliability as an intrinsic motivation for reinforcement learning, and as a means of learning from failure to prevent repeated instances of similar failures. Our motivation is to improve both learning efficiency and goal-directedness. We evaluate our work with experimental results for three planning domains.", "output": [ "Introduce an approach to allow agents to learn PPDDL action models incrementally over multiple planning problems under the framework of reinforcement learning." ] }, { "id": "task668-918435350aeb4d1e8740909571155be0", "input": "Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity of the environment and the language scales. To overcome this limitation, we present a framework within which instruction-conditional RL agents are trained using rewards obtained not from the environment, but from reward models which are jointly trained from expert examples. As reward models improve, they learn to accurately reward agents for completing tasks for environment configurations---and for instructions---not present amongst the expert data. This framework effectively separates the representation of what instructions require from how they can be executed.\n In a simple grid world, it enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements. We further show the method allows our agent to adapt to changes in the environment without requiring new expert examples.", "output": [ "We propose AGILE, a framework for training agents to perform instructions from examples of respective goal-states." ] }, { "id": "task668-33a5b9c9219c4cd9ab790cb186b1247e", "input": "State-of-the-art Unsupervised Domain Adaptation (UDA) methods learn transferable features by minimizing the feature distribution discrepancy between the source and target domains. Different from these methods which do not model the feature distributions explicitly, in this paper, we explore explicit feature distribution modeling for UDA. In particular, we propose Distribution Matching Prototypical Network (DMPN) to model the deep features from each domain as Gaussian mixture distributions. With explicit feature distribution modeling, we can easily measure the discrepancy between the two domains. In DMPN, we propose two new domain discrepancy losses with probabilistic interpretations. The first one minimizes the distances between the corresponding Gaussian component means of the source and target data. The second one minimizes the pseudo negative log likelihood of generating the target features from source feature distribution. To learn both discriminative and domain invariant features, DMPN is trained by minimizing the classification loss on the labeled source data and the domain discrepancy losses together. Extensive experiments are conducted over two UDA tasks. Our approach yields a large margin in the Digits Image transfer task over state-of-the-art approaches. More remarkably, DMPN obtains a mean accuracy of 81.4% on VisDA 2017 dataset. The hyper-parameter sensitivity analysis shows that our approach is robust w.r.t hyper-parameter changes.", "output": [ "We propose to explicitly model deep feature distributions of source and target data as Gaussian mixture distributions for Unsupervised Domain Adaptation (UDA) and achieve superior results in multiple UDA tasks than state-of-the-art methods." ] }, { "id": "task668-a54d43bfbc27465ea0e7a68db774f16d", "input": "Despite its empirical success, the theoretical underpinnings of the stability, convergence and acceleration properties of batch normalization (BN) remain elusive. In this paper, we attack this problem from a modelling approach, where we perform thorough theoretical analysis on BN applied to simplified model: ordinary least squares (OLS). We discover that gradient descent on OLS with BN has interesting properties, including a scaling law, convergence for arbitrary learning rates for the weights, asymptotic acceleration effects, as well as insensitivity to choice of learning rates. We then demonstrate numerically that these findings are not specific to the OLS problem and hold qualitatively for more complex supervised learning problems. This points to a new direction towards uncovering the mathematical principles that underlies batch normalization.", "output": [ "We mathematically analyze the effect of batch normalization on a simple model and obtain key new insights that applies to general supervised learning." ] }, { "id": "task668-eaaae9c589ce4ce79c2d2b150bc96b45", "input": "We develop a metalearning approach for learning hierarchically structured poli- cies, improving sample efficiency on unseen tasks through the use of shared primitives—policies that are executed for large numbers of timesteps. Specifi- cally, a set of primitives are shared within a distribution of tasks, and are switched between by task-specific policies. We provide a concrete metric for measuring the strength of such hierarchies, leading to an optimization problem for quickly reaching high reward on unseen tasks. We then present an algorithm to solve this problem end-to-end through the use of any off-the-shelf reinforcement learning method, by repeatedly sampling new tasks and resetting task-specific policies. We successfully discover meaningful motor primitives for the directional movement of four-legged robots, solely by interacting with distributions of mazes. We also demonstrate the transferability of primitives to solve long-timescale sparse-reward obstacle courses, and we enable 3D humanoid robots to robustly walk and crawl with the same policy.", "output": [ "learn hierarchal sub-policies through end-to-end training over a distribution of tasks", "The authors consider the problem of learning a useful set of ‘sub policies’ that can be shared between tasks so as to jump start learning on new tasks drawn from the task distribution. ", "This paper proposes a novel method for inducing temporal hierarchical structure in a specialized multi-task setting." ] }, { "id": "task668-cf068e7b9b00444d864d46e055bad017", "input": "Deep generative models such as Variational AutoEncoder (VAE) and Generative Adversarial Network (GAN) play an increasingly important role in machine learning and computer vision. However, there are two fundamental issues hindering their real-world applications: the difficulty of conducting variational inference in VAE and the functional absence of encoding real-world samples in GAN. In this paper, we propose a novel algorithm named Latently Invertible Autoencoder (LIA) to address the above two issues in one framework. An invertible network and its inverse mapping are symmetrically embedded in the latent space of VAE. Thus the partial encoder first transforms the input into feature vectors and then the distribution of these feature vectors is reshaped to fit a prior by the invertible network. The decoder proceeds in the reverse order of the encoder's composite mappings. A two-stage stochasticity-free training scheme is designed to train LIA via adversarial learning, in the sense that the decoder of LIA is first trained as a standard GAN with the invertible network and then the partial encoder is learned from an autoencoder by detaching the invertible network from LIA. Experiments conducted on the FFHQ face dataset and three LSUN datasets validate the effectiveness of LIA for inference and generation.", "output": [ "A new model Latently Invertible Autoencoder is proposed to solve the problem of variational inference in VAE using the invertible network and two-stage adversarial training." ] }, { "id": "task668-88cf01b19fb94c32877ad890d7259453", "input": "Building robust online content recommendation systems requires learning com- plex interactions between user preferences and content features. The field has evolved rapidly in recent years from traditional multi-arm bandit and collabora- tive filtering techniques, with new methods integrating Deep Learning models that enable to capture non-linear feature interactions. Despite progress, the dynamic nature of online recommendations still poses great challenges, such as finding the delicate balance between exploration and exploitation. In this paper we provide a novel method, Deep Density Networks (DDN) which deconvolves measurement and data uncertainty and predicts probability densities of CTR, enabling us to perform more efficient exploration of the feature space. We show the usefulness of using DDN online in a real world content recommendation system that serves billions of recommendations per day, and present online and offline results to eval- uate the benefit of using DDN.", "output": [ "We have introduced Deep Density Network, a unified DNN model to estimate uncertainty for exploration/exploitation in recommendation systems." ] }, { "id": "task668-414f63c0c37a4c14bb7197727b7ac4ae", "input": "Using Recurrent Neural Networks (RNNs) in sequence modeling tasks is promising in delivering high-quality results but challenging to meet stringent latency requirements because of the memory-bound execution pattern of RNNs. We propose a big-little dual-module inference to dynamically skip unnecessary memory access and computation to speedup RNN inference. Leveraging the error-resilient feature of nonlinear activation functions used in RNNs, we propose to use a lightweight little module that approximates the original RNN layer, which is referred to as the big module, to compute activations of the insensitive region that are more error-resilient. The expensive memory access and computation of the big module can be reduced as the results are only used in the sensitive region. Our method can reduce the overall memory access by 40% on average and achieve 1.54x to 1.75x speedup on CPU-based server platform with negligible impact on model quality.", "output": [ "We accelerate RNN inference by dynamically reducing redundant memory access using a mixture of accurate and approximate modules." ] }, { "id": "task668-2c8326f528e444178aa32cf31b8c44eb", "input": "Convolutional Neural Networks (CNNs) significantly improve the state-of-the-art for many applications, especially in computer vision. However, CNNs still suffer from a tendency to confidently classify out-distribution samples from unknown classes into pre-defined known classes. Further, they are also vulnerable to adversarial examples. We are relating these two issues through the tendency of CNNs to over-generalize for areas of the input space not covered well by the training set. We show that a CNN augmented with an extra output class can act as a simple yet effective end-to-end model for controlling over-generalization. As an appropriate training set for the extra class, we introduce two resources that are computationally efficient to obtain: a representative natural out-distribution set and interpolated in-distribution samples. To help select a representative natural out-distribution set among available ones, we propose a simple measurement to assess an out-distribution set's fitness. We also demonstrate that training such an augmented CNN with representative out-distribution natural datasets and some interpolated samples allows it to better handle a wide range of unseen out-distribution samples and black-box adversarial examples without training it on any adversaries. Finally, we show that generation of white-box adversarial attacks using our proposed augmented CNN can become harder, as the attack algorithms have to get around the rejection regions when generating actual adversaries.", "output": [ "Properly training CNNs with dustbin class increase their robustness to adversarial attacks and their capacity to deal with out-distribution samples.", "This paper proposes adding an additional label for detecting OOD samples and adversarial examples in CNN models.", "The paper proposes an additional class that incorporates natural out-distribution images and interpolated images for adversarial and out-distribution samples in CNNs" ] }, { "id": "task668-9601039d9ba9401592b90673988fd537", "input": " A collection of scientific papers is often accompanied by tags:\n keywords, topics, concepts etc., associated with each paper.\n Sometimes these tags are human-generated, sometimes they are\n machine-generated. We propose a simple measure of the consistency\n of the tagging of scientific papers: whether these tags are\n predictive for the citation graph links. Since the authors tend to\n cite papers about the topics close to those of their publications, a\n consistent tagging system could predict citations. We present an\n algorithm to calculate consistency, and experiments with human- and\n machine-generated tags. We show that augmentation, i.e. the combination\n of the manual tags with the machine-generated ones, can enhance the\n consistency of the tags. We further introduce cross-consistency,\n the ability to predict citation links between papers tagged by\n different taggers, e.g. manually and by a machine.\n Cross-consistency can be used to evaluate the tagging quality when\n the amount of labeled data is limited.", "output": [ "A good tagger gives similar tags to a given paper and the papers it cites" ] }, { "id": "task668-2e9ed6734afd47758e51f3afd42b90de", "input": "This paper presents a system for immersive visualization of Non-Euclidean spaces using real-time ray tracing. It exploits the capabilities of the new generation of GPU’s based on the NVIDIA’s Turing architecture in order to develop new methods for intuitive exploration of landscapes featuring non-trivial geometry and topology in virtual reality.", "output": [ "Immersive Visualization of the Classical Non-Euclidean Spaces using Real-Time Ray Tracing." ] }, { "id": "task668-ba87d43977d94fd9a0c37bb7bf6a0062", "input": "We identify a phenomenon, which we refer to as *multi-model forgetting*, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, due to the overwriting of shared parameters. To overcome this, we introduce a statistically-justified weight plasticity loss that regularizes the learning of a model's shared parameters according to their importance for the previous models, and demonstrate its effectiveness when training two models sequentially and for neural architecture search. Adding weight plasticity in neural architecture search preserves the best models to the end of the search and yields improved results in both natural language processing and computer vision tasks.", "output": [ "We identify a phenomenon, neural brainwashing, and introduce a statistically-justified weight plasticity loss to overcome this.", "This paper discusses the phenomena of “neural brainwashing”, which refers to that the performance of one model is affected via another model sharing model parameters." ] }, { "id": "task668-de07b4399cdd4c57a03ab1d9432389c9", "input": "Few-shot learning is the process of learning novel classes using only a few examples and it remains a challenging task in machine learning. Many sophisticated few-shot learning algorithms have been proposed based on the notion that networks can easily overfit to novel examples if they are simply fine-tuned using only a few examples. In this study, we show that in the commonly used low-resolution mini-ImageNet dataset, the fine-tuning method achieves higher accuracy than common few-shot learning algorithms in the 1-shot task and nearly the same accuracy as that of the state-of-the-art algorithm in the 5-shot task. We then evaluate our method with more practical tasks, namely the high-resolution single-domain and cross-domain tasks. With both tasks, we show that our method achieves higher accuracy than common few-shot learning algorithms. We further analyze the experimental results and show that: 1) the retraining process can be stabilized by employing a low learning rate, 2) using adaptive gradient optimizers during fine-tuning can increase test accuracy, and 3) test accuracy can be improved by updating the entire network when a large domain-shift exists between base and novel classes.", "output": [ "An empirical study that provides a novel perspective on few-shot learning, in which a fine-tuning method shows comparable accuracy to more complex state-of-the-art methods in several classification tasks." ] }, { "id": "task668-02c1ae3d2099490eb07682d2122dc74b", "input": "Can the success of reinforcement learning methods for simple combinatorial optimization problems be extended to multi-robot sequential assignment planning? In addition to the challenge of achieving near-optimal performance in large problems, transferability to an unseen number of robots and tasks is another key challenge for real-world applications. In this paper, we suggest a method that achieves the first success in both challenges for robot/machine scheduling problems.\n \n Our method comprises of three components. First, we show any robot scheduling problem can be expressed as a random probabilistic graphical model (PGM). We develop a mean-field inference method for random PGM and use it for Q-function inference. Second, we show that transferability can be achieved by carefully designing two-step sequential encoding of problem state. Third, we resolve the computational scalability issue of fitted Q-iteration by suggesting a heuristic auction-based Q-iteration fitting method enabled by transferability we achieved.\n \n We apply our method to discrete-time, discrete space problems (Multi-Robot Reward Collection (MRRC)) and scalably achieve 97% optimality with transferability. This optimality is maintained under stochastic contexts. By extending our method to continuous time, continuous space formulation, we claim to be the first learning-based method with scalable performance in any type of multi-machine scheduling problems; our method scalability achieves comparable performance to popular metaheuristics in Identical parallel machine scheduling (IPMS) problems.", "output": [ "RL can solve (stochastic) multi-robot/scheduling problems scalably and transferably using graph embedding" ] }, { "id": "task668-45ad35231eb948b5a449a1407accde96", "input": "To flexibly and efficiently reason about temporal sequences, abstract representations that compactly represent the important information in the sequence are needed. One way of constructing such representations is by focusing on the important events in a sequence. In this paper, we propose a model that learns both to discover such key events (or keyframes) as well as to represent the sequence in terms of them. We do so using a hierarchical Keyframe-Inpainter (KeyIn) model that first generates keyframes and their temporal placement and then inpaints the sequences between keyframes. We propose a fully differentiable formulation for efficiently learning the keyframe placement. We show that KeyIn finds informative keyframes in several datasets with diverse dynamics. When evaluated on a planning task, KeyIn outperforms other recent proposals for learning hierarchical representations.", "output": [ "We propose a model that learns to discover informative frames in a future video sequence and represent the video via its keyframes." ] }, { "id": "task668-49178d62628b417e8883feca7fd7904a", "input": "We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. The proposed mechanism reuses CNN feature activations to find the most informative parts of the image at different depths with the help of gating mechanisms and without part annotations. Thus, it can be used to augment any layer of a CNN to extract low- and high-level local information to be more discriminative. \n\n Differently, from other approaches, the mechanism we propose just needs a single pass through the input and it can be trained end-to-end through SGD. As a consequence, the proposed mechanism is modular, architecture-independent, easy to implement, and faster than iterative approaches.\n\n Experiments show that, when augmented with our approach, Wide Residual Networks systematically achieve superior performance on each of five different fine-grained recognition datasets: the Adience age and gender recognition benchmark, Caltech-UCSD Birds-200-2011, Stanford Dogs, Stanford Cars, and UEC Food-100, obtaining competitive and state-of-the-art scores.", "output": [ "We enhance CNNs with a novel attention mechanism for fine-grained recognition. Superior performance is obtained on 5 datasets.", "Describes a novel attentional mechanism applid to fine-grained recognition that consistently improves the recognition accuracy of the baseline", "This paper proposes a feed-forward attention mechanism for fine-grained image classification", "This paper presents an interesting attention mechanism for fine-grained image classification." ] }, { "id": "task668-7ab1ef48fe204897868465fde0fdea53", "input": "Solving long-horizon sequential decision making tasks in environments with sparse rewards is a longstanding problem in reinforcement learning (RL) research. Hierarchical Reinforcement Learning (HRL) has held the promise to enhance the capabilities of RL agents via operation on different levels of temporal abstraction. Despite the success of recent works in dealing with inherent nonstationarity and sample complexity, it remains difficult to generalize to unseen environments and to transfer different layers of the policy to other agents. In this paper, we propose a novel HRL architecture, Hierarchical Decompositional Reinforcement Learning (HiDe), which allows decomposition of the hierarchical layers into independent subtasks, yet allows for joint training of all layers in end-to-end manner. The main insight is to combine a control policy on a lower level with an image-based planning policy on a higher level. We evaluate our method on various complex continuous control tasks for navigation, demonstrating that generalization across environments and transfer of higher level policies can be achieved. See videos https://sites.google.com/view/hide-rl", "output": [ "Learning Functionally Decomposed Hierarchies for Continuous Navigation Tasks" ] }, { "id": "task668-c79a9dcd49314ad8b6613f04069580c1", "input": "Deep learning has become a widely used tool in many computational and classification problems. \n Nevertheless obtaining and labeling data, which is needed for strong results, is often expensive or even not possible. \n In this paper three different algorithmic approaches to deal with limited access to data are evaluated and compared to each other. \n We show the drawbacks and benefits of each method. \n One successful approach, especially in one- or few-shot learning tasks, is the use of external data during the classification task. \n Another successful approach, which achieves state of the art results in semi-supervised learning (SSL) benchmarks, is consistency regularization.\n Especially virtual adversarial training (VAT) has shown strong results and will be investigated in this paper. \n The aim of consistency regularization is to force the network not to change the output, when the input or the network itself is perturbed.\n Generative adversarial networks (GANs) have also shown strong empirical results. \n In many approaches the GAN architecture is used in order to create additional data and therefor to increase the generalization capability of the classification network.\n Furthermore we consider the use of unlabeled data for further performance improvement. \n The use of unlabeled data is investigated both for GANs and VAT. \n", "output": [ "Comparison of siamese neural networks, GANs, and VAT for few shot learning. " ] }, { "id": "task668-50340aa81e8441d9a133daaf1a8f96b4", "input": "There has been a large amount of interest, both in the past and particularly recently, into the relative advantage of different families of universal function approximators, for instance neural networks, polynomials, rational functions, etc. However, current research has focused almost exclusively on understanding this problem in a worst case setting: e.g. characterizing the best L1 or L_{infty} approximation in a box (or sometimes, even under an adversarially constructed data distribution.) In this setting many classical tools from approximation theory can be effectively used.\n\n However, in typical applications we expect data to be high dimensional, but structured -- so, it would only be important to approximate the desired function well on the relevant part of its domain, e.g. a small manifold on which real input data actually lies. Moreover, even within this domain the desired quality of approximation may not be uniform; for instance in classification problems, the approximation needs to be more accurate near the decision boundary. These issues, to the best of our knowledge, have remain unexplored until now.\n\t\n With this in mind, we analyze the performance of neural networks and polynomial kernels in a natural regression setting where the data enjoys sparse latent structure, and the labels depend in a simple way on the latent variables. We give an almost-tight theoretical analysis of the performance of both neural networks and polynomials for this problem, as well as verify our theory with simulations. Our results both involve new (complex-analytic) techniques, which may be of independent interest, and show substantial qualitative differences with what is known in the worst-case setting.", "output": [ "Beyond-worst-case analysis of the representational power of ReLU nets & polynomial kernels -- in particular in the presence of sparse latent structure." ] }, { "id": "task668-f87cb176463d4cd18910254264710224", "input": "Evolutionary Strategies (ES) are a popular family of black-box zeroth-order optimization algorithms which rely on search distributions to efficiently optimize a large variety of objective functions. This paper investigates the potential benefits of using highly flexible search distributions in ES algorithms, in contrast to standard ones (typically Gaussians). We model such distributions with Generative Neural Networks (GNNs) and introduce a new ES algorithm that leverages their expressiveness to accelerate the stochastic search. Because it acts as a plug-in, our approach allows to augment virtually any standard ES algorithm with flexible search distributions. We demonstrate the empirical advantages of this method on a diversity of objective functions.", "output": [ "We propose a new algorithm leveraging the expressiveness of Generative Neural Networks to improve Evolutionary Strategies algorithms." ] }, { "id": "task668-b73eb4aa16d64f67a62c7f3d5d7959b8", "input": "Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels. In this paper, we claim that such overfitting can be avoided by \"early stopping\" training a deep neural network before the noisy labels are severely memorized. Then, we resume training the early stopped network using a \"maximal safe set,\" which maintains a collection of almost certainly true-labeled samples at each epoch since the early stop point. Putting them all together, our novel two-phase training method, called Prestopping, realizes noise-free training under any type of label noise for practical use. Extensive experiments using four image benchmark data sets verify that our method significantly outperforms four state-of-the-art methods in test error by 0.4–8.2 percent points under existence of real-world noise.", "output": [ "We propose a novel two-phase training approach based on \"early stopping\" for robust training on noisy labels.", "Paper proposes to study how early stopping in optimization helps find confident examples", "This paper proposes a two-phase training method for learning with label noise." ] }, { "id": "task668-885be50a5e4e47e79c18cb0cac5b81de", "input": "System identification is the process of building a mathematical model of an unknown system from measurements of its inputs and outputs. It is a key step for model-based control, estimator design, and output prediction. This work presents an algorithm for non-linear offline system identification from partial observations, i.e. situations in which the system's full-state is not directly observable. The algorithm presented, called SISL, iteratively infers the system's full state through non-linear optimization and then updates the model parameters. We test our algorithm on a simulated system of coupled Lorenz attractors, showing our algorithm's ability to identify high-dimensional systems that prove intractable for particle-based approaches. We also use SISL to identify the dynamics of an aerobatic helicopter. By augmenting the state with unobserved fluid states, we learn a model that predicts the acceleration of the helicopter better than state-of-the-art approaches.", "output": [ "This work presents a scalable algorithm for non-linear offline system identification from partial observations." ] }, { "id": "task668-18cbbdf9b8a0480ab37f7a0c4f6a94d3", "input": "Network Embeddings (NEs) map the nodes of a given network into $d$-dimensional Euclidean space $\\mathbb{R}^d$. Ideally, this mapping is such that 'similar' nodes are mapped onto nearby points, such that the NE can be used for purposes such as link prediction (if 'similar' means being 'more likely to be connected') or classification (if 'similar' means 'being more likely to have the same label'). In recent years various methods for NE have been introduced, all following a similar strategy: defining a notion of similarity between nodes (typically some distance measure within the network), a distance measure in the embedding space, and a loss function that penalizes large distances for similar nodes and small distances for dissimilar nodes.\n\n A difficulty faced by existing methods is that certain networks are fundamentally hard to embed due to their structural properties: (approximate) multipartiteness, certain degree distributions, assortativity, etc. To overcome this, we introduce a conceptual innovation to the NE literature and propose to create \\emph{Conditional Network Embeddings} (CNEs); embeddings that maximally add information with respect to given structural properties (e.g. node degrees, block densities, etc.). We use a simple Bayesian approach to achieve this, and propose a block stochastic gradient descent algorithm for fitting it efficiently.\n\n We demonstrate that CNEs are superior for link prediction and multi-label classification when compared to state-of-the-art methods, and this without adding significant mathematical or computational complexity. Finally, we illustrate the potential of CNE for network visualization.", "output": [ "We introduce a network embedding method that accounts for prior information about the network, yielding superior empirical performance.", "The paper proposed to use a prior distribution to constraint the network embedding, for the formulation this paper used very restricted Gaussian distributions.", "Proposes learning unsupervised node embeddings by considering the structural properties of networks." ] }, { "id": "task668-1716e15eefc9479abba25fa7da0283d2", "input": "Meta-learning is a promising strategy for learning to efficiently learn within new tasks, using data gathered from a distribution of tasks. However, the meta-learning literature thus far has focused on the task segmented setting, where at train-time, offline data is assumed to be split according to the underlying task, and at test-time, the algorithms are optimized to learn in a single task. In this work, we enable the application of generic meta-learning algorithms to settings where this task segmentation is unavailable, such as continual online learning with a time-varying task. We present meta-learning via online changepoint analysis (MOCA), an approach which augments a meta-learning algorithm with a differentiable Bayesian changepoint detection scheme. The framework allows both training and testing directly on time series data without segmenting it into discrete tasks. We demonstrate the utility of this approach on a nonlinear meta-regression benchmark as well as two meta-image-classification benchmarks.", "output": [ "Bayesian changepoint detection enables meta-learning directly from time series data.", "The paper considers the meta-learning in the task un-segmented setting and apply Bayesian online change point detection with meta-learning.", "This paper pushes meta-learning towards task-unsegmented settings, where the MOCA framework adopts a Bayesian changepoint estimation scheme for task change detection." ] }, { "id": "task668-2ee5300675a74dcbbe282ea8a3deac6b", "input": "Variational auto-encoders\n (VAEs) offer a tractable approach when performing approximate inference in otherwise intractable generative models. However, standard VAEs often produce latent codes that are disperse and lack interpretability, thus making the resulting representations unsuitable for auxiliary tasks (e.g. classification) and human interpretation. We address these issues by merging ideas from variational auto-encoders and sparse coding, and propose to explicitly model sparsity in the latent space of a VAE with a Spike and Slab prior distribution. We derive the evidence lower bound using a discrete mixture recognition function thereby making approximate posterior inference as computational efficient as in the standard VAE case. With the new approach, we are able to infer truly sparse representations with generally intractable non-linear probabilistic models. We show that these sparse representations are advantageous over standard VAE representations on two benchmark classification tasks (MNIST and Fashion-MNIST) by demonstrating improved classification accuracy and significantly increased robustness to the number of latent dimensions. Furthermore, we demonstrate qualitatively that the sparse elements capture subjectively understandable sources of variation.", "output": [ "We explore the intersection of VAEs and sparse coding.", "This paper proposes an extension of VAEs with sparse priors and posteriors to learn sparse interpretable representations." ] }, { "id": "task668-f48340175185401e8696bdef89e52b97", "input": "Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models. However, in such visual perception pipeline the detected objects must also be tracked, in a process called Multiple Object Tracking (MOT), to build the moving trajectories of surrounding obstacles. Since MOT is designed to be robust against errors in object detection, it poses a general challenge to existing attack techniques that blindly target objection detection: we find that a success rate of over 98% is needed for them to actually affect the tracking results, a requirement that no existing attack technique can satisfy. In this paper, we are the first to study adversarial machine learning attacks against the complete visual perception pipeline in autonomous driving, and discover a novel attack technique, tracker hijacking, that can effectively fool MOT using AEs on object detection. Using our technique, successful AEs on as few as one single frame can move an existing object in to or out of the headway of an autonomous vehicle to cause potential safety hazards. We perform evaluation using the Berkeley Deep Drive dataset and find that on average when 3 frames are attacked, our attack can have a nearly 100% success rate while attacks that blindly target object detection only have up to 25%.", "output": [ "We study the adversarial machine learning attacks against the Multiple Object Tracking mechanisms for the first time. " ] }, { "id": "task668-b9c5d060e11b41faae26c66dadb90067", "input": "Transferring knowledge across tasks to improve data-efficiency is one of\n the open key challenges in the area of global optimization algorithms. Readily\n available algorithms are typically designed to be universal optimizers and, thus,\n often suboptimal for specific tasks. We propose a novel transfer learning method to\n obtain customized optimizers within the well-established framework of Bayesian\n optimization, allowing our algorithm to utilize the proven generalization\n capabilities of Gaussian processes. Using reinforcement learning to meta-train an\n acquisition function (AF) on a set of related tasks, the proposed method learns to\n extract implicit structural information and to exploit it for improved data-efficiency.\n We present experiments on a sim-to-real transfer task as well as on several simulated\n functions and two hyperparameter search problems. The results show that our\n algorithm (1) automatically identifies structural properties of objective functions\n from available source tasks or simulations, (2) performs favourably in settings with\n both scarse and abundant source data, and (3) falls back to the performance level\n of general AFs if no structure is present.", "output": [ "We perform efficient and flexible transfer learning in the framework of Bayesian optimization through meta-learned neural acquisition functions.", "The authors present MetaBO which uses reinforcement learning to meta-learn the acquisition function for Bayesian Optimization, showing increasing sample efficiency on new tasks.", "The authors propose a meta-learning based alternative to standard acquisition functions (AFs), whereby a pretrained neural network outputs acquisition values as a function of hand-chosen features." ] }, { "id": "task668-01de162bc94f45d5b9ea3236d0332ccd", "input": "The point estimates of ReLU classification networks, arguably the most widely used neural network architecture, have recently been shown to have arbitrarily high confidence far away from the training data. This architecture is thus not robust, e.g., against out-of-distribution data. Approximate Bayesian posteriors on the weight space have been empirically demonstrated to improve predictive uncertainty in deep learning. The theoretical analysis of such Bayesian approximations is limited, including for ReLU classification networks. We present an analysis of approximate Gaussian posterior distributions on the weights of ReLU networks. We show that even a simplistic (thus cheap), non-Bayesian Gaussian distribution fixes the asymptotic overconfidence issue. Furthermore, when a Bayesian method, even if a simple one, is employed to obtain the Gaussian, the confidence becomes better calibrated. This theoretical result motivates a range of Laplace approximations along a fidelity-cost trade-off. We validate these findings empirically via experiments using common deep ReLU networks.", "output": [ "We argue theoretically that by simply assuming the weights of a ReLU network to be Gaussian distributed (without even a Bayesian formalism) could fix this issue; for a more calibrated uncertainty, a simple Bayesian method could already be sufficient." ] }, { "id": "task668-5f89bc7678c34665824919d10f83e245", "input": "Batch Normalization (BN) has become a cornerstone of deep learning across diverse architectures, appearing to help optimization as well as generalization. While the idea makes intuitive sense, theoretical analysis of its effectiveness has been lacking. Here theoretical support is provided for one of its conjectured properties, namely, the ability to allow gradient descent to succeed with less tuning of learning rates. It is shown that even if we fix the learning rate of scale-invariant parameters (e.g., weights of each layer with BN) to a constant (say, 0.3), gradient descent still approaches a stationary point (i.e., a solution where gradient is zero) in the rate of T^{−1/2} in T iterations, asymptotically matching the best bound for gradient descent with well-tuned learning rates. A similar result with convergence rate T^{−1/4} is also shown for stochastic gradient descent.", "output": [ "We give a theoretical analysis of the ability of batch normalization to automatically tune learning rates, in the context of finding stationary points for a deep learning objective." ] }, { "id": "task668-5bfb48100e9a46d4b002981cd0e621ab", "input": "Context information plays an important role in human language understanding, and it is also useful for machines to learn vector representations of language. In this paper, we explore an asymmetric encoder-decoder structure for unsupervised context-based sentence representation learning. As a result, we build an encoder-decoder architecture with an RNN encoder and a CNN decoder, and we show that neither an autoregressive decoder nor an RNN decoder is required. We further combine a suite of effective designs to significantly improve model efficiency while also achieving better performance. Our model is trained on two different large unlabeled corpora, and in both cases transferability is evaluated on a set of downstream language understanding tasks. We empirically show that our model is simple and fast while producing rich sentence representations that excel in downstream tasks.", "output": [ "We proposed an RNN-CNN encoder-decoder model for fast unsupervised sentence representation learning.", "Modifications to the skip-thought framework for learning sentence embeddings.", "This paper presents a new RNN encoder–CNN decoder hybrid design for use in pretraining, which does not require an autoregressive decoder when pretraining encoders.", "The authors extend Skip-thought by decoding only one target sentence using a CNN decoder." ] }, { "id": "task668-92d05a606f5e43b3bb2d8b80a34c11b3", "input": "Bayesian methods have been successfully applied to sparsify weights of neural networks and to remove structure units from the networks, e. g. neurons. We apply and further develop this approach for gated recurrent architectures. Specifically, in addition to sparsification of individual weights and neurons, we propose to sparsify preactivations of gates and information flow in LSTM. It makes some gates and information flow components constant, speeds up forward pass and improves compression. Moreover, the resulting structure of gate sparsity is interpretable and depends on the task.", "output": [ "We propose to sparsify preactivations of gates and information flow in LSTM to make them constant and boost the neuron sparsity level", "This paper proposed a sparsification method for recurrent neural networks by eliminating neurons with zero preactivations to obtain compact networks." ] }, { "id": "task668-15d90242ad7446efba88c9eb588d40e2", "input": "There are two main lines of research on visual reasoning: neural module network (NMN) with explicit multi-hop reasoning through handcrafted neural modules, and monolithic network with implicit reasoning in the latent feature space. The former excels in interpretability and compositionality, while the latter usually achieves better performance due to model flexibility and parameter efficiency. \n In order to bridge the gap of the two, we present Meta Module Network (MMN), a novel hybrid approach that can efficiently utilize a Meta Module to perform versatile functionalities, while preserving compositionality and interpretability through modularized design. The proposed model first parses an input question into a functional program through a Program Generator. Instead of handcrafting a task-specific network to represent each function like traditional NMN, we use Recipe Encoder to translate the functions into their corresponding recipes (specifications), which are used to dynamically instantiate the Meta Module into Instance Modules. To endow different instance modules with designated functionality, a Teacher-Student framework is proposed, where a symbolic teacher pre-executes against the scene graphs to provide guidelines for the instantiated modules (student) to follow. In a nutshell, MMN adopts the meta module to increase its parameterization efficiency, and uses recipe encoding to improve its generalization ability over NMN. Experiments conducted on the GQA benchmark demonstrates that: (1) MMN achieves significant improvement over both NMN and monolithic network baselines; (2) MMN is able to generalize to unseen but related functions.", "output": [ "We propose a new Meta Module Network to resolve some of the restrictions of previous Neural Module Network to achieve strong performance on realistic visual reasoning dataset." ] }, { "id": "task668-6971d046fd5440c4b5bc563c753fcd32", "input": "We conduct a mathematical analysis on the Batch normalization (BN) effect on gradient backpropagation in residual network training in this work, which is believed to play a critical role in addressing the gradient vanishing/explosion problem. Specifically, by analyzing the mean and variance behavior of the input and the gradient in the forward and backward passes through the BN and residual branches, respectively, we show that they work together to confine the gradient variance to a certain range across residual blocks in backpropagation. As a result, the gradient vanishing/explosion problem is avoided. Furthermore, we use the same analysis to discuss the tradeoff between depth and width of a residual network and demonstrate that shallower yet wider resnets have stronger learning performance than deeper yet thinner resnets.", "output": [ "Batch normalisation maintains gradient variance throughout training, thus stabilizing optimization.", "This paper analyzed the effect of batch normalization on gradient backpropagation in residual networks" ] }, { "id": "task668-a7e02f774f4b4816a8303f26a03675c6", "input": "We introduce causal implicit generative models (CiGMs): models that allow sampling from not only the true observational but also the true interventional distributions. We show that adversarial training can be used to learn a CiGM, if the generator architecture is structured based on a given causal graph. We consider the application of conditional and interventional sampling of face images with binary feature labels, such as mustache, young. We preserve the dependency structure between the labels with a given causal graph. We devise a two-stage procedure for learning a CiGM over the labels and the image. First we train a CiGM over the binary labels using a Wasserstein GAN where the generator neural network is consistent with the causal graph between the labels. Later, we combine this with a conditional GAN to generate images conditioned on the binary labels. We propose two new conditional GAN architectures: CausalGAN and CausalBEGAN. We show that the optimal generator of the CausalGAN, given the labels, samples from the image distributions conditioned on these labels. The conditional GAN combined with a trained CiGM for the labels is then a CiGM over the labels and the generated image. We show that the proposed architectures can be used to sample from observational and interventional image distributions, even for interventions which do not naturally occur in the dataset.", "output": [ "We introduce causal implicit generative models, which can sample from conditional and interventional distributions and also propose two new conditional GANs which we use for training them.", "A method of combining a casual graph, describing the dependency structure of labels with two conditional GAN architechtures that generate images conditioning on the binary label", "The authors address the issue of learning a causal model between image variables and the image itself from observational data, when given a causal structure between image labels." ] }, { "id": "task668-3a05f964b4a244c28326b90c84774aac", "input": "Sequence-to-sequence attention-based models are a promising approach for end-to-end speech recognition. The increased model power makes the training procedure more difficult, and analyzing failure modes of these models becomes harder because of the end-to-end nature. In this work, we present various analyses to better understand training and model properties. We investigate on pretraining variants such as growing in depth and width, and their impact on the final performance, which leads to over 8% relative improvement in word error rate. For a better understanding of how the attention process works, we study the encoder output and the attention energies and weights. Our experiments were performed on Switchboard, LibriSpeech and Wall Street Journal.", "output": [ "improved pretraining, and analysing encoder output and attention" ] }, { "id": "task668-aa131b7660c14a2eada65ea0156b4b0b", "input": "In this paper we show strategies to easily identify fake samples generated with the Generative Adversarial Network framework. One strategy is based on the statistical analysis and comparison of raw pixel values and features extracted from them. The other strategy learns formal specifications from the real data and shows that fake samples violate the specifications of the real data. We show that fake samples produced with GANs have a universal signature that can be used to identify fake samples. We provide results on MNIST, CIFAR10, music and speech data.", "output": [ "We show strategies to easily identify fake samples generated with the Generative Adversarial Network framework.", "Show that fake samples created with common generative adversarial network (GAN) implementations are easily identified using various statistical techniques. ", "The paper proposes statistics to identify fake data generated using GANs based on simple marginal statistics or formal specifications automatically generated from real data." ] }, { "id": "task668-7c5819d56b1a477989de5309539881af", "input": "Variance reduction methods which use a mixture of large and small batch gradients, such as SVRG (Johnson & Zhang, 2013) and SpiderBoost (Wang et al., 2018), require significantly more computational resources per update than SGD (Robbins & Monro, 1951). We reduce the computational cost per update of variance reduction methods by introducing a sparse gradient operator blending the top-K operator (Stich et al., 2018; Aji & Heafield, 2017) and the randomized coordinate descent operator. While the computational cost of computing the derivative of a model parameter is constant, we make the observation that the gains in variance reduction are proportional to the magnitude of the derivative. In this paper, we show that a sparse gradient based on the magnitude of past gradients reduces the computational cost of model updates without a significant loss in variance reduction. Theoretically, our algorithm is at least as good as the best available algorithm (e.g. SpiderBoost) under appropriate settings of parameters and can be much more efficient if our algorithm succeeds in capturing the sparsity of the gradients. Empirically, our algorithm consistently outperforms SpiderBoost using various models to solve various image classification tasks. We also provide empirical evidence to support the intuition behind our algorithm via a simple gradient entropy computation, which serves to quantify gradient sparsity at every iteration.", "output": [ "We use sparsity to improve the computational complexity of variance reduction methods." ] }, { "id": "task668-aaa16566195f46be9e6e5a7d9160e88a", "input": "We present a novel approach for the certification of neural networks against adversarial perturbations which combines scalable overapproximation methods with precise (mixed integer) linear programming. This results in significantly better precision than state-of-the-art verifiers on challenging feedforward and convolutional neural networks with piecewise linear activation functions.", "output": [ "We refine the over-approximation results from incomplete verifiers using MILP solvers to prove more robustness properties than state-of-the-art. ", "Introduces a verifier that obtains improvement on precision of incomplete verifiers and scalability of the complete verifiers using over-parameterization, mixed integer linear programming and linear programming relaxation.", "A mixed strategy to obtain better precision on robustness verifications of feed-forward neural networks with piecewise linear activation functions, achieving better precision than incomplete verifiers and more scalability than complete verifiers." ] }, { "id": "task668-fdbf255bc0dd4b628f142034facdd8ce", "input": "This paper proposes a dual variational autoencoder (DualVAE), a framework for generating images corresponding to multiclass labels. Recent research on conditional generative models, such as the Conditional VAE, exhibit image transfer by changing labels. However, when the dimension of multiclass labels is large, these models cannot change images corresponding to labels, because learning multiple distributions of the corresponding class is necessary to transfer an image. This leads to the lack of training data. Therefore, instead of conditioning with labels, we condition with latent vectors that include label information. DualVAE divides one distribution of the latent space by linear decision boundaries using labels. Consequently, DualVAE can easily transfer an image by moving a latent vector toward a decision boundary and is robust to the missing values of multiclass labels. To evaluate our proposed method, we introduce a conditional inception score (CIS) for measuring how much an image changes to the target class. We evaluate the images transferred by DualVAE using the CIS in CelebA datasets and demonstrate state-of-the-art performance in a multiclass setting.", "output": [ " a new framework using dual space for generating images corresponding to multiclass labels when the number of class is large" ] }, { "id": "task668-f9feabfb39be45c49edadfdf840d1a41", "input": "Training agents to operate in one environment often yields overfitted models that are unable to generalize to the changes in that environment. However, due to the numerous variations that can occur in the real-world, the agent is often required to be robust in order to be useful. This has not been the case for agents trained with reinforcement learning (RL) algorithms. In this paper, we investigate the overfitting of RL agents to the training environments in visual navigation tasks. Our experiments show that deep RL agents can overfit even when trained on multiple environments simultaneously. \n We propose a regularization method which combines RL with supervised learning methods by adding a term to the RL objective that would encourage the invariance of a policy to variations in the observations that ought not to affect the action taken. The results of this method, called invariance regularization, show an improvement in the generalization of policies to environments not seen during training.\n", "output": [ "We propose a regularization term that, when added to the reinforcement learning objective, allows the policy to maximize the reward and simultaneously learn to be invariant to the irrelevant changes within the input.." ] }, { "id": "task668-81fcbd0e87e043ad8d38bf7b81d5aa87", "input": "Classification systems typically act in isolation, meaning they are required to implicitly memorize the characteristics of all candidate classes in order to classify. The cost of this is increased memory usage and poor sample efficiency. We propose a model which instead verifies using reference images during the classification process, reducing the burden of memorization. The model uses iterative non-differentiable queries in order to classify an image. We demonstrate that such a model is feasible to train and can match baseline accuracy while being more parameter efficient. However, we show that finding the correct balance between image recognition and verification is essential to pushing the model towards desired behavior, suggesting that a pipeline of recognition followed by verification is a more promising approach towards designing more powerful networks with simpler architectures.", "output": [ "Image classification via iteratively querying for reference image from a candidate class with a RNN and use CNN to compare to the input image" ] }, { "id": "task668-a1acae4e888945c7b12ca4e8019025c9", "input": "The success of popular algorithms for deep reinforcement learning, such as policy-gradients and Q-learning, relies heavily on the availability of an informative reward signal at each timestep of the sequential decision-making process. When rewards are only sparsely available during an episode, or a rewarding feedback is provided only after episode termination, these algorithms perform sub-optimally due to the difficultly in credit assignment. Alternatively, trajectory-based policy optimization methods, such as cross-entropy method and evolution strategies, do not require per-timestep rewards, but have been found to suffer from high sample complexity by completing forgoing the temporal nature of the problem. Improving the efficiency of RL algorithms in real-world problems with sparse or episodic rewards is therefore a pressing need. In this work, we introduce a self-imitation learning algorithm that exploits and explores well in the sparse and episodic reward settings. We view each policy as a state-action visitation distribution and formulate policy optimization as a divergence minimization problem. We show that with Jensen-Shannon divergence, this divergence minimization problem can be reduced into a policy-gradient algorithm with shaped rewards learned from experience replays. Experimental results indicate that our algorithm works comparable to existing algorithms in environments with dense rewards, and significantly better in environments with sparse and episodic rewards. We then discuss limitations of self-imitation learning, and propose to solve them by using Stein variational policy gradient descent with the Jensen-Shannon kernel to learn multiple diverse policies. We demonstrate its effectiveness on a challenging variant of continuous-control MuJoCo locomotion tasks.", "output": [ "Policy optimization by using past good rollouts from the agent; learning shaped rewards via divergence minimization; SVPG with JS-kernel for population-based exploration." ] }, { "id": "task668-2dc33da21bd745478f11c94effb94998", "input": "We present the iterative two-pass decomposition flow to accelerate existing convolutional neural networks (CNNs). The proposed rank selection algorithm can effectively determine the proper ranks of the target convolutional layers for the low rank approximation. Our two-pass CP-decomposition helps prevent from the instability problem. The iterative flow makes the decomposition of the deeper networks systematic. The experiment results shows that VGG16 can be accelerated with a 6.2x measured speedup while the accuracy drop remains only 1.2%.\n", "output": [ "We present the iterative two-pass CP decomposition flow to effectively accelerate existing convolutional neural networks (CNNs).", "The paper proposes a novel workflow for acceleration and compression of CNNs and also proposes a way to determine the target rank of each layer given the target overall acceleration. ", "This paper addresses the problem of learning a low rank tensor filter operation for filtering layers in deep neural networks (DNNs). " ] }, { "id": "task668-326bce8ca0ea4010a041330883e159f1", "input": "Detecting communities or the modular structure of real-life networks (e.g. a social\n network or a product purchase network) is an important task because the way a\n network functions is often determined by its communities.\n The traditional approaches to community detection involve modularity-based approaches,\n which generally speaking, construct partitions based on heuristics that\n seek to maximize the ratio of the edges within the partitions to those between\n them. Node embedding approaches, which represent each node in a graph as a\n real-valued vector, transform the problem of community detection in a graph to\n that of clustering a set of vectors. Existing node embedding approaches are primarily\n based on first initiating uniform random walks from each node to construct\n a context of a node and then seeks to make the vector representation of\n the node close to its context. However, standard node embedding approaches do\n not directly take into account the community structure of a network while constructing\n the context around each node. To alleviate this, we explore two different\n threads of work. First, we investigate the use of biased random walks (specifically,\n maximum entropy based walks) to obtain more centrality preserving embedding\n of nodes, which we hypothesize may lead to more effective clusters in the embedded\n space. Second, we propose a community structure aware node embedding\n approach where we incorporate modularity-based partitioning heuristics into\n the objective function of node embedding. We demonstrate that our proposed approach\n for community detection outperforms a number of modularity-based baselines\n as well as K-means on a standard node-embedded vector space (specifically,\n node2vec) on a wide range of real-life networks of different sizes and densities.", "output": [ "A community preserving node embedding algorithm that results in more effective detection of communities with a clustering on the embedded space" ] }, { "id": "task668-5bb9439418c44b41b44580b96ac4a51c", "input": "In high-dimensional reinforcement learning settings with sparse rewards, performing\n effective exploration to even obtain any reward signal is an open challenge.\n While model-based approaches hold promise of better exploration via planning, it\n is extremely difficult to learn a reliable enough Markov Decision Process (MDP)\n in high dimensions (e.g., over 10^100 states). In this paper, we propose learning\n an abstract MDP over a much smaller number of states (e.g., 10^5), which we can\n plan over for effective exploration. We assume we have an abstraction function\n that maps concrete states (e.g., raw pixels) to abstract states (e.g., agent position,\n ignoring other objects). In our approach, a manager maintains an abstract\n MDP over a subset of the abstract states, which grows monotonically through targeted\n exploration (possible due to the abstract MDP). Concurrently, we learn a\n worker policy to travel between abstract states; the worker deals with the messiness\n of concrete states and presents a clean abstraction to the manager. On three of\n the hardest games from the Arcade Learning Environment (Montezuma's,\n Pitfall!, and Private Eye), our approach outperforms the previous\n state-of-the-art by over a factor of 2 in each game. In Pitfall!, our approach is\n the first to achieve superhuman performance without demonstrations.", "output": [ "We automatically construct and explore a small abstract Markov Decision Process, enabling us to achieve state-of-the-art results on Montezuma's Revenge, Pitfall!, and Private Eye by a significant margin." ] }, { "id": "task668-a17e0fad9a434deba0f9c5b4ab1165ec", "input": "In this work, we propose a goal-driven collaborative task that contains language, vision, and action in a virtual environment as its core components. Specifically, we develop a Collaborative image-Drawing game between two agents, called CoDraw. Our game is grounded in a virtual world that contains movable clip art objects. The game involves two players: a Teller and a Drawer. The Teller sees an abstract scene containing multiple clip art pieces in a semantically meaningful configuration, while the Drawer tries to reconstruct the scene on an empty canvas using available clip art pieces. The two players communicate via two-way communication using natural language. We collect the CoDraw dataset of ~10K dialogs consisting of ~138K messages exchanged between human agents. We define protocols and metrics to evaluate the effectiveness of learned agents on this testbed, highlighting the need for a novel \"crosstalk\" condition which pairs agents trained independently on disjoint subsets of the training data for evaluation. We present models for our task, including simple but effective baselines and neural network approaches trained using a combination of imitation learning and goal-driven training. All models are benchmarked using both fully automated evaluation and by playing the game with live human agents.", "output": [ "We introduce a dataset, models, and training + evaluation protocols for a collaborative drawing task that allows studying goal-driven and perceptually + actionably grounded language generation and understanding. " ] }, { "id": "task668-c9a8717de9374e3589769e41be24ac38", "input": "Variational inference (VI) is a popular approach for approximate Bayesian inference that is particularly promising for highly parameterized models such as deep neural networks. A key challenge of variational inference is to approximate the posterior over model parameters with a distribution that is simpler and tractable yet sufficiently expressive. In this work, we propose a method for training highly flexible variational distributions by starting with a coarse approximation and iteratively refining it. Each refinement step makes cheap, local adjustments and only requires optimization of simple variational families. We demonstrate theoretically that our method always improves a bound on the approximation (the Evidence Lower BOund) and observe this empirically across a variety of benchmark tasks. In experiments, our method consistently outperforms recent variational inference methods for deep learning in terms of log-likelihood and the ELBO. We see that the gains are further amplified on larger scale models, significantly outperforming standard VI and deep ensembles on residual networks on CIFAR10.", "output": [ "The paper proposes an algorithm to increase the flexibility of the variational posterior in Bayesian neural networks through iterative optimization.", "A method for training flexible variational posterior distributions, applied to Bayesian neural nets to perform variation inference (VI) over the weights." ] }, { "id": "task668-531821e3afad4c4dbbb21879065aa8eb", "input": "Adversarial attacks on convolutional neural networks (CNN) have gained significant attention and there have been active research efforts on defense mechanisms. Stochastic input transformation methods have been proposed, where the idea is to recover the image from adversarial attack by random transformation, and to take the majority vote as consensus among the random samples. However, the transformation improves the accuracy on adversarial images at the expense of the accuracy on clean images. While it is intuitive that the accuracy on clean images would deteriorate, the exact mechanism in which how this occurs is unclear. In this paper, we study the distribution of softmax induced by stochastic transformations. We observe that with random transformations on the clean images, although the mass of the softmax distribution could shift to the wrong class, the resulting distribution of softmax could be used to correct the prediction. Furthermore, on the adversarial counterparts, with the image transformation, the resulting shapes of the distribution of softmax are similar to the distributions from the clean images. With these observations, we propose a method to improve existing transformation-based defenses. We train a separate lightweight distribution classifier to recognize distinct features in the distributions of softmax outputs of transformed images. Our empirical studies show that our distribution classifier, by training on distributions obtained from clean images only, outperforms majority voting for both clean and adversarial images. Our method is generic and can be integrated with existing transformation-based defenses.", "output": [ "We enhance existing transformation-based defenses by using a distribution classifier on the distribution of softmax obtained from transformed images." ] }, { "id": "task668-e33990c963814802afb20a89aceef23c", "input": "We study the problem of multiset prediction. The goal of multiset prediction is to train a predictor that maps an input to a multiset consisting of multiple items. Unlike existing problems in supervised learning, such as classification, ranking and sequence generation, there is no known order among items in a target multiset, and each item in the multiset may appear more than once, making this problem extremely challenging. In this paper, we propose a novel multiset loss function by viewing this problem from the perspective of sequential decision making. The proposed multiset loss function is empirically evaluated on two families of datasets, one synthetic and the other real, with varying levels of difficulty, against various baseline loss functions including reinforcement learning, sequence, and aggregated distribution matching loss functions. The experiments reveal the effectiveness of the proposed loss function over the others.", "output": [ "We study the problem of multiset prediction and propose a novel multiset loss function, providing analysis and empirical evidence that demonstrates its effectiveness." ] }, { "id": "task668-348d4ae547374acd9322065d8982b89b", "input": "Unpaired image-to-image translation among category domains has achieved remarkable success in past decades. Recent studies mainly focus on two challenges. For one thing, such translation is inherently multimodal due to variations of domain-specific information (e.g., the domain of house cat has multiple fine-grained subcategories). For another, existing multimodal approaches have limitations in handling more than two domains, i.e. they have to independently build one model for every pair of domains. To address these problems, we propose the Hierarchical Image-to-image Translation (HIT) method which jointly formulates the multimodal and multi-domain problem in a semantic hierarchy structure, and can further control the uncertainty of multimodal. Specifically, we regard the domain-specific variations as the result of the multi-granularity property of domains, and one can control the granularity of the multimodal translation by dividing a domain with large variations into multiple subdomains which capture local and fine-grained variations. With the assumption of Gaussian prior, variations of domains are modeled in a common space such that translations can further be done among multiple domains within one model. To learn such complicated space, we propose to leverage the inclusion relation among domains to constrain distributions of parent and children to be nested. Experiments on several datasets validate the promising results and competitive performance against state-of-the-arts.", "output": [ "Granularity controled multi-domain and multimodal image to image translation method" ] }, { "id": "task668-5c1020ecd31b45e88e0f822074238de9", "input": "A plethora of methods attempting to explain predictions of black-box models have been proposed by the Explainable Artificial Intelligence (XAI) community. Yet, measuring the quality of the generated explanations is largely unexplored, making quantitative comparisons non-trivial. In this work, we propose a suite of multifaceted metrics that enables us to objectively compare explainers based on the correctness, consistency, as well as the confidence of the generated explanations. These metrics are computationally inexpensive, do not require model-retraining and can be used across different data modalities. We evaluate them on common explainers such as Grad-CAM, SmoothGrad, LIME and Integrated Gradients. Our experiments show that the proposed metrics reflect qualitative observations reported in earlier works.", "output": [ "We propose a suite of metrics that capture desired properties of explainability algorithms and use it to objectively compare and evaluate such methods" ] }, { "id": "task668-d8da2adbc56444428ed5240a9abd8202", "input": "The Boltzmann distribution is a natural model for many systems, from brains to materials and biomolecules, but is often of limited utility for fitting data because Monte Carlo algorithms are unable to simulate it in available time. This gap between the expressive capabilities and sampling practicalities of energy-based models is exemplified by the protein folding problem, since energy landscapes underlie contemporary knowledge of protein biophysics but computer simulations are challenged to fold all but the smallest proteins from first principles. In this work we aim to bridge the gap between the expressive capacity of energy functions and the practical capabilities of their simulators by using an unrolled Monte Carlo simulation as a model for data. We compose a neural energy function with a novel and efficient simulator based on Langevin dynamics to build an end-to-end-differentiable model of atomic protein structure given amino acid sequence information. We introduce techniques for stabilizing backpropagation under long roll-outs and demonstrate the model's capacity to make multimodal predictions and to, in some cases, generalize to unobserved protein fold types when trained on a large corpus of protein structures.", "output": [ "We use an unrolled simulator as an end-to-end differentiable model of protein structure and show it can (sometimes) hierarchically generalize to unseen fold topologies." ] }, { "id": "task668-78ab06c2146e445d801fedcd20d2fae0", "input": "Latent space based GAN methods and attention based encoder-decoder architectures have achieved impressive results in text generation and Unsupervised NMT respectively. Leveraging the two domains, we propose an adversarial latent space based architecture capable of generating parallel sentences in two languages concurrently and translating bidirectionally. The bilingual generation goal is achieved by sampling from the latent space that is adversarially constrained to be shared between both languages. First an NMT model is trained, with back-translation and an adversarial setup, to enforce a latent state between the two languages. The encoder and decoder are shared for the two translation directions. Next, a GAN is trained to generate ‘synthetic’ code mimicking the languages’ shared latent space. This code is then fed into the decoder to generate text in either language. We perform our experiments on Europarl and Multi30k datasets, on the English-French language pair, and document our performance using both Supervised and Unsupervised NMT.", "output": [ "We present a novel method for Bilingual Text Generation producing parallel concurrent sentences in two languages." ] }, { "id": "task668-998c7285fac84845a767e6c087cdaf47", "input": "Adversarial examples are modified samples that preserve original image structures but deviate classifiers. Researchers have put efforts into developing methods for generating adversarial examples and finding out origins. Past research put much attention on decision boundary changes caused by these methods. This paper, in contrast, discusses the origin of adversarial examples from a more underlying knowledge representation point of view. Human beings can learn and classify prototypes as well as transformations of objects. While neural networks store learned knowledge in a more hybrid way of combining all prototypes and transformations as a whole distribution. Hybrid storage may lead to lower distances between different classes so that small modifications can mislead the classifier. A one-step distribution imitation method is designed to imitate distribution of the nearest different class neighbor. Experiments show that simply by imitating distributions from a training set without any knowledge of the classifier can still lead to obvious impacts on classification results from deep networks. It also implies that adversarial examples can be in more forms than small perturbations. Potential ways of alleviating adversarial examples are discussed from the representation point of view. The first path is to change the encoding of data sent to the training step. Training data that are more prototypical can help seize more robust and accurate structural knowledge. The second path requires constructing learning frameworks with improved representations.", "output": [ "Hybird storage and representation of learned knowledge may be a reason for adversarial examples." ] }, { "id": "task668-5a9e85ea1e674eed83273f350178c809", "input": "Deep neural networks have demonstrated promising prediction and classification performance on many healthcare applications. However, the interpretability of those models are often lacking. On the other hand, classical interpretable models such as rule lists or decision trees do not lead to the same level of accuracy as deep neural networks and can often be too complex to interpret (due to the potentially large depth of rule lists). In this work, we present PEARL, Prototype lEArning via Rule Lists, which iteratively uses rule lists to guide a neural network to learn representative data prototypes. The resulting prototype neural network provides accurate prediction, and the prediction can be easily explained by prototype and its guiding rule lists. Thanks to the prediction power of neural networks, the rule lists from\t\t\t\t prototypes are more concise and hence provide better interpretability. On two real-world electronic healthcare records (EHR) datasets, PEARL consistently outperforms all baselines across both datasets, especially achieving performance improvement over conventional rule learning by up to 28% and over prototype learning by up to 3%. Experimental results also show the resulting interpretation of PEARL is simpler than the standard rule learning.", "output": [ "a method combining rule list learning and prototype learning ", "Presents a new interpretable prediction framework, which combines rule based learning, prototype learning, and NNs, that is particularly applicable to longitudinal data.", "This paper aims at tackling the lack of interpretability of deep learning models, and propose Prototype lEArning via Rule Lists (PEARL), which combines rule learning and prototype learning to achieve more accurate classification and makes the task of interpretability simpler." ] }, { "id": "task668-9989266edff64d7fb1235fce0f5c4085", "input": "In this paper, we turn our attention to the interworking between the activation functions and the batch normalization, which is a virtually mandatory technique to train deep networks currently. We propose the activation function Displaced Rectifier Linear Unit (DReLU) by conjecturing that extending the identity function of ReLU to the third quadrant enhances compatibility with batch normalization. Moreover, we used statistical tests to compare the impact of using distinct activation functions (ReLU, LReLU, PReLU, ELU, and DReLU) on the learning speed and test accuracy performance of standardized VGG and Residual Networks state-of-the-art models. These convolutional neural networks were trained on CIFAR-100 and CIFAR-10, the most commonly used deep learning computer vision datasets. The results showed DReLU speeded up learning in all models and datasets. Besides, statistical significant performance assessments (p<0.05) showed DReLU enhanced the test accuracy presented by ReLU in all scenarios. Furthermore, DReLU showed better test accuracy than any other tested activation function in all experiments with one exception, in which case it presented the second best performance. Therefore, this work demonstrates that it is possible to increase performance replacing ReLU by an enhanced activation function.", "output": [ "A new activation function called Displaced Rectifier Linear Unit is proposed. It is showed to enhance the training and inference performance of batch normalized convolutional neural networks.", "The paper compares and suggests against the usage of batch normalization after using rectifier linear units", "This paper proposes an activation function, called displaced ReLU, to improve the performance of CNNs that use batch normalization." ] }, { "id": "task668-8fa6a85f7a0641699c39453a0b8e20ad", "input": "Optimization algorithms for training deep models not only affects the convergence rate and stability of the training process, but are also highly related to the generalization performance of trained models. While adaptive algorithms, such as Adam and RMSprop, have shown better optimization performance than stochastic gradient descent (SGD) in many scenarios, they often lead to worse generalization performance than SGD, when used for training deep neural networks (DNNs). In this work, we identify two problems regarding the direction and step size for updating the weight vectors of hidden units, which may degrade the generalization performance of Adam. As a solution, we propose the normalized direction-preserving Adam (ND-Adam) algorithm, which controls the update direction and step size more precisely, and thus bridges the generalization gap between Adam and SGD. Following a similar rationale, we further improve the generalization performance in classification tasks by regularizing the softmax logits. By bridging the gap between SGD and Adam, we also shed some light on why certain optimization algorithms generalize better than others.", "output": [ "A tailored version of Adam for training DNNs, which bridges the generalization gap between Adam and SGD.", "Proposes a variant of ADAM optimization algorithm that normalizes weights of each hidden unit using batch normalization", "Extension of the Adam optimization algorithm to preserve the update direction by adapting the learning rate for the incoming weights to a hidden unit jointly using the L2 norm of the gradient vector" ] }, { "id": "task668-166015e5e43d4ec186e416f0d691a3ad", "input": "Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervised pre-training. Due to the cost of applying such models to down-stream tasks, several model compression techniques on pre-trained language representations have been proposed (Sun et al., 2019; Sanh, 2019). However, surprisingly, the simple baseline of just pre-training and fine-tuning compact models has been overlooked. In this paper, we first show that pre-training remains important in the context of smaller architectures, and fine-tuning pre-trained compact models can be competitive to more elaborate methods proposed in concurrent work. Starting with pre-trained compact models, we then explore transferring task knowledge from large fine-tuned models through standard knowledge distillation. The resulting simple, yet effective and general algorithm, Pre-trained Distillation, brings further improvements. Through extensive experiments, we more generally explore the interaction between pre-training and distillation under two variables that have been under-studied: model size and properties of unlabeled task data. One surprising observation is that they have a compound effect even when sequentially applied on the same data. To accelerate future research, we will make our 24 pre-trained miniature BERT models publicly available.", "output": [ "Studies how self-supervised learning and knowledge distillation interact in the context of building compact models.", "Investigates training compact pre-trained language models via distillation and shows that using a teacher for distilling a compact student model performs better than directly pre-training the model.", "This submission shows that pre-training a student directly on masked language modeling is better than distillation, and the best is to combine both and distill from that pre-trained student model." ] }, { "id": "task668-d36a963e5c254a3c8a60cc873b14f036", "input": "We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data. We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner.", "output": [ "We show how to learn spectral decompositions of linear operators with deep learning, and use it for unsupervised learning without a generative model.", "The authors propose to use a deep learning framework to solve the computation of the largest eigenvectors.", "This paper presents a framework to learn eigenfunctions via a stochastic process and proposes to tackle the challenge of computing eigenfunctions in a large-scale context by approximating then using a two-phase stochastic optimization process." ] }, { "id": "task668-d73569f2e8f6411a8a098dd7f1d9bff1", "input": "The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments. This challenging task demands that the agent be aware of which instruction was completed, which instruction is needed next, which way to go, and its navigation progress towards the goal. In this paper, we introduce a self-monitoring agent with two complementary components: (1) visual-textual co-grounding module to locate the instruction completed in the past, the instruction required for the next action, and the next moving direction from surrounding images and (2) progress monitor to ensure the grounded instruction correctly reflects the navigation progress. We test our self-monitoring agent on a standard benchmark and analyze our proposed approach through a series of ablation studies that elucidate the contributions of the primary components. Using our proposed method, we set the new state of the art by a significant margin (8% absolute increase in success rate on the unseen test set). Code is available at https://github.com/chihyaoma/selfmonitoring-agent.", "output": [ "We propose a self-monitoring agent for the Vision-and-Language Navigation task.", "A method for vision+language navigation which tracks progress on the instruction using a progress monitor and a visual-textual co-grounding module, and performs well on standard benchmarks.", "This paper describes a model for vision-and-language navigation with a panoramic visual attention and an auxillary progress monitoring loss, giving state-of-the-art results." ] }, { "id": "task668-5e2b79c4f5ae415ca648b5e36e539698", "input": "Infinite-width neural networks have been extensively used to study the theoretical properties underlying the extraordinary empirical success of standard, finite-width neural networks. Nevertheless, until now, infinite-width networks have been limited to at most two hidden layers. To address this shortcoming, we study the initialisation requirements of these networks and show that the main challenge for constructing them is defining the appropriate sampling distributions for the weights. Based on these observations, we propose a principled approach to weight initialisation that correctly accounts for the functional nature of the hidden layer activations and facilitates the construction of arbitrarily many infinite-width layers, thus enabling the construction of arbitrarily deep infinite-width networks. The main idea of our approach is to iteratively reparametrise the hidden-layer activations into appropriately defined reproducing kernel Hilbert spaces and use the canonical way of constructing probability distributions over these spaces for specifying the required weight distributions in a principled way. Furthermore, we examine the practical implications of this construction for standard, finite-width networks. In particular, we derive a novel weight initialisation scheme for standard, finite-width networks that takes into account the structure of the data and information about the task at hand. We demonstrate the effectiveness of this weight initialisation approach on the MNIST, CIFAR-10 and Year Prediction MSD datasets.", "output": [ "We propose a method for the construction of arbitrarily deep infinite-width networks, based on which we derive a novel weight initialisation scheme for finite-width networks and demonstrate its competitive performance.", "Proposes a weight initialization approach to enable infinitely deep and infinite-width networks with experimental results on small datasets.", "Proposes deep neural networks of infinite width." ] }, { "id": "task668-65fb78f9f9b046c6a1552de98222fe89", "input": "Long training times of deep neural networks are a bottleneck in machine learning research. The major impediment to fast training is the quadratic growth of both memory and compute requirements of dense and convolutional layers with respect to their information bandwidth. Recently, training `a priori' sparse networks has been proposed as a method for allowing layers to retain high information bandwidth, while keeping memory and compute low. However, the choice of which sparse topology should be used in these networks is unclear. In this work, we provide a theoretical foundation for the choice of intra-layer topology. First, we derive a new sparse neural network initialization scheme that allows us to explore the space of very deep sparse networks. Next, we evaluate several topologies and show that seemingly similar topologies can often have a large difference in attainable accuracy. To explain these differences, we develop a data-free heuristic that can evaluate a topology independently from the dataset the network will be trained on. We then derive a set of requirements that make a good topology, and arrive at a single topology that satisfies all of them.", "output": [ "We investigate pruning DNNs before training and provide an answer to which topology should be used for training a priori sparse networks.", "The authors propose to replace dense layers with sparsely-connected linear layers and an approach to finding the best topology by measuring how well the sparse layers approximate random weights of their dense counterparts.", "The paper proposes a sparse cascade architecture that is a multiplication of several sparse matrices and a specific connectivity pattern that outperforms other provided considerations." ] }, { "id": "task668-657a144ce37d4b37966a213a59bfc77c", "input": "Modern neural architectures critically rely on attention for mapping structured inputs to sequences. In this paper we show that prevalent attention architectures do not adequately model the dependence among the attention and output tokens across a predicted sequence.\n We present an alternative architecture called Posterior Attention Models that after a principled factorization of the full joint distribution of the attention and output variables, proposes two major changes. First, the position where attention is marginalized is changed from the input to the output. Second, the attention propagated to the next decoding stage is a posterior attention distribution conditioned on the output. Empirically on five translation and two morphological inflection tasks the proposed posterior attention models yield better BLEU score and alignment accuracy than existing attention models.", "output": [ "Computing attention based on posterior distribution leads to more meaningful attention and better performance", "This paper proposes a sequence to sequence model where attention is treated as a latent variable, and derives novel inference procedures for this model, obtaining improvements in machine translation and morphological inflection generation tasks.", "This paper presents a novel posterior attention model for seq2seq problems" ] }, { "id": "task668-50193d2cd0814238baccd2a51027b723", "input": "Training a model to perform a task typically requires a large amount of data from the domains in which the task will be applied.\n However, it is often the case that data are abundant in some domains but scarce in others. Domain adaptation deals with the challenge of adapting a model trained from a data-rich source domain to perform well in a data-poor target domain. In general, this requires learning plausible mappings between domains. CycleGAN is a powerful framework that efficiently learns to map inputs from one domain to another using adversarial training and a cycle-consistency constraint. However, the conventional approach of enforcing cycle-consistency via reconstruction may be overly restrictive in cases where one or more domains have limited training data. In this paper, we propose an augmented cyclic adversarial learning model that enforces the cycle-consistency constraint via an external task specific model, which encourages the preservation of task-relevant content as opposed to exact reconstruction. We explore digit classification in a low-resource setting in supervised, semi and unsupervised situation, as well as high resource unsupervised. In low-resource supervised setting, the results show that our approach improves absolute performance by 14% and 4% when adapting SVHN to MNIST and vice versa, respectively, which outperforms unsupervised domain adaptation methods that require high-resource unlabeled target domain. Moreover, using only few unsupervised target data, our approach can still outperforms many high-resource unsupervised models. Our model also outperforms on USPS to MNIST and synthetic digit to SVHN for high resource unsupervised adaptation. In speech domains, we similarly adopt a speech recognition model from each domain as the task specific model. Our approach improves absolute performance of speech recognition by 2% for female speakers in the TIMIT dataset, where the majority of training samples are from male voices.", "output": [ "A new cyclic adversarial learning augmented with auxiliary task model which improves domain adaptation performance in low resource supervised and unsupervised situations ", "Proposes an extension of cycle-consistent adversatial adaptation methods in order to tackle domain adaptation where limited supervised target data is available.", "This paper introduces a domain adaptation approach based on the idea of Cyclic GAN and proposes two different algorithms." ] }, { "id": "task668-3b556e5a68cb4391a2ab465b6481614e", "input": "We introduce a novel end-to-end approach for learning to cluster in the absence of labeled examples. Our clustering objective is based on optimizing normalized cuts, a criterion which measures both intra-cluster similarity as well as inter-cluster dissimilarity. We define a differentiable loss function equivalent to the expected normalized cuts. Unlike much of the work in unsupervised deep learning, our trained model directly outputs final cluster assignments, rather than embeddings that need further processing to be usable. Our approach generalizes to unseen datasets across a wide variety of domains, including text, and image. Specifically, we achieve state-of-the-art results on popular unsupervised clustering benchmarks (e.g., MNIST, Reuters, CIFAR-10, and CIFAR-100), outperforming the strongest baselines by up to 10.9%. Our generalization results are superior (by up to 21.9%) to the recent top-performing clustering approach with the ability to generalize.", "output": [ "We introduce a novel end-to-end approach for learning to cluster in the absence of labeled examples. We define a differentiable loss function equivalent to the expected normalized cuts." ] }, { "id": "task668-baa1667e8eeb4ca4a77cbe6aabd454af", "input": "Federated learning, where a global model is trained by iterative parameter averaging of locally-computed updates, is a promising approach for distributed training of deep networks; it provides high communication-efficiency and privacy-preservability, which allows to fit well into decentralized data environments, e.g., mobile-cloud ecosystems. However, despite the advantages, the federated learning-based methods still have a challenge in dealing with non-IID training data of local devices (i.e., learners). In this regard, we study the effects of a variety of hyperparametric conditions under the non-IID environments, to answer important concerns in practical implementations: (i) We first investigate parameter divergence of local updates to explain performance degradation from non-IID data. The origin of the parameter divergence is also found both empirically and theoretically. (ii) We then revisit the effects of optimizers, network depth/width, and regularization techniques; our observations show that the well-known advantages of the hyperparameter optimization strategies could rather yield diminishing returns with non-IID data. (iii) We finally provide the reasons of the failure cases in a categorized way, mainly based on metrics of the parameter divergence.", "output": [ "We investigate the internal reasons of our observations, the diminishing effects of the well-known hyperparameter optimization methods on federated learning from decentralized non-IID data." ] }, { "id": "task668-7e73fa1cd12c49319d28c8a117e4218a", "input": "Recent work has demonstrated that neural networks are vulnerable to adversarial examples, i.e., inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against a well-defined class of adversaries. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest robustness against a first-order adversary as a natural security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models.", "output": [ "We provide a principled, optimization-based re-look at the notion of adversarial examples, and develop methods that produce models that are adversarially robust against a wide range of adversaries.", "Investigates a minimax formulation of deep network learning to increase their robustness, using projected gradient descent as the main adversary. ", "This paper proposes to look at making neural networks resistant to adversarial loss through the framework of saddle-point problems. " ] }, { "id": "task668-b6ff1e2ac8ca4841b5be56be4f3f5b3f", "input": "We provide a novel perspective on the forward pass through a block of layers in a deep network. In particular, we show that a forward pass through a standard dropout layer followed by a linear layer and a non-linear activation is equivalent to optimizing a convex objective with a single iteration of a $\\tau$-nice Proximal Stochastic Gradient method. We further show that replacing standard Bernoulli dropout with additive dropout is equivalent to optimizing the same convex objective with a variance-reduced proximal method. By expressing both fully-connected and convolutional layers as special cases of a high-order tensor product, we unify the underlying convex optimization problem in the tensor setting and derive a formula for the Lipschitz constant $L$ used to determine the optimal step size of the above proximal methods. We conduct experiments with standard convolutional networks applied to the CIFAR-10 and CIFAR-100 datasets and show that replacing a block of layers with multiple iterations of the corresponding solver, with step size set via $L$, consistently improves classification accuracy.", "output": [ "A framework that links deep network layers to stochastic optimization algorithms; can be used to improve model accuracy and inform network design." ] }, { "id": "task668-84dc1e4f8e624fd0a3eccf2b2c781837", "input": "In this paper we use the geometric properties of the optimal transport (OT) problem and the Wasserstein distances to define a prior distribution for the latent space of an auto-encoder. We introduce Sliced-Wasserstein Auto-Encoders (SWAE), that enable one to shape the distribution of the latent space into any samplable probability distribution without the need for training an adversarial network or having a likelihood function specified. In short, we regularize the auto-encoder loss with the sliced-Wasserstein distance between the distribution of the encoded training samples and a samplable prior distribution. We show that the proposed formulation has an efficient numerical solution that provides similar capabilities to Wasserstein Auto-Encoders (WAE) and Variational Auto-Encoders (VAE), while benefiting from an embarrassingly simple implementation. We provide extensive error analysis for our algorithm, and show its merits on three benchmark datasets.", "output": [ "In this paper we use the sliced-Wasserstein distance to shape the latent distribution of an auto-encoder into any samplable prior distribution. " ] }, { "id": "task668-f11a0d8d360e48f4923de91777b6edf3", "input": "We describe two end-to-end autoencoding models for semi-supervised graph-based dependency parsing. The first model is a Local Autoencoding Parser (LAP) encoding the input using continuous latent variables in a sequential manner; The second model is a Global Autoencoding Parser (GAP) encoding the input into dependency trees as latent variables, with exact inference. Both models consist of two parts: an encoder enhanced by deep neural networks (DNN) that can utilize the contextual information to encode the input into latent variables, and a decoder which is a generative model able to reconstruct the input. Both LAP and GAP admit a unified structure with different loss functions for labeled and unlabeled data with shared parameters. We conducted experiments on WSJ and UD dependency parsing data sets, showing that our models can exploit the unlabeled data to boost the performance given a limited amount of labeled data.", "output": [ "We describe two end-to-end autoencoding parsers for semi-supervised graph-based dependency parsing." ] }, { "id": "task668-3a38a830aa5f47ef9500c28e3c8b3e9b", "input": "We propose a new application of embedding techniques to problem retrieval in adaptive tutoring. The objective is to retrieve problems similar in mathematical concepts. There are two challenges: First, like sentences, problems helpful to tutoring are never exactly the same in terms of the underlying concepts. Instead, good problems mix concepts in innovative ways, while still displaying continuity in their relationships. Second, it is difficult for humans to determine a similarity score consistent across a large enough training set. We propose a hierarchical problem embedding algorithm, called Prob2Vec, that consists of an abstraction and an embedding step. Prob2Vec achieves 96.88\\% accuracy on a problem similarity test, in contrast to 75\\% from directly applying state-of-the-art sentence embedding methods. It is surprising that Prob2Vec is able to distinguish very fine-grained differences among problems, an ability humans need time and effort to acquire. In addition, the sub-problem of concept labeling with imbalanced training data set is interesting in its own right. It is a multi-label problem suffering from dimensionality explosion, which we propose ways to ameliorate. We propose the novel negative pre-training algorithm that dramatically reduces false negative and positive ratios for classification, using an imbalanced training data set.", "output": [ "We propose the Prob2Vec method for problem embedding used in a personalized e-learning tool in addition to a data level classification method, called negative pre-training, for cases where the training data set is imbalanced." ] }, { "id": "task668-3a5c8cd821824a6985bcd16eb9461dab", "input": "We propose Automating Science Journalism (ASJ), the process of producing a press release from a scientific paper, as a novel task that can serve as a new benchmark for neural abstractive summarization. ASJ is a challenging task as it requires long source texts to be summarized to long target texts, while also paraphrasing complex scientific concepts to be understood by the general audience. For this purpose, we introduce a specialized dataset for ASJ that contains scientific papers and their press releases from Science Daily. While state-of-the-art sequence-to-sequence (seq2seq) models could easily generate convincing press releases for ASJ, these are generally nonfactual and deviate from the source. To address this issue, we improve seq2seq generation via transfer learning by co-training with new targets: (i) scientific abstracts of sources and (ii) partitioned press releases. We further design a measure for factuality that scores how pertinent to the scientific papers the press releases under our seq2seq models are. Our quantitative and qualitative evaluation shows sizable improvements over a strong baseline, suggesting that the proposed framework could improve seq2seq summarization beyond ASJ.", "output": [ "New: application of seq2seq modelling to automating sciene journalism; highly abstractive dataset; transfer learning tricks; automatic evaluation measure." ] }, { "id": "task668-6d9bd831110b46f29021c6d1577bf22c", "input": "We present Cross-View Training (CVT), a simple but effective method for deep semi-supervised learning. On labeled examples, the model is trained with standard cross-entropy loss. On an unlabeled example, the model first performs inference (acting as a \"teacher\") to produce soft targets. The model then learns from these soft targets (acting as a ``\"student\"). We deviate from prior work by adding multiple auxiliary student prediction layers to the model. The input to each student layer is a sub-network of the full model that has a restricted view of the input (e.g., only seeing one region of an image). The students can learn from the teacher (the full model) because the teacher sees more of each example. Concurrently, the students improve the quality of the representations used by the teacher as they learn to make predictions with limited data. When combined with Virtual Adversarial Training, CVT improves upon the current state-of-the-art on semi-supervised CIFAR-10 and semi-supervised SVHN. We also apply CVT to train models on five natural language processing tasks using hundreds of millions of sentences of unlabeled data. On all tasks CVT substantially outperforms supervised learning alone, resulting in models that improve upon or are competitive with the current state-of-the-art.\n", "output": [ "Self-training with different views of the input gives excellent results for semi-supervised image recognition, sequence tagging, and dependency parsing." ] }, { "id": "task668-45d0fae833954bc789dd1e701e8b4e12", "input": "Adoption of deep learning in safety-critical systems raise the need for understanding what deep neural networks do not understand. Several methodologies to estimate model uncertainty have been proposed, but these methodologies constrain either how the neural network is trained or constructed. We present Outlier Detection In Neural networks (ODIN), an assumption-free method for detecting outlier observations during prediction, based on principles widely used in manufacturing process monitoring. By using a linear approximation of the hidden layer manifold, we add prediction-time outlier detection to models after training without altering architecture or training. We demonstrate that ODIN efficiently detect outliers during prediction on Fashion-MNIST, ImageNet-synsets and speech command recognition.", "output": [ "An add-on method for deep learning to detect outliers during prediction-time" ] }, { "id": "task668-39e84f84691445bc94df97ba3e54caea", "input": "We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks, which are tasks where multiple agents must work together to achieve a goal they could not individually. Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own. Thus, we propose to incentivize agents to take (joint) actions whose effects cannot be predicted via a composition of the predicted effect for each individual agent. We study two instantiations of this idea, one based on the true states encountered, and another based on a dynamics model trained concurrently with the policy. While the former is simpler, the latter has the benefit of being analytically differentiable with respect to the action taken. We validate our approach in robotic bimanual manipulation tasks with sparse rewards; we find that our approach yields more efficient learning than both 1) training with only the sparse reward and 2) using the typical surprise-based formulation of intrinsic motivation, which does not bias toward synergistic behavior. Videos are available on the project webpage: https://sites.google.com/view/iclr2020-synergistic.", "output": [ "We propose a formulation of intrinsic motivation that is suitable as an exploration bias in multi-agent sparse-reward synergistic tasks, by encouraging agents to affect the world in ways that would not be achieved if they were acting individually.", "The paper focuses on using intrinsic motivation to improve the exploration process of reinforcement learning agents in tasks that require multi-agent to achieve." ] }, { "id": "task668-05836f81b7874c7fbdf309b49c2cc460", "input": "Recent empirical results on over-parameterized deep networks are marked by a striking absence of the classic U-shaped test error curve: test error keeps decreasing in wider networks. Researchers are actively working on bridging this discrepancy by proposing better complexity measures. Instead, we directly measure prediction bias and variance for four classification and regression tasks on modern deep networks. We find that both bias and variance can decrease as the number of parameters grows. Qualitatively, the phenomenon persists over a number of gradient-based optimizers. To better understand the role of optimization, we decompose the total variance into variance due to training set sampling and variance due to initialization. Variance due to initialization is significant in the under-parameterized regime. In the over-parameterized regime, total variance is much lower and dominated by variance due to sampling. We provide theoretical analysis in a simplified setting that is consistent with our empirical findings.", "output": [ "We provide evidence against classical claims about the bias-variance tradeoff and propose a novel decomposition for variance." ] }, { "id": "task668-cee9769dddc448dca8c77ae543d85e3a", "input": "This paper studies a class of adaptive gradient based momentum algorithms that update the search directions and learning rates simultaneously using past gradients. This class, which we refer to as the ''``Adam-type'', includes the popular algorithms such as Adam, AMSGrad, AdaGrad. Despite their popularity in training deep neural networks (DNNs), the convergence of these algorithms for solving non-convex problems remains an open question. In this paper, we develop an analysis framework and a set of mild sufficient conditions that guarantee the convergence of the Adam-type methods, with a convergence rate of order $O(\\log{T}/\\sqrt{T})$ for non-convex stochastic optimization. Our convergence analysis applies to a new algorithm called AdaFom (AdaGrad with First Order Momentum). We show that the conditions are essential, by identifying concrete examples in which violating the conditions makes an algorithm diverge. Besides providing one of the first comprehensive analysis for Adam-type methods in the non-convex setting, our results can also help the practitioners to easily monitor the progress of algorithms and determine their convergence behavior.", "output": [ "We analyze convergence of Adam-type algorithms and provide mild sufficient conditions to guarantee their convergence, we also show violating the conditions can makes an algorithm diverge.", "Presents a convergence analysis in the non-convex setting for a family of optimization algorithms.", "This paper investigates the convergence condition of Adam-type optimizers in the unconstrained non-convex optimization problems." ] }, { "id": "task668-daf17607a9fe46c38a489e0f56c3f4e2", "input": "Owing to the ubiquity of computer software, software vulnerability detection (SVD) has become an important problem in the software industry and in the field of computer security. One of the most crucial issues in SVD is coping with the scarcity of labeled vulnerabilities in projects that require the laborious manual labeling of code by software security experts. One possible way to address is to employ deep domain adaptation which has recently witnessed enormous success in transferring learning from structural labeled to unlabeled data sources. The general idea is to map both source and target data into a joint feature space and close the discrepancy gap of those data in this joint feature space. Generative adversarial network (GAN) is a technique that attempts to bridge the discrepancy gap and also emerges as a building block to develop deep domain adaptation approaches with state-of-the-art performance. However, deep domain adaptation approaches using the GAN principle to close the discrepancy gap are subject to the mode collapsing problem that negatively impacts the predictive performance. Our aim in this paper is to propose Dual Generator-Discriminator Deep Code Domain Adaptation Network (Dual-GD-DDAN) for tackling the problem of transfer learning from labeled to unlabeled software projects in the context of SVD in order to resolve the mode collapsing problem faced in previous approaches. The experimental results on real-world software projects show that our proposed method outperforms state-of-the-art baselines by a wide margin.", "output": [ "Our aim in this paper is to propose a new approach for tackling the problem of transfer learning from labeled to unlabeled software projects in the context of SVD in order to resolve the mode collapsing problem faced in previous approaches." ] }, { "id": "task668-a691f8f09d1344268f273d19f4101dfc", "input": "Variational Auto Encoders (VAE) are capable of generating realistic images, sounds and video sequences. From practitioners point of view, we are usually interested in solving problems where tasks are learned sequentially, in a way that avoids revisiting all previous data at each stage. We address this problem by introducing a conceptually simple and scalable end-to-end approach of incorporating past knowledge by learning prior directly from the data. We consider scalable boosting-like approximation for intractable theoretical optimal prior. We provide empirical studies on two commonly used benchmarks, namely MNIST and Fashion MNIST on disjoint sequential image generation tasks. For each dataset proposed method delivers the best results among comparable approaches, avoiding catastrophic forgetting in a fully automatic way with a fixed model architecture.", "output": [ "Novel algorithm for Incremental learning of VAE with fixed architecture" ] }, { "id": "task668-9cbabc11e1864d88b1bc77e59d328589", "input": "Transfer reinforcement learning (RL) aims at improving learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks. However, it remains challenging to transfer knowledge between different environmental dynamics without having access to the source environments. In this work, we explore a new challenge in transfer RL, where only a set of source policies collected under unknown diverse dynamics is available for learning a target task efficiently. To address this problem, the proposed approach, MULTI-source POLicy AggRegation (MULTIPOLAR), comprises two key techniques. We learn to aggregate the actions provided by the source policies adaptively to maximize the target task performance. Meanwhile, we learn an auxiliary network that predicts residuals around the aggregated actions, which ensures the target policy's expressiveness even when some of the source policies perform poorly. We demonstrated the effectiveness of MULTIPOLAR through an extensive experimental evaluation across six simulated environments ranging from classic control problems to challenging robotics simulations, under both continuous and discrete action spaces.", "output": [ "We propose MULTIPOLAR, a transfer RL method that leverages a set of source policies collected under unknown diverse environmental dynamics to efficiently learn a target policy in another dynamics." ] }, { "id": "task668-f60494ca857e4fdca3ff566ed1f57272", "input": "We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world. In spite of being trained with only internally available signals, these dynamic body models come to represent external objects through the necessity of predicting their effects on the agent's own body. That is, the model learns holistic persistent representations of objects in the world, even though the only training signals are body signals. Our dynamics model is able to successfully predict distributions over 132 sensor readings over 100 steps into the future and we demonstrate that even when the body is no longer in contact with an object, the latent variables of the dynamics model continue to represent its shape. We show that active data collection by maximizing the entropy of predictions about the body---touch sensors, proprioception and vestibular information---leads to learning of dynamic models that show superior performance when used for control. We also collect data from a real robotic hand and show that the same models can be used to answer questions about properties of objects in the real world. Videos with qualitative results of our models are available at https://goo.gl/mZuqAV.", "output": [ "We train predictive models on proprioceptive information and show they represent properties of external objects." ] }, { "id": "task668-68ec6b54314f44fbbc1acbce3c5deb87", "input": "Reinforcement learning (RL) has proven to be a powerful paradigm for deriving complex behaviors from simple reward signals in a wide range of environments. When applying RL to continuous control agents in simulated physics environments, the body is usually considered to be part of the environment. However, during evolution the physical body of biological organisms and their controlling brains are co-evolved, thus exploring a much larger space of actuator/controller configurations. Put differently, the intelligence does not reside only in the agent's mind, but also in the design of their body. \n We propose a method for uncovering strong agents, consisting of a good combination of a body and policy, based on combining RL with an evolutionary procedure. Given the resulting agent, we also propose an approach for identifying the body changes that contributed the most to the agent performance. We use the Shapley value from cooperative game theory to find the fair contribution of individual components, taking into account synergies between components. \n We evaluate our methods in an environment similar to the the recently proposed Robo-Sumo task, where agents in a 3D environment with simulated physics compete in tipping over their opponent or pushing them out of the arena. Our results show that the proposed methods are indeed capable of generating strong agents, significantly outperforming baselines that focus on optimizing the agent policy alone. \n\n A video is available at: www.youtube.com/watch?v=eei6Rgom3YY", "output": [ "Evolving the shape of the body in RL controlled agents improves their performance (and help learning)", "PEOM algorithm that incorporates Shapley value to accelerate the evolution by identifying contribution of each body part" ] }, { "id": "task668-6a6bb413ee924ca899b8466b7c668e51", "input": "Adversarial examples have somewhat disrupted the enormous success of machine learning (ML) and are causing concern with regards to its trustworthiness: A small perturbation of an input results in an arbitrary failure of an otherwise seemingly well-trained ML system. While studies are being conducted to discover the intrinsic properties of adversarial examples, such as their transferability and universality, there is insufficient theoretic analysis to help understand the phenomenon in a way that can influence the design process of ML experiments. In this paper, we deduce an information-theoretic model which explains adversarial attacks universally as the abuse of feature redundancies in ML algorithms. We prove that feature redundancy is a necessary condition for the existence of adversarial examples. Our model helps to explain the major questions raised in many anecdotal studies on adversarial examples. Our theory is backed up by empirical measurements of the information content of benign and adversarial examples on both image and text datasets. Our measurements show that typical adversarial examples introduce just enough redundancy to overflow the decision making of a machine learner trained on corresponding benign examples. We conclude with actionable recommendations to improve the robustness of machine learners against adversarial examples.", "output": [ "A new theoretical explanation for the existence of adversarial examples" ] }, { "id": "task668-f4d7c5c2885244b3a64183df546ec38a", "input": "Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale. We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity and fidelity than previous work. Our proposed model, Dual Video Discriminator GAN (DVD-GAN), scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator. We evaluate on the related tasks of video synthesis and video prediction, and achieve new state-of-the-art Fréchet Inception Distance for prediction for Kinetics-600, as well as state-of-the-art Inception Score for synthesis on the UCF-101 dataset, alongside establishing a strong baseline for synthesis on Kinetics-600.", "output": [ "We propose DVD-GAN, a large video generative model that is state of the art on several tasks and produces highly complex videos when trained on large real world datasets." ] }, { "id": "task668-12fc8ef8369b4b08af6f950c8c204095", "input": "While deep neural networks have shown outstanding results in a wide range of applications,\n learning from a very limited number of examples is still a challenging\n task. Despite the difficulties of the few-shot learning, metric-learning techniques\n showed the potential of the neural networks for this task. While these methods\n perform well, they don’t provide satisfactory results. In this work, the idea of\n metric-learning is extended with Support Vector Machines (SVM) working mechanism,\n which is well known for generalization capabilities on a small dataset.\n Furthermore, this paper presents an end-to-end learning framework for training\n adaptive kernel SVMs, which eliminates the problem of choosing a correct kernel\n and good features for SVMs. Next, the one-shot learning problem is redefined\n for audio signals. Then the model was tested on vision task (using Omniglot\n dataset) and speech task (using TIMIT dataset) as well. Actually, the algorithm\n using Omniglot dataset improved accuracy from 98.1% to 98.5% on the one-shot\n classification task and from 98.9% to 99.3% on the few-shot classification task.", "output": [ "The proposed method is an end-to-end neural SVM, which is optimized for few-shot learning." ] }, { "id": "task668-e0e0d59e43ad46ea9b9a7eb43e8bf6ad", "input": "We introduce a new and rigorously-formulated PAC-Bayes few-shot meta-learning algorithm that implicitly learns a model prior distribution of interest. Our proposed method extends the PAC-Bayes framework from a single task setting to the few-shot meta-learning setting to upper-bound generalisation errors on unseen tasks. We also propose a generative-based approach to model the shared prior and task-specific posterior more expressively compared to the usual diagonal Gaussian assumption. We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate, with state-of-the-art calibration and classification results on mini-ImageNet benchmark, and competitive results in a multi-modal task-distribution regression.", "output": [ "Bayesian meta-learning using PAC-Bayes framework and implicit prior distributions" ] }, { "id": "task668-ac26cc76b91345db934e61faa818a26e", "input": "Normalising Flows (NFs) are a class of likelihood-based generative models that have recently gained popularity. They are based on the idea of transforming a simple density into that of the data. We seek to better understand this class of models, and how they compare to previously proposed techniques for generative modeling and unsupervised representation learning. For this purpose we reinterpret NFs in the framework of Variational Autoencoders (VAEs), and present a new form of VAE that generalises normalising flows. The new generalised model also reveals a close connection to denoising autoencoders, and we therefore call our model the Variational Denoising Autoencoder (VDAE). Using our unified model, we systematically examine the model space between flows, variational autoencoders, and denoising autoencoders, in a set of preliminary experiments on the MNIST handwritten digits. The experiments shed light on the modeling assumptions implicit in these models, and they suggest multiple new directions for future research in this space.", "output": [ "We explore the relationship between Normalising Flows and Variational- and Denoising Autoencoders, and propose a novel model that generalises them." ] }, { "id": "task668-1367ec5f7d9f4f8eba85ea00db6a25a9", "input": "Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatio-temporally coordinated activation patterns, or \"motifs\", are thought to be building blocks of neural representations and information processing. We here propose LeMoNADe, a new exploratory data analysis method that facilitates hunting for motifs in calcium imaging videos, the dominant microscopic functional imaging modality in neurophysiology. Our nonparametric method extracts motifs directly from videos, bypassing the difficult intermediate step of spike extraction. Our technique augments variational autoencoders with a discrete stochastic node, and we show in detail how a differentiable reparametrization and relaxation can be used. An evaluation on simulated data, with available ground truth, reveals excellent quantitative performance. In real video data acquired from brain slices, with no ground truth available, LeMoNADe uncovers nontrivial candidate motifs that can help generate hypotheses for more focused biological investigations.", "output": [ "We present LeMoNADe, an end-to-end learned motif detection method directly operating on calcium imaging videos.", "This paper proposes a VAE-style model for identifying motifs from calcium imaging videos, relying on Bernouli variables and requires Gumbel-softmax trick for inference." ] }, { "id": "task668-905fd8a002364f5d9c03b69e6b2465f9", "input": "We introduce an approach for augmenting model-free deep reinforcement learning agents with a mechanism for relational reasoning over structured representations, which improves performance, learning efficiency, generalization, and interpretability. Our architecture encodes an image as a set of vectors, and applies an iterative message-passing procedure to discover and reason about relevant entities and relations in a scene. In six of seven StarCraft II Learning Environment mini-games, our agent achieved state-of-the-art performance, and surpassed human grandmaster-level on four. In a novel navigation and planning task, our agent's performance and learning efficiency far exceeded non-relational baselines, it was able to generalize to more complex scenes than it had experienced during training. Moreover, when we examined its learned internal representations, they reflected important structure about the problem and the agent's intentions. The main contribution of this work is to introduce techniques for representing and reasoning about states in model-free deep reinforcement learning agents via relational inductive biases. Our experiments show this approach can offer advantages in efficiency, generalization, and interpretability, and can scale up to meet some of the most challenging test environments in modern artificial intelligence.", "output": [ "Relational inductive biases improve out-of-distribution generalization capacities in model-free reinforcement learning agents", "A shared relational network architecture for parameterizing the actor and critic network, focused on distributed advantage actor-critic algorithms, that enhances model-free deep reinforcement techniques with relational knowledge about the environment so agents can learn interpretable state representations.", "A quantitative and qualitative analysis and evaluation of the self-attention mechanism combined with relation network in the context of model-free RL." ] }, { "id": "task668-c2e960737f384d188ff2f8ee5956a8d1", "input": "Recurrent neural networks have achieved excellent performance in many applications. However, on portable devices with limited resources, the models are often too large to deploy. For applications on the server with large scale concurrent requests, the latency during inference can also be very critical for costly computing resources. In this work, we address these problems by quantizing the network, both weights and activations, into multiple binary codes {-1,+1}. We formulate the quantization as an optimization problem. Under the key observation that once the quantization coefficients are fixed the binary codes can be derived efficiently by binary search tree, alternating minimization is then applied. We test the quantization for two well-known RNNs, i.e., long short term memory (LSTM) and gated recurrent unit (GRU), on the language models. Compared with the full-precision counter part, by 2-bit quantization we can achieve ~16x memory saving and ~6x real inference acceleration on CPUs, with only a reasonable loss in the accuracy. By 3-bit quantization, we can achieve almost no loss in the accuracy or even surpass the original model, with ~10.5x memory saving and ~3x real inference acceleration. Both results beat the exiting quantization works with large margins. We extend our alternating quantization to image classification tasks. In both RNNs and feedforward neural networks, the method also achieves excellent performance.", "output": [ "We propose a new quantization method and apply it to quantize RNNs for both compression and acceleration", "This paper proposes a multi-bit quantization method for recurrent neural networks.", "A technique for quantizing neural network weight matrices, and an alternating optimization procedure to estimate the set of k binary vectors and coefficients that best represent the original vector." ] }, { "id": "task668-317c39aa30c041a3803518adf6bc3ca1", "input": "Modern deep artificial neural networks have achieved impressive results through models with very large capacity---compared to the number of training examples---that control overfitting with the help of different forms of regularization. Regularization can be implicit, as is the case of stochastic gradient descent or parameter sharing in convolutional layers, or explicit. Most common explicit regularization techniques, such as dropout and weight decay, reduce the effective capacity of the model and typically require the use of deeper and wider architectures to compensate for the reduced capacity. Although these techniques have been proven successful in terms of results, they seem to waste capacity. In contrast, data augmentation techniques reduce the generalization error by increasing the number of training examples and without reducing the effective capacity. In this paper we systematically analyze the effect of data augmentation on some popular architectures and conclude that data augmentation alone---without any other explicit regularization techniques---can achieve the same performance or higher as regularized models, especially when training with fewer examples.", "output": [ "In a deep convolutional neural network trained with sufficient level of data augmentation, optimized by SGD, explicit regularizers (weight decay and dropout) might not provide any additional generalization improvement.", "This paper proposes data augmentation as an alternative to commonly used regularisation techniques, and shows that for a few reference models/tasks that the same generalization performance can be achived using only data augmentation.", "This paper presents a systematic study of data augmentation in image classification with deep neural networks, suggesting that data augmentation can replicit some common regularizers like weight decay and dropout." ] }, { "id": "task668-a99965e02c1743ba9db0800f22256876", "input": "Recent work on explanation generation for decision-making problems has viewed the explanation process as one of model reconciliation where an AI agent brings the human mental model (of its capabilities, beliefs, and goals) to the same page with regards to a task at hand. This formulation succinctly captures many possible types of explanations, as well as explicitly addresses the various properties -- e.g. the social aspects, contrastiveness, and selectiveness -- of explanations studied in social sciences among human-human interactions. However, it turns out that the same process can be hijacked into producing \"alternative explanations\" -- i.e. explanations that are not true but still satisfy all the properties of a proper explanation. In previous work, we have looked at how such explanations may be perceived by the human in the loop and alluded to one possible way of generating them. In this paper, we go into more details of this curious feature of the model reconciliation process and discuss similar implications to the overall notion of explainable decision-making.", "output": [ "Model Reconciliation is an established framework for plan explanations, but can be easily hijacked to produce lies." ] }, { "id": "task668-0dfd3cecdf714fe6a03b8749bf8fdeaa", "input": "Graph convolutional networks (GCNs) are powerful deep neural networks for graph-structured data. However, GCN computes nodes' representation recursively from their neighbors, making the receptive field size grow exponentially with the number of layers. Previous attempts on reducing the receptive field size by subsampling neighbors do not have any convergence guarantee, and their receptive field size per node is still in the order of hundreds. In this paper, we develop a preprocessing strategy and two control variate based algorithms to further reduce the receptive field size. Our algorithms are guaranteed to converge to GCN's local optimum regardless of the neighbor sampling size. Empirical results show that our algorithms have a similar convergence speed per epoch with the exact algorithm even using only two neighbors per node. The time consumption of our algorithm on the Reddit dataset is only one fifth of previous neighbor sampling algorithms.", "output": [ "A control variate based stochastic training algorithm for graph convolutional networks that the receptive field can be only two neighbors per node." ] }, { "id": "task668-ad0ceb73261e48d8a7d9c87ad5c791f0", "input": "Chemical information extraction is to convert chemical knowledge in text into true chemical database, which is a text processing task heavily relying on chemical compound name identification and standardization. Once a systematic name for a chemical compound is given, it will naturally and much simply convert the name into the eventually required molecular formula. However, for many chemical substances, they have been shown in many other names besides their systematic names which poses a great challenge for this task. In this paper, we propose a framework to do the auto standardization from the non-systematic names to the corresponding systematic names by using the spelling error correction, byte pair encoding tokenization and neural sequence to sequence model. Our framework is trained end to end and is fully data-driven. Our standardization accuracy on the test dataset achieves 54.04% which has a great improvement compared to previous state-of-the-art result.", "output": [ "We designed an end-to-end framework using sequence to sequence model to do the chemical names standardization.", "Standardizes non systematic names in chemical information extraction by creating a parallel corpus of non-systematic and systematic names and building a seq2seq model.", "This work presents a method to translate non-systematic names of chemical compounds into their systematic equivalents using a combination of mechanisms" ] }, { "id": "task668-a9bac7a32eea440db66df2487aa326bc", "input": "We propose order learning to determine the order graph of classes, representing ranks or priorities, and classify an object instance into one of the classes. To this end, we design a pairwise comparator to categorize the relationship between two instances into one of three cases: one instance is `greater than,' `similar to,' or `smaller than' the other. Then, by comparing an input instance with reference instances and maximizing the consistency among the comparison results, the class of the input can be estimated reliably. We apply order learning to develop a facial age estimator, which provides the state-of-the-art performance. Moreover, the performance is further improved when the order graph is divided into disjoint chains using gender and ethnic group information or even in an unsupervised manner.", "output": [ "The notion of order learning is proposed and it is applied to regression problems in computer vision" ] }, { "id": "task668-37222012205e4381bd44a73c9415cfb3", "input": "Flies and mice are species separated by 600 million years of evolution, yet have evolved olfactory systems that share many similarities in their anatomic and functional organization. What functions do these shared anatomical and functional features serve, and are they optimal for odor sensing? In this study, we address the optimality of evolutionary design in olfactory circuits by studying artificial neural networks trained to sense odors. We found that artificial neural networks quantitatively recapitulate structures inherent in the olfactory system, including the formation of glomeruli onto a compression layer and sparse and random connectivity onto an expansion layer. Finally, we offer theoretical justifications for each result. Our work offers a framework to explain the evolutionary convergence of olfactory circuits, and gives insight and logic into the anatomic and functional structure of the olfactory system.", "output": [ "Artificial neural networks evolved the same structures present in the olfactory systems of flies and mice after being trained to classify odors" ] }, { "id": "task668-91b83d8042f842d3a2768cccaebdb6fa", "input": "Multi-domain learning (MDL) aims at obtaining a model with minimal average risk across multiple domains. Our empirical motivation is automated microscopy data, where cultured cells are imaged after being exposed to known and unknown chemical perturbations, and each dataset displays significant experimental bias. This paper presents a multi-domain adversarial learning approach, MuLANN, to leverage multiple datasets with overlapping but distinct class sets, in a semi-supervised setting. Our contributions include: i) a bound on the average- and worst-domain risk in MDL, obtained using the H-divergence; ii) a new loss to accommodate semi-supervised multi-domain learning and domain adaptation; iii) the experimental validation of the approach, improving on the state of the art on two standard image benchmarks, and a novel bioimage dataset, Cell.", "output": [ "Adversarial Domain adaptation and Multi-domain learning: a new loss to handle multi- and single-domain classes in the semi-supervised setting." ] }, { "id": "task668-386ca2c85fa149478bdc58a98d319a31", "input": "The driving force behind the recent success of LSTMs has been their ability to learn complex and non-linear relationships. Consequently, our inability to describe these relationships has led to LSTMs being characterized as black boxes. To this end, we introduce contextual decomposition (CD), an interpretation algorithm for analysing individual predictions made by standard LSTMs, without any changes to the underlying model. By decomposing the output of a LSTM, CD captures the contributions of combinations of words or variables to the final prediction of an LSTM. On the task of sentiment analysis with the Yelp and SST data sets, we show that CD is able to reliably identify words and phrases of contrasting sentiment, and how they are combined to yield the LSTM's final prediction. Using the phrase-level labels in SST, we also demonstrate that CD is able to successfully extract positive and negative negations from an LSTM, something which has not previously been done.", "output": [ "We introduce contextual decompositions, an interpretation algorithm for LSTMs capable of extracting word, phrase and interaction-level importance score" ] }, { "id": "task668-d89c164d15c142d58718f22cf7379d63", "input": "We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption that learning from different tasks, sharing common properties, is helpful to generalize the knowledge of them resulting in a more effective feature extraction compared to learning a single task. Intuitively, the resulting set of features offers performance benefits when used by Reinforcement Learning algorithms. We prove this by providing theoretical guarantees that highlight the conditions for which is convenient to share representations among tasks, extending the well-known finite-time bounds of Approximate Value-Iteration to the multi-task setting. In addition, we complement our analysis by proposing multi-task extensions of three Reinforcement Learning algorithms that we empirically evaluate on widely used Reinforcement Learning benchmarks showing significant improvements over the single-task counterparts in terms of sample efficiency and performance.", "output": [ "A study on the benefit of sharing representation in Multi-Task Reinforcement Learning." ] }, { "id": "task668-fe0276d28da54a4bbf275cd2ce38ef43", "input": "The need for large amounts of training image data with clearly defined features is a major obstacle to applying generative adversarial networks(GAN) on image generation where training data is limited but diverse, since insufficient latent feature representation in the already scarce data often leads to instability and mode collapse during GAN training. To overcome the hurdle of limited data when applying GAN to limited datasets, we propose in this paper the strategy of \\textit{parallel recurrent data augmentation}, where the GAN model progressively enriches its training set with sample images constructed from GANs trained in parallel at consecutive training epochs. Experiments on a variety of small yet diverse datasets demonstrate that our method, with little model-specific considerations, produces images of better quality as compared to the images generated without such strategy. The source code and generated images of this paper will be made public after review.", "output": [ "We introduced a novel, simple, and efficient data augmentation method that boosts the performances of existing GANs when training data is limited and diverse. " ] }, { "id": "task668-aa88be0cd9e34199a6dfb9d45138abd4", "input": "Deep neural networks (DNNs) are widely adopted in real-world cognitive applications because of their high accuracy. The robustness of DNN models, however, has been recently challenged by adversarial attacks where small disturbance on input samples may result in misclassification. State-of-the-art defending algorithms, such as adversarial training or robust optimization, improve DNNs' resilience to adversarial attacks by paying high computational costs. Moreover, these approaches are usually designed to defend one or a few known attacking techniques only. The effectiveness to defend other types of attacking methods, especially those that have not yet been discovered or explored, cannot be guaranteed. This work aims for a general approach of enhancing the robustness of DNN models under adversarial attacks. In particular, we propose Bamboo -- the first data augmentation method designed for improving the general robustness of DNN without any hypothesis on the attacking algorithms. Bamboo augments the training data set with a small amount of data uniformly sampled on a fixed radius ball around each training data and hence, effectively increase the distance between natural data points and decision boundary. Our experiments show that Bamboo substantially improve the general robustness against arbitrary types of attacks and noises, achieving better results comparing to previous adversarial training methods, robust optimization methods and other data augmentation methods with the same amount of data points.", "output": [ "The first data augmentation method specially designed for improving the general robustness of DNN without any hypothesis on the attacking algorithms.", "Proposes a data augmentation training method to gain model robustness against adversarial perturbations, by augmenting uniformly random samples from a fixed-radius sphere centered at training data. " ] }, { "id": "task668-efd8635eeefb4eaf9d09981af6729290", "input": "Some recent work has shown separation between the expressive power of depth-2 and depth-3 neural networks. These separation results are shown by constructing functions and input distributions, so that the function is well-approximable by a depth-3 neural network of polynomial size but it cannot be well-approximated under the chosen input distribution by any depth-2 neural network of polynomial size. These results are not robust and require carefully chosen functions as well as input distributions.\n\n We show a similar separation between the expressive power of depth-2 and depth-3 sigmoidal neural networks over a large class of input distributions, as long as the weights are polynomially bounded. While doing so, we also show that depth-2 sigmoidal neural networks with small width and small weights can be well-approximated by low-degree multivariate polynomials.", "output": [ "depth-2-vs-3 separation for sigmoidal neural networks over general distributions" ] }, { "id": "task668-21b87e1b5cb940cd9e0d62a1c5923cc9", "input": "To simultaneously capture syntax and semantics from a text corpus, we propose a new larger-context language model that extracts recurrent hierarchical semantic structure via a dynamic deep topic model to guide natural language generation. Moving beyond a conventional language model that ignores long-range word dependencies and sentence order, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence topic dependences. For inference, we develop a hybrid of stochastic-gradient MCMC and recurrent autoencoding variational Bayes. Experimental results on a variety of real-world text corpora demonstrate that the proposed model not only outperforms state-of-the-art larger-context language models, but also learns interpretable recurrent multilayer topics and generates diverse sentences and paragraphs that are syntactically correct and semantically coherent.", "output": [ "We introduce a novel larger-context language model to simultaneously captures syntax and semantics, making it capable of generating highly interpretable sentences and paragraphs" ] }, { "id": "task668-7cf4ab0365554de49efafc259021cad7", "input": "This paper presents a method to autonomously find periodicities in a signal. It is based on the same idea of using Fourier Transform and autocorrelation function presented in Vlachos et al. 2005. While showing interesting results this method does not perform well on noisy signals or signals with multiple periodicities. Thus, our method adds several new extra steps (hints clustering, filtering and detrending) to fix these issues. Experimental results show that the proposed method outperforms the state of the art algorithms.", "output": [ "This paper presents a method to autonomously find multiple periodicities in a signal, using FFT and ACF and add three news steps (clustering/filtering/detrending)" ] }, { "id": "task668-43206efa9ae54a59891975f49cd2d5de", "input": "Neural models achieved considerable improvement for many natural language processing tasks, but they offer little transparency, and interpretability comes at a cost. In some domains, automated predictions without justifications have limited applicability. Recently, progress has been made regarding single-aspect sentiment analysis for reviews, where the ambiguity of a justification is minimal. In this context, a justification, or mask, consists of (long) word sequences from the input text, which suffice to make the prediction. Existing models cannot handle more than one aspect in one training and induce binary masks that might be ambiguous. In our work, we propose a neural model for predicting multi-aspect sentiments for reviews and generates a probabilistic multi-dimensional mask (one per aspect) simultaneously, in an unsupervised and multi-task learning manner. Our evaluation shows that on three datasets, in the beer and hotel domain, our model outperforms strong baselines and generates masks that are: strong feature predictors, meaningful, and interpretable.", "output": [ "Neural model predicting multi-aspect sentiments and generating a probabilistic multi-dimensional mask simultaneously. Model outperforms strong baselines and generates masks that are: strong feature predictors, meaningful, and interpretable." ] }, { "id": "task668-0fcc161d841b44358bf5142dd81d0f68", "input": "Exploration in environments with sparse rewards is a key challenge for reinforcement learning. How do we design agents with generic inductive biases so that they can explore in a consistent manner instead of just using local exploration schemes like epsilon-greedy? We propose an unsupervised reinforcement learning agent which learns a discrete pixel grouping model that preserves spatial geometry of the sensors and implicitly of the environment as well. We use this representation to derive geometric intrinsic reward functions, like centroid coordinates and area, and learn policies to control each one of them with off-policy learning. These policies form a basis set of behaviors (options) which allows us explore in a consistent way and use them in a hierarchical reinforcement learning setup to solve for extrinsically defined rewards. We show that our approach can scale to a variety of domains with competitive performance, including navigation in 3D environments and Atari games with sparse rewards.", "output": [ "structured exploration in deep reinforcement learning via unsupervised visual abstraction discovery and control", "The paper introduces visual abstractions that are used for reinforcement learning, where an algorithm learns to \"control\" each abstraction as well as select the options to achieve the overall task." ] }, { "id": "task668-42eb1ad65fbc4f0c9422acf93426a038", "input": "Over the last decade, two competing control strategies have emerged for solving complex control tasks with high efficacy. Model-based control algorithms, such as model-predictive control (MPC) and trajectory optimization, peer into the gradients of underlying system dynamics in order to solve control tasks with high sample efficiency. However, like all gradient-based numerical optimization methods,model-based control methods are sensitive to intializations and are prone to becoming trapped in local minima. Deep reinforcement learning (DRL), on the other hand, can somewhat alleviate these issues by exploring the solution space through sampling — at the expense of computational cost. In this paper, we present a hybrid method that combines the best aspects of gradient-based methods and DRL. We base our algorithm on the deep deterministic policy gradients (DDPG) algorithm and propose a simple modification that uses true gradients from a differentiable physical simulator to increase the convergence rate of both the actor and the critic. We demonstrate our algorithm on seven 2D robot control tasks, with the most complex one being a differentiable half cheetah with hard contact constraints. Empirical results show that our method boosts the performance of DDPGwithout sacrificing its robustness to local minima.", "output": [ "We propose a novel method that leverages the gradients from differentiable simulators to improve the performance of RL for robotics control" ] }, { "id": "task668-4e1fe23a6a87493eb7b2e323e78e51c5", "input": "This work addresses the long-standing problem of robust event localization in the presence of temporally of misaligned labels in the training data. We propose a novel versatile loss function that generalizes a number of training regimes from standard fully-supervised cross-entropy to count-based weakly-supervised learning. Unlike classical models which are constrained to strictly fit the annotations during training, our soft localization learning approach relaxes the reliance on the exact position of labels instead. Training with this new loss function exhibits strong robustness to temporal misalignment of labels, thus alleviating the burden of precise annotation of temporal sequences. We demonstrate state-of-the-art performance against standard benchmarks in a number of challenging experiments and further show that robustness to label noise is not achieved at the expense of raw performance.", "output": [ "This work introduces a novel loss function for the robust training of temporal localization DNN in the presence of misaligned labels.", "A new loss for training models that predict where events occur in a training sequence with noisy labels by comparing smoothed label and prediction sequence." ] }, { "id": "task668-4f3b57f6e2584c07ae41df487c35bead", "input": "Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, molecular regression problems, and global weather and climate modelling. A naive application of convolutional networks to a planar projection of the spherical signal is destined to fail, because the space-varying distortions introduced by such a projection will make translational weight sharing ineffective.\n\n In this paper we introduce the building blocks for constructing spherical CNNs. We propose a definition for the spherical cross-correlation that is both expressive and rotation-equivariant. The spherical correlation satisfies a generalized Fourier theorem, which allows us to compute it efficiently using a generalized (non-commutative) Fast Fourier Transform (FFT) algorithm. We demonstrate the computational efficiency, numerical accuracy, and effectiveness of spherical CNNs applied to 3D model recognition and atomization energy regression.", "output": [ "We introduce Spherical CNNs, a convolutional network for spherical signals, and apply it to 3D model recognition and molecular energy regression.", "The paper proposes a framework for constructing spherical convolutional networks based on a novel synthesis of several existing concepts", "This paper focuses on how to extend convolutional neural networks to have built-in spherical invariance, and adapts tools from non-Abelian harmonic analysis to achieve this goal.", "The authors develop a novel scheme for representing spherical data from the ground up" ] }, { "id": "task668-5c74584397274d4a85ab8bc14d1301b4", "input": "Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another. Although recent feed-forward neural networks can generate stylized images in real-time, these models produce a single stylization given a pair of style/content images, and the user doesn't have control over the synthesized output. Moreover, the style transfer depends on the hyper-parameters of the model with varying ``optimum\" for different input images. Therefore, if the stylized output is not appealing to the user, she/he has to try multiple models or retrain one with different hyper-parameters to get a favorite stylization. In this paper, we address these issues by proposing a novel method which allows adjustment of crucial hyper-parameters, after the training and in real-time, through a set of manually adjustable parameters. These parameters enable the user to modify the synthesized outputs from the same pair of style/content images, in search of a favorite stylized image. Our quantitative and qualitative experiments indicate how adjusting these parameters is comparable to retraining the model with different hyper-parameters. We also demonstrate how these parameters can be randomized to generate results which are diverse but still very similar in style and content.", "output": [ "Stochastic style transfer with adjustable features. " ] }, { "id": "task668-ec768e60b7644f6d8a11992bb98847c7", "input": "This paper presents the formal release of {\\em MedMentions}, a new manually annotated resource for the recognition of biomedical concepts. What distinguishes MedMentions from other annotated biomedical corpora is its size (over 4,000 abstracts and over 350,000 linked mentions), as well as the size of the concept ontology (over 3 million concepts from UMLS 2017) and its broad coverage of biomedical disciplines. In addition to the full corpus, a sub-corpus of MedMentions is also presented, comprising annotations for a subset of UMLS 2017 targeted towards document retrieval. To encourage research in Biomedical Named Entity Recognition and Linking, data splits for training and testing are included in the release, and a baseline model and its metrics for entity linking are also described.", "output": [ "The paper introduces a new gold-standard corpus corpus of biomedical scientific literature manually annotated with UMLS concept mentions.", "Details the construction of a manually annotated dataset covering biomedical concepts that is larger and covered by a larger ontology than previous datasets.", "This paper uses MedMentions, a TaggerOne semi-Markov model for end-to-end concept recognition and linking on a set of Pubmed abstracts to label papers with biomedical concepts/entities" ] }, { "id": "task668-f8818909e08d49adb14488d698aae49b", "input": "In real-world machine learning applications, large outliers and pervasive noise are commonplace, and access to clean training data as required by standard deep autoencoders is unlikely.\n Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis. Autoencoder neural networks learn to reconstruct normal images, and hence can classify those images as anomalous if the reconstruction error exceeds some threshold. In this paper, we proposed an unsupervised method based on subset scanning over autoencoder activations. The contributions of our work are threefold. First, we propose a novel method combining detection with reconstruction error and subset scanning scores to improve the anomaly score of current autoencoders without requiring any retraining. Second, we provide the ability to inspect and visualize the set of anomalous nodes in the reconstruction error space that make a sample noised. Third, we show that subset scanning can be used for anomaly detection in the inner layers of the autoencoder. We provide detection power results for several untargeted adversarial noise models under standard datasets.", "output": [ "Unsupervised method to detect adversarial samples in autoencoder's activations and reconstruction error space" ] }, { "id": "task668-cd8b68026fd94310a40c7112c635bda0", "input": "We extend the recent results of (Arora et al., 2019) by a spectral analysis of representations corresponding to kernel and neural embeddings. They showed that in a simple single layer network, the alignment of the labels to the eigenvectors of the corresponding Gram matrix determines both the convergence of the optimization during training as well as the generalization properties. We generalize their result to kernel and neural representations and show that these extensions improve both optimization and generalization of the basic setup studied in (Arora et al., 2019).", "output": [ "Spectral analysis for understanding how different representations can improve optimization and generalization." ] }, { "id": "task668-e35ddaba5c7d4082a7b077371b4a1aa3", "input": "Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching predictions, and so on. While numerous studies have been conducted, most existing works assume that the data from different sources or across different locations are equally reliable. Due to cost, accessibility, or other factors, it is inevitable that the data quality could vary, which introduces significant biases into the model and leads to unreliable prediction results. The problem could be exacerbated in black-box prediction models, such as deep neural networks. In this paper, we propose a novel solution that can automatically infer data quality levels of different sources through local variations of spatiotemporal signals without explicit labels. Furthermore, we integrate the estimate of data quality level with graph convolutional networks to exploit their efficient structures. We evaluate our proposed method on forecasting temperatures in Los Angeles.", "output": [ "We propose a method that infers the time-varying data quality level for spatiotemporal forecasting without explicitly assigned labels.", "Introduces a new definition of data quality that relies on the notion of local variation defined in (Zhou and Scholkopf) and extends it to multiple heterogenous data sources.", "This work proposed a new way to evaluate the quality of different data sources with the time-vary graph model, with the quality level used as a regularization term in the objective function" ] }, { "id": "task668-b266d95ec02d45f4a9fa654dc0441be1", "input": "We introduce our Distribution Regression Network (DRN) which performs regression from input probability distributions to output probability distributions. Compared to existing methods, DRN learns with fewer model parameters and easily extends to multiple input and multiple output distributions. On synthetic and real-world datasets, DRN performs similarly or better than the state-of-the-art. Furthermore, DRN generalizes the conventional multilayer perceptron (MLP). In the framework of MLP, each node encodes a real number, whereas in DRN, each node encodes a probability distribution.", "output": [ "A learning network which generalizes the MLP framework to perform distribution-to-distribution regression" ] }, { "id": "task668-1349165454b9499c9d45646c964611e4", "input": "In this paper we propose to view the acceptance rate of the Metropolis-Hastings algorithm as a universal objective for learning to sample from target distribution -- given either as a set of samples or in the form of unnormalized density. This point of view unifies the goals of such approaches as Markov Chain Monte Carlo (MCMC), Generative Adversarial Networks (GANs), variational inference. To reveal the connection we derive the lower bound on the acceptance rate and treat it as the objective for learning explicit and implicit samplers. The form of the lower bound allows for doubly stochastic gradient optimization in case the target distribution factorizes (i.e. over data points). We empirically validate our approach on Bayesian inference for neural networks and generative models for images.", "output": [ "Learning to sample via lower bounding the acceptance rate of the Metropolis-Hastings algorithm" ] }, { "id": "task668-4020f29c78c4482185f12f6736a4cd71", "input": "Due to the success of residual networks (resnets) and related architectures, shortcut connections have quickly become standard tools for building convolutional neural networks. The explanations in the literature for the apparent effectiveness of shortcuts are varied and often contradictory. We hypothesize that shortcuts work primarily because they act as linear counterparts to nonlinear layers. We test this hypothesis by using several variations on the standard residual block, with different types of linear connections, to build small (100k--1.2M parameter) image classification networks. Our experiments show that other kinds of linear connections can be even more effective than the identity shortcuts. Our results also suggest that the best type of linear connection for a given application may depend on both network width and depth.", "output": [ "We generalize residual blocks to tandem blocks, which use arbitrary linear maps instead of shortcuts, and improve performance over ResNets.", "This paper performs an analysis of shortcut connections in ResNet-like architectures, and proposes to substitute the identity shortcuts with an alternative convolutional one referred to as tandem block.", "This paper investigates the effect of replacing identity skip connections with trainable convolutional skip connections in ResNet and finds that performance improves." ] }, { "id": "task668-564401feab024b88b72b0ca73392cb60", "input": "Background: Statistical mechanics results (Dauphin et al. (2014); Choromanska et al. (2015)) suggest that local minima with high error are exponentially rare in high dimensions. However, to prove low error guarantees for Multilayer Neural Networks (MNNs), previous works so far required either a heavily modified MNN model or training method, strong assumptions on the labels (e.g., “near” linear separability), or an unrealistically wide hidden layer with \\Omega\\(N) units. \n\n Results: We examine a MNN with one hidden layer of piecewise linear units, a single output, and a quadratic loss. We prove that, with high probability in the limit of N\\rightarrow\\infty datapoints, the volume of differentiable regions of the empiric loss containing sub-optimal differentiable local minima is exponentially vanishing in comparison with the same volume of global minima, given standard normal input of dimension d_0=\\tilde{\\Omega}(\\sqrt{N}), and a more realistic number of d_1=\\tilde{\\Omega}(N/d_0) hidden units. We demonstrate our results numerically: for example, 0% binary classification training error on CIFAR with only N/d_0 = 16 hidden neurons.", "output": [ "\"Bad\" local minima are vanishing in a multilayer neural net: a proof with more reasonable assumptions than before", "In networks with a single hidden layer, the volume of suboptimal local minima exponentially decreases in comparison to global minima.", "This paper aims to answer why standard SGD based algorithms on neural network converge to 'good' solutions." ] }, { "id": "task668-b460d53faedd4c76b0a0811c06c5c0b9", "input": "Interpretability and small labelled datasets are key issues in the practical application of deep learning, particularly in areas such as medicine. In this paper, we present a semi-supervised technique that addresses both these issues simultaneously. We learn dense representations from large unlabelled image datasets, then use those representations to both learn classifiers from small labeled sets and generate visual rationales explaining the predictions. Using chest radiography diagnosis as a motivating application, we show our method has good generalization ability by learning to represent our chest radiography dataset while training a classifier on an separate set from a different institution. Our method identifies heart failure and other thoracic diseases. For each prediction, we generate visual rationales for positive classifications by optimizing a latent representation to minimize the probability of disease while constrained by a similarity measure in image space. Decoding the resultant latent representation produces an image without apparent disease. The difference between the original and the altered image forms an interpretable visual rationale for the algorithm's prediction. Our method simultaneously produces visual rationales that compare favourably to previous techniques and a classifier that outperforms the current state-of-the-art.", "output": [ "We propose a method of using GANs to generate high quality visual rationales to help explain model predictions. " ] }, { "id": "task668-cc4b54c534d545ef899241d37f1029ed", "input": "Deep learning software demands reliability and performance. However, many of the existing deep learning frameworks are software libraries that act as an unsafe DSL in Python and a computation graph interpreter. We present DLVM, a design and implementation of a compiler infrastructure with a linear algebra intermediate representation, algorithmic differentiation by adjoint code generation, domain- specific optimizations and a code generator targeting GPU via LLVM. Designed as a modern compiler infrastructure inspired by LLVM, DLVM is more modular and more generic than existing deep learning compiler frameworks, and supports tensor DSLs with high expressivity. With our prototypical staged DSL embedded in Swift, we argue that the DLVM system enables a form of modular, safe and performant frameworks for deep learning.", "output": [ "We introduce a novel compiler infrastructure that addresses shortcomings of existing deep learning frameworks.", "Proposal to move from ad-hoc code generation in deep learning engines to compiler and languages best practices.", "This paper presents a compiler framework that allows definition of domain-specific languages for deep learning systems, and defines compilation stages that can take advantage of standard optimizations and specialized optimizations for neural networks.", "This paper introduces a DLVM to take advantage of the compiler aspects of a tensor compiler" ] }, { "id": "task668-ab391d1fd4aa4ec78a75426445775cd0", "input": "Stochastic gradient descent (SGD) has been the dominant optimization method for training deep neural networks due to its many desirable properties. One of the more remarkable and least understood quality of SGD is that it generalizes relatively well\n on unseen data even when the neural network has millions of parameters. We hypothesize that in certain cases it is desirable to relax its intrinsic generalization properties and introduce an extension of SGD called deep gradient boosting (DGB). The key idea of DGB is that back-propagated gradients inferred using the chain rule can be viewed as pseudo-residual targets of a gradient boosting problem. Thus at each layer of a neural network the weight update is calculated by solving the corresponding boosting problem using a linear base learner. The resulting weight update formula can also be viewed as a normalization procedure of the data that arrives at each layer during the forward pass. When implemented as a separate input normalization layer (INN) the new architecture shows improved performance on image recognition tasks when compared to the same architecture without normalization layers. As opposed to batch normalization (BN), INN has no learnable parameters however it matches its performance on CIFAR10 and ImageNet classification tasks.", "output": [ "What can we learn about training neural networks if we treat each layer as a gradient boosting problem?" ] }, { "id": "task668-eaff6b3ed36b4668800c41ab02647bda", "input": "All living organisms struggle against the forces of nature to carve out niches where\n they can maintain relative stasis. We propose that such a search for order amidst\n chaos might offer a unifying principle for the emergence of useful behaviors in\n artificial agents. We formalize this idea into an unsupervised reinforcement learning\n method called surprise minimizing RL (SMiRL). SMiRL trains an agent with the\n objective of maximizing the probability of observed states under a model trained on\n all previously seen states. The resulting agents acquire several proactive behaviors\n to seek and maintain stable states such as balancing and damage avoidance, that\n are closely tied to the affordances of the environment and its prevailing sources\n of entropy, such as winds, earthquakes, and other agents. We demonstrate that\n our surprise minimizing agents can successfully play Tetris, Doom, and control\n a humanoid to avoid falls, without any task-specific reward supervision. We\nfurther show that SMiRL can be used as an unsupervised pre-training objective\n that substantially accelerates subsequent reward-driven learning", "output": [ "Learning emergent behavior by minimizing Bayesian surprise with RL in natural environments with entropy." ] }, { "id": "task668-0dce0f672435474598c8203a7fba5a7a", "input": "The paper proposes and demonstrates a Deep Convolutional Neural Network (DCNN) architecture to identify users with disguised face attempting a fraudulent ATM transaction. The recent introduction of Disguised Face Identification (DFI) framework proves the applicability of deep neural networks for this very problem. All the ATMs nowadays incorporate a hidden camera in them and capture the footage of their users. However, it is impossible for the police to track down the impersonators with disguised faces from the ATM footage. The proposed deep convolutional neural network is trained to identify, in real time, whether the user in the captured image is trying to cloak his identity or not. The output of the DCNN is then reported to the ATM to take appropriate steps and prevent the swindler from completing the transaction. The network is trained using a dataset of images captured in similar situations as of an ATM. The comparatively low background clutter in the images enables the network to demonstrate high accuracy in feature extraction and classification for all the different disguises.", "output": [ "Proposed System can prevent impersonators with facial disguises from completing a fraudulent transaction using a pre-trained DCNN." ] }, { "id": "task668-3ab566d2ff0244fc92d7e9f558889781", "input": "As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability. More recently, absorbing the idea of mining-based strategies is adopted to emphasize the misclassified samples and achieve promising results. However, during the entire training process, the prior methods either do not explicitly emphasize the sample based on its importance that renders the hard samples not fully exploited or explicitly emphasize the effects of semi-hard/hard samples even at the early training stage that may lead to convergence issues. In this work, we propose a novel Adaptive Curriculum Learning loss (CurricularFace) that embeds the idea of curriculum learning into the loss function to achieve a novel training strategy for deep face recognition, which mainly addresses easy samples in the early training stage and hard ones in the later stage. Specifically, our CurricularFace adaptively adjusts the relative importance of easy and hard samples during different training stages. In each stage, different samples are assigned with different importance according to their corresponding difficultness. Extensive experimental results on popular benchmarks demonstrate the superiority of our CurricularFace over the state-of-the-art competitors. Code will be available upon publication.", "output": [ "A novel Adaptive Curriculum Learning loss for deep face recognition" ] }, { "id": "task668-1bf1806c33a747f5b79fb678fb5b6022", "input": "We analyze the joint probability distribution on the lengths of the\n vectors of hidden variables in different layers of a fully connected\n deep network, when the weights and biases are chosen randomly according to\n Gaussian distributions, and the input is binary-valued. We show\n that, if the activation function satisfies a minimal set of\n assumptions, satisfied by all activation functions that we know that\n are used in practice, then, as the width of the network gets large,\n the ``length process'' converges in probability to a length map\n that is determined as a simple function of the variances of the\n random weights and biases, and the activation function.\n\n We also show that this convergence may fail for activation functions \n that violate our assumptions.", "output": [ "We prove that, for activation functions satisfying some conditions, as a deep network gets wide, the lengths of the vectors of hidden variables converge to a length map." ] }, { "id": "task668-d122bb008493430e9d3af07601543505", "input": "In many applications labeled data is not readily available, and needs to be collected via pain-staking human supervision.\n We propose a rule-exemplar model for collecting human supervision to combine the scalability of rules with the quality of instance labels. The supervision is coupled such that it is both natural for humans and synergistic for learning. We propose a training algorithm that jointly denoises rules via latent coverage variables, and trains the model through a soft implication loss over the coverage and label variables. Empirical evaluation on five different tasks shows that (1) our algorithm is more accurate than several existing methods of learning from a mix of clean and noisy supervision, and (2) \nthe coupled rule-exemplar supervision is effective in denoising rules.", "output": [ "Coupled rule-exemplar supervision and a implication loss helps to jointly learn to denoise rules and imply labels." ] }, { "id": "task668-c38d99f63ac24fc3b479ebe1d8ae19e0", "input": "Within many machine learning algorithms, a fundamental problem concerns efficient calculation of an unbiased gradient wrt parameters $\\boldsymbol{\\gamma}$ for expectation-based objectives $\\mathbb{E}_{q_{\\boldsymbol{\\gamma}} (\\boldsymbol{y})} [f (\\boldsymbol{y}) ]$. Most existing methods either ($i$) suffer from high variance, seeking help from (often) complicated variance-reduction techniques; or ($ii$) they only apply to reparameterizable continuous random variables and employ a reparameterization trick. To address these limitations, we propose a General and One-sample (GO) gradient that ($i$) applies to many distributions associated with non-reparameterizable continuous {\\em or} discrete random variables, and ($ii$) has the same low-variance as the reparameterization trick. We find that the GO gradient often works well in practice based on only one Monte Carlo sample (although one can of course use more samples if desired). Alongside the GO gradient, we develop a means of propagating the chain rule through distributions, yielding statistical back-propagation, coupling neural networks to common random variables.", "output": [ "a Rep-like gradient for non-reparameterizable continuous/discrete distributions; further generalized to deep probabilistic models, yielding statistical back-propagation", "Presents a gradient estimator for expectation-based objectives that is unbiased, has low variance, and applies to either continuous and discrete random variables.", "An improved method for computing derivates of the expectation, and a new gradient estimator of low variance that allows training of generative models in which observations or latent variables are discrete.", "Designs a low variance gradient for distributions associated with continuous or discrete random variables." ] }, { "id": "task668-fdb333a02bad487ab54fa988f0493d01", "input": "A long-held conventional wisdom states that larger models train more slowly when using gradient descent. This work challenges this widely-held belief, showing that larger models can potentially train faster despite the increasing computational requirements of each training step. In particular, we study the effect of network structure (depth and width) on halting time and show that larger models---wider models in particular---take fewer training steps to converge.\n\n We design simple experiments to quantitatively characterize the effect of overparametrization on weight space traversal. Results show that halting time improves when growing model's width for three different applications, and the improvement comes from each factor: The distance from initialized weights to converged weights shrinks with a power-law-like relationship, the average step size grows with a power-law-like relationship, and gradient vectors become more aligned with each other during traversal.\n", "output": [ "Empirically shows that larger models train in fewer training steps, because all factors in weight space traversal improve.", "This paper shows that wider RNNs improve convergence speed when applied to NLP problems, and by extension the effect of increasing the widths in deep neural networks on the convergence of optimization", "This paper characterizes the impact of over-parametrization in the number of iterations it takes an algorithm to converge, and presents further empirical observations on the effects of over-parametrization in neural network training." ] }, { "id": "task668-32e1adedcb164b87a42b0aa188dac4ba", "input": "Deep learning has made remarkable achievement in many fields. However, learning\n the parameters of neural networks usually demands a large amount of labeled\n data. The algorithms of deep learning, therefore, encounter difficulties when applied\n to supervised learning where only little data are available. This specific task\n is called few-shot learning. To address it, we propose a novel algorithm for fewshot\n learning using discrete geometry, in the sense that the samples in a class are\n modeled as a reduced simplex. The volume of the simplex is used for the measurement\n of class scatter. During testing, combined with the test sample and the\n points in the class, a new simplex is formed. Then the similarity between the test\n sample and the class can be quantized with the ratio of volumes of the new simplex\n to the original class simplex. Moreover, we present an approach to constructing\n simplices using local regions of feature maps yielded by convolutional neural networks.\n Experiments on Omniglot and miniImageNet verify the effectiveness of\n our simplex algorithm on few-shot learning.", "output": [ "A simplex-based geometric method is proposed to cope with few-shot learning problems." ] }, { "id": "task668-79076edaa10a4ec0acfb41e610a90ea9", "input": "Suppose a deep classification model is trained with samples that need to be kept private for privacy or confidentiality reasons. In this setting, can an adversary obtain the private samples if the classification model is given to the adversary? We call this reverse engineering against the classification model the Classifier-to-Generator (C2G) Attack. This situation arises when the classification model is embedded into mobile devices for offline prediction (e.g., object recognition for the automatic driving car and face recognition for mobile phone authentication).\n For C2G attack, we introduce a novel GAN, PreImageGAN. In PreImageGAN, the generator is designed to estimate the the sample distribution conditioned by the preimage of classification model $f$, $P(X|f(X)=y)$, where $X$ is the random variable on the sample space and $y$ is the probability vector representing the target label arbitrary specified by the adversary. In experiments, we demonstrate PreImageGAN works successfully with hand-written character recognition and face recognition. In character recognition, we show that, given a recognition model of hand-written digits, PreImageGAN allows the adversary to extract alphabet letter images without knowing that the model is built for alphabet letter images. In face recognition, we show that, when an adversary obtains a face recognition model for a set of individuals, PreImageGAN allows the adversary to extract face images of specific individuals contained in the set, even when the adversary has no knowledge of the face of the individuals.", "output": [ "Estimation of training data distribution from trained classifier using GAN." ] }, { "id": "task668-15f9f80e639f47089206db8a7af1786f", "input": "Recurrent models for sequences have been recently successful at many tasks, especially for language modeling\n and machine translation. Nevertheless, it remains challenging to extract good representations from\n these models. For instance, even though language has a clear hierarchical structure going from characters\n through words to sentences, it is not apparent in current language models.\n We propose to improve the representation in sequence models by\n augmenting current approaches with an autoencoder that is forced to compress\n the sequence through an intermediate discrete latent space. In order to propagate gradients\n though this discrete representation we introduce an improved semantic hashing technique.\n We show that this technique performs well on a newly proposed quantitative efficiency measure.\n We also analyze latent codes produced by the model showing how they correspond to\n words and phrases. Finally, we present an application of the autoencoder-augmented\n model to generating diverse translations.", "output": [ "Autoencoders for text with a new method for using discrete latent space." ] }, { "id": "task668-3fff0e061141418a8abf14d6a0a37b4e", "input": "Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack. Such adaptation property strongly relies on cellular neuromodulation, the biological mechanism that dynamically controls neuron intrinsic properties and response to external stimuli in a context dependent manner. In this paper, we take inspiration from cellular neuromodulation to construct a new deep neural network architecture that is specifically designed to learn adaptive behaviours. The network adaptation capabilities are tested on navigation benchmarks in a meta-learning context and compared with state-of-the-art approaches. Results show that neuromodulation is capable of adapting an agent to different tasks and that neuromodulation-based approaches provide a promising way of improving adaptation of artificial systems.", "output": [ "This paper introduces neuromodulation in artificial neural networks." ] }, { "id": "task668-3597f917bfd54f65a17b72592157ea61", "input": "Graph Neural Networks (GNNs) are a class of deep models that operates on data with arbitrary topology and order-invariant structure represented as graphs. We introduce an efficient memory layer for GNNs that can learn to jointly perform graph representation learning and graph pooling. We also introduce two new networks based on our memory layer: Memory-Based Graph Neural Network (MemGNN) and Graph Memory Network (GMN) that can learn hierarchical graph representations by coarsening the graph throughout the layers of memory. The experimental results demonstrate that the proposed models achieve state-of-the-art results in six out of seven graph classification and regression benchmarks. We also show that the learned representations could correspond to chemical features in the molecule data.", "output": [ "We introduce an efficient memory layer that can learn representation and coarsen input graphs simultaneously without relying on message passing." ] }, { "id": "task668-270b13b20a9a4199889ddeb2f8b8c37e", "input": "The loss of a few neurons in a brain rarely results in any visible loss of function. However, the insight into what “few” means in this context is unclear. How many random neuron failures will it take to lead to a visible loss of function? In this paper, we address the fundamental question of the impact of the crash of a random subset of neurons on the overall computation of a neural network and the error in the output it produces. We study fault tolerance of neural networks subject to small random neuron/weight crash failures in a probabilistic setting. We give provable guarantees on the robustness of the network to these crashes. Our main contribution is a bound on the error in the output of a network under small random Bernoulli crashes proved by using a Taylor expansion in the continuous limit, where close-by neurons at a layer are similar. The failure mode we adopt in our model is characteristic of neuromorphic hardware, a promising technology to speed up artificial neural networks, as well as of biological networks. We show that our theoretical bounds can be used to compare the fault tolerance of different architectures and to design a regularizer improving the fault tolerance of a given architecture. We design an algorithm achieving fault tolerance using a reasonable number of neurons. In addition to the theoretical proof, we also provide experimental validation of our results and suggest a connection to the generalization capacity problem.", "output": [ "We give a bound for NNs on the output error in case of random weight failures using a Taylor expansion in the continuous limit where nearby neurons are similar", "This paper considers the problem of dropping neurons from a neural network, showing that if the goal is to become robust to randomly dropped neurons during evaluation, then it is sufficient to just train with dropout.", "This contribution studies the impact of deletions of random neurons on prediction accuracy of trained architecture, with the application to failure analysis and the specific context of neuromorphic hardware." ] }, { "id": "task668-bced57a577444bf68f673b5ad7cc1987", "input": "Non-autoregressive machine translation (NAT) systems predict a sequence of output tokens in parallel, achieving substantial improvements in generation speed compared to autoregressive models. Existing NAT models usually rely on the technique of knowledge distillation, which creates the training data from a pretrained autoregressive model for better performance. Knowledge distillation is empirically useful, leading to large gains in accuracy for NAT models, but the reason for this success has, as of yet, been unclear. In this paper, we first design systematic experiments to investigate why knowledge distillation is crucial to NAT training. We find that knowledge distillation can reduce the complexity of data sets and help NAT to model the variations in the output data. Furthermore, a strong correlation is observed between the capacity of an NAT model and the optimal complexity of the distilled data for the best translation quality. Based on these findings, we further propose several approaches that can alter the complexity of data sets to improve the performance of NAT models. We achieve the state-of-the-art performance for the NAT-based models, and close the gap with the autoregressive baseline on WMT14 En-De benchmark.", "output": [ "We systematically examine why knowledge distillation is crucial to the training of non-autoregressive translation (NAT) models, and propose methods to further improve the distilled data to best match the capacity of an NAT model." ] }, { "id": "task668-505f4f2b9a0b4a6cb2ca1e4aa7d2b90b", "input": "Deep learning has achieved remarkable successes in solving challenging reinforcement learning (RL) problems when dense reward function is provided. However, in sparse reward environment it still often suffers from the need to carefully shape reward function to guide policy optimization. This limits the applicability of RL in the real world since both reinforcement learning and domain-specific knowledge are required. It is therefore of great practical importance to develop algorithms which can learn from a binary signal indicating successful task completion or other unshaped, sparse reward signals. We propose a novel method called competitive experience replay, which efficiently supplements a sparse reward by placing learning in the context of an exploration competition between a pair of agents. Our method complements the recently proposed hindsight experience replay (HER) by inducing an automatic exploratory curriculum. We evaluate our approach on the tasks of reaching various goal locations in an ant maze and manipulating objects with a robotic arm. Each task provides only binary rewards indicating whether or not the goal is achieved. Our method asymmetrically augments these sparse rewards for a pair of agents each learning the same task, creating a competitive game designed to drive exploration. Extensive experiments demonstrate that this method leads to faster converge and improved task performance.", "output": [ "a novel method to learn with sparse reward using adversarial reward re-labeling", "Proposes to use a competitive multi-agent setting for encouraging exploration and shows that CER + HER > HER ~ CER", "Propose a new method for learning from sparse rewards in model-free reinforcement learning settings and densifying reward", "To address the sparse reward problems and encourage exploration in RL algorithms, the authors propose a relabeling strategy called Competitive Experience Reply (CER)." ] }, { "id": "task668-1c88dd713ed541659e939c6f8d679891", "input": "To leverage crowd-sourced data to train multi-speaker text-to-speech (TTS) models that can synthesize clean speech for all speakers, it is essential to learn disentangled representations which can independently control the speaker identity and background noise in generated signals. However, learning such representations can be challenging, due to the lack of labels describing the recording conditions of each training example, and the fact that speakers and recording conditions are often correlated, e.g. since users often make many recordings using the same equipment. This paper proposes three components to address this problem by: (1) formulating a conditional generative model with factorized latent variables, (2) using data augmentation to add noise that is not correlated with speaker identity and whose label is known during training, and (3) using adversarial factorization to improve disentanglement. Experimental results demonstrate that the proposed method can disentangle speaker and noise attributes even if they are correlated in the training data, and can be used to consistently synthesize clean speech for all speakers. Ablation studies verify the importance of each proposed component.", "output": [ "Data augmentation and adversarial training are very effective for disentangling correlated speaker and noise, enabling independent control of each attribute for text-to-speech synthesis." ] }, { "id": "task668-ea1ce1668e974a1a929129819a1c1020", "input": "We propose a novel framework to adaptively adjust the dropout rates for the deep neural network based on a Rademacher complexity bound. The state-of-the-art deep learning algorithms impose dropout strategy to prevent feature co-adaptation. However, choosing the dropout rates remains an art of heuristics or relies on empirical grid-search over some hyperparameter space. In this work, we show the network Rademacher complexity is bounded by a function related to the dropout rate vectors and the weight coefficient matrices. Subsequently, we impose this bound as a regularizer and provide a theoretical justified way to trade-off between model complexity and representation power. Therefore, the dropout rates and the empirical loss are unified into the same objective function, which is then optimized using the block coordinate descent algorithm. We discover that the adaptively adjusted dropout rates converge to some interesting distributions that reveal meaningful patterns.Experiments on the task of image and document classification also show our method achieves better performance compared to the state-of the-art dropout algorithms.", "output": [ "We propose a novel framework to adaptively adjust the dropout rates for the deep neural network based on a Rademacher complexity bound.", "The authors connect dropout parameters to a bound of the Rademacher complexity of the network", "Relates complexity of networks' learnability to dropout rates in backpropagation." ] }, { "id": "task668-1c217a116bb443ba87d3d1bd443b69d1", "input": "We study the precise mechanisms which allow autoencoders to encode and decode a simple geometric shape, the disk. In this carefully controlled setting, we are able to describe the specific form of the optimal solution to the minimisation problem of the training step. We show that the autoencoder indeed approximates this solution during training. Secondly, we identify a clear failure in the generalisation capacity of the autoencoder, namely its inability to interpolate data. Finally, we explore several regularisation schemes to resolve the generalisation problem. Given the great attention that has been recently given to the generative capacity of neural networks, we believe that studying in depth simple geometric cases sheds some light on the generation process and can provide a minimal requirement experimental setup for more complex architectures. \n", "output": [ "We study the functioning of autoencoders in a simple setting and advise new strategies for their regularisation in order to obtain bettre generalisation with latent interpolation in mind for image sythesis. " ] }, { "id": "task668-5667c9cf8ba1410c8a7bee57ddd61f63", "input": "Despite the growing interest in continual learning, most of its contemporary works have been studied in a rather restricted setting where tasks are clearly distinguishable, and task boundaries are known during training. However, if our goal is to develop an algorithm that learns as humans do, this setting is far from realistic, and it is essential to develop a methodology that works in a task-free manner. Meanwhile, among several branches of continual learning, expansion-based methods have the advantage of eliminating catastrophic forgetting by allocating new resources to learn new data. In this work, we propose an expansion-based approach for task-free continual learning. Our model, named Continual Neural Dirichlet Process Mixture (CN-DPM), consists of a set of neural network experts that are in charge of a subset of the data. CN-DPM expands the number of experts in a principled way under the Bayesian nonparametric framework. With extensive experiments, we show that our model successfully performs task-free continual learning for both discriminative and generative tasks such as image classification and image generation.", "output": [ "We propose an expansion-based approach for task-free continual learning for the first time. Our model consists of a set of neural network experts and expands the number of experts under the Bayesian nonparametric principle." ] }, { "id": "task668-cf2e533b0ec64c7d8a9abf385e404426", "input": "We perform an in-depth investigation of the suitability of self-attention models for character-level neural machine translation. We test the standard transformer model, as well as a novel variant in which the encoder block combines information from nearby characters using convolution. We perform extensive experiments on WMT and UN datasets, testing both bilingual and multilingual translation to English using up to three input languages (French, Spanish, and Chinese). Our transformer variant consistently outperforms the standard transformer at the character-level and converges faster while learning more robust character-level alignments.", "output": [ "We perform an in-depth investigation of the suitability of self-attention models for character-level neural machine translation." ] }, { "id": "task668-3ee92acc4b5443368f127e54916f5ae6", "input": "Inferring the structural properties of a protein from its amino acid sequence is a challenging yet important problem in biology. Structures are not known for the vast majority of protein sequences, but structure is critical for understanding function. Existing approaches for detecting structural similarity between proteins from sequence are unable to recognize and exploit structural patterns when sequences have diverged too far, limiting our ability to transfer knowledge between structurally related proteins. We newly approach this problem through the lens of representation learning. We introduce a framework that maps any protein sequence to a sequence of vector embeddings --- one per amino acid position --- that encode structural information. We train bidirectional long short-term memory (LSTM) models on protein sequences with a two-part feedback mechanism that incorporates information from (i) global structural similarity between proteins and (ii) pairwise residue contact maps for individual proteins. To enable learning from structural similarity information, we define a novel similarity measure between arbitrary-length sequences of vector embeddings based on a soft symmetric alignment (SSA) between them. Our method is able to learn useful position-specific embeddings despite lacking direct observations of position-level correspondence between sequences. We show empirically that our multi-task framework outperforms other sequence-based methods and even a top-performing structure-based alignment method when predicting structural similarity, our goal. Finally, we demonstrate that our learned embeddings can be transferred to other protein sequence problems, improving the state-of-the-art in transmembrane domain prediction.", "output": [ "We present a method for learning protein sequence embedding models using structural information in the form of global structural similarity between proteins and within protein residue-residue contacts." ] }, { "id": "task668-b73c79d1946246ddb5a8a6b386281451", "input": "The linear transformations in converged deep networks show fast eigenvalue decay. The distribution of eigenvalues looks like a Heavy-tail distribution, where the vast majority of eigenvalues is small, but not actually zero, and only a few spikes of large eigenvalues exist.\n We use a stochastic approximator to generate histograms of eigenvalues. This allows us to investigate layers with hundreds of thousands of dimensions. We show how the distributions change over the course of image net training, converging to a similar heavy-tail spectrum across all intermediate layers.", "output": [ "We investigate the eigenvalues of the linear layers in deep networks and show that the distributions develop heavy-tail behavior during training." ] }, { "id": "task668-544d578cb54945aaae1261f644e44145", "input": "Despite the impressive performance of deep neural networks (DNNs) on numerous learning tasks, they still exhibit uncouth behaviours. One puzzling behaviour is the subtle sensitive reaction of DNNs to various noise attacks. Such a nuisance has strengthened the line of research around developing and training noise-robust networks. In this work, we propose a new training regularizer that aims to minimize the probabilistic expected training loss of a DNN subject to a generic Gaussian input. We provide an efficient and simple approach to approximate such a regularizer for arbitrarily deep networks. This is done by leveraging the analytic expression of the output mean of a shallow neural network, avoiding the need for memory and computation expensive data augmentation. We conduct extensive experiments on LeNet and AlexNet on various datasets including MNIST, CIFAR10, and CIFAR100 to demonstrate the effectiveness of our proposed regularizer. In particular, we show that networks that are trained with the proposed regularizer benefit from a boost in robustness against Gaussian noise to an equivalent amount of performing 3-21 folds of noisy data augmentation. Moreover, we empirically show on several architectures and datasets that improving robustness against Gaussian noise, by using the new regularizer, can improve the overall robustness against 6 other types of attacks by two orders of magnitude.", "output": [ "An efficient estimate to the Gaussian first moment of DNNs as a regularizer to training robust networks." ] }, { "id": "task668-59b21c56e659462b8a89ac15035b2e2b", "input": "The rich and accessible labeled data fuel the revolutionary success of deep learning. Nonetheless, massive supervision remains a luxury for many real applications, boosting great interest in label-scarce techniques such as few-shot learning (FSL). An intuitively feasible approach to FSL is to conduct data augmentation via synthesizing additional training samples. The key to this approach is how to guarantee both discriminability and diversity of the synthesized samples. In this paper, we propose a novel FSL model, called $\\textrm{D}^2$GAN, which synthesizes Diverse and Discriminative features based on Generative Adversarial Networks (GAN). $\\textrm{D}^2$GAN secures discriminability of the synthesized features by constraining them to have high correlation with real features of the same classes while low correlation with those of different classes. Based on the observation that noise vectors that are closer in the latent code space are more likely to be collapsed into the same mode when mapped to feature space, $\\textrm{D}^2$GAN incorporates a novel anti-collapse regularization term, which encourages feature diversity by penalizing the ratio of the logarithmic similarity of two synthesized features and the logarithmic similarity of the latent codes generating them. Experiments on three common benchmark datasets verify the effectiveness of $\\textrm{D}^2$GAN by comparing with the state-of-the-art.", "output": [ "A new GAN based few-shot learning algorithm by synthesizing diverse and discriminative Features", "A meta-learning method that learns a generative model that can augment the support set of a few-shot learner which optimizes a combination of losses." ] }, { "id": "task668-174ab92bb7f24cc693fc4c6de07431c3", "input": "Compression is a key step to deploy large neural networks on resource-constrained platforms. As a popular compression technique, quantization constrains the number of distinct weight values and thus reducing the number of bits required to represent and store each weight. In this paper, we study the representation power of quantized neural networks. First, we prove the universal approximability of quantized ReLU networks on a wide class of functions. Then we provide upper bounds on the number of weights and the memory size for a given approximation error bound and the bit-width of weights for function-independent and function-dependent structures. Our results reveal that, to attain an approximation error bound of $\\epsilon$, the number of weights needed by a quantized network is no more than $\\mathcal{O}\\left(\\log^5(1/\\epsilon)\\right)$ times that of an unquantized network. This overhead is of much lower order than the lower bound of the number of weights needed for the error bound, supporting the empirical success of various quantization techniques. To the best of our knowledge, this is the first in-depth study on the complexity bounds of quantized neural networks.", "output": [ "This paper proves the universal approximability of quantized ReLU neural networks and puts forward the complexity bound given arbitrary error." ] }, { "id": "task668-d0e3c0c717d84da89c6cbe231d5547f9", "input": "Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query. However, prior work models queries as single points in the vector space, which is problematic because a complex query represents a potentially large set of its answer entities, but it is unclear how such a set can be represented as a single point. Furthermore, prior work can only handle queries that use conjunctions ($\\wedge$) and existential quantifiers ($\\exists$). Handling queries with logical disjunctions ($\\vee$) remains an open problem. Here we propose query2box, an embedding-based framework for reasoning over arbitrary queries with $\\wedge$, $\\vee$, and $\\exists$ operators in massive and incomplete KGs. Our main insight is that queries can be embedded as boxes (i.e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query. We show that conjunctions can be naturally represented as intersections of boxes and also prove a negative result that handling disjunctions would require embedding with dimension proportional to the number of KG entities. However, we show that by transforming queries into a Disjunctive Normal Form, query2box is capable of handling arbitrary logical queries with $\\wedge$, $\\vee$, $\\exists$ in a scalable manner. We demonstrate the effectiveness of query2box on two large KGs and show that query2box achieves up to 25% relative improvement over the state of the art.\n", "output": [ "Answering a wide class of logical queries over knowledge graphs with box embeddings in vector space" ] }, { "id": "task668-037b62f1a1134ffbb67725e632ab64fb", "input": "Recent research developing neural network architectures with external memory have often used the benchmark bAbI question and answering dataset which provides a challenging number of tasks requiring reasoning. Here we employed a classic associative inference task from the human neuroscience literature in order to more carefully probe the reasoning capacity of existing memory-augmented architectures. This task is thought to capture the essence of reasoning -- the appreciation of distant relationships among elements distributed across multiple facts or memories. Surprisingly, we found that current architectures struggle to reason over long distance associations. Similar results were obtained on a more complex task involving finding the shortest path between nodes in a path. We therefore developed a novel architecture, MEMO, endowed with the capacity to reason over longer distances. This was accomplished with the addition of two novel components. First, it introduces a separation between memories/facts stored in external memory and the items that comprise these facts in external memory. Second, it makes use of an adaptive retrieval mechanism, allowing a variable number of ‘memory hops’ before the answer is produced. MEMO is capable of solving our novel reasoning tasks, as well as all 20 tasks in bAbI.", "output": [ "A memory architecture that support inferential reasoning.", "This paper proposes changes to the End2End Memory Network architecture, introduces a new Paired Associative Inference task that most existing models struggle to solve, and shows that their proposed architecture solves the task better.", "A new task (paired associate inference) drawn from cognitive psychology, and proposal for a new memory architecture with features that allow for better performance on the paired associate task." ] }, { "id": "task668-1b666d4615764351911b838712ebf88b", "input": "Model interpretability and systematic, targeted model adaptation present central challenges in deep learning. In the domain of intuitive physics, we study the task of visually predicting stability of block towers with the goal of understanding and influencing the model's reasoning. Our contributions are two-fold. Firstly, we introduce neural stethoscopes as a framework for quantifying the degree of importance of specific factors of influence in deep networks as well as for actively promoting and suppressing information as appropriate. In doing so, we unify concepts from multitask learning as well as training with auxiliary and adversarial losses. Secondly, we deploy the stethoscope framework to provide an in-depth analysis of a state-of-the-art deep neural network for stability prediction, specifically examining its physical reasoning. We show that the baseline model is susceptible to being misled by incorrect visual cues. This leads to a performance breakdown to the level of random guessing when training on scenarios where visual cues are inversely correlated with stability. Using stethoscopes to promote meaningful feature extraction increases performance from 51% to 90% prediction accuracy. Conversely, training on an easy dataset where visual cues are positively correlated with stability, the baseline model learns a bias leading to poor performance on a harder dataset. Using an adversarial stethoscope, the network is successfully de-biased, leading to a performance increase from 66% to 88%.", "output": [ "Combining auxiliary and adversarial training to interrogate and help physical understanding." ] }, { "id": "task668-88112c56c3f04304b63512bebaf25cab", "input": "A point cloud is an agile 3D representation, efficiently modeling an object's surface geometry. However, these surface-centric properties also pose challenges on designing tools to recognize and synthesize point clouds. This work presents a novel autoregressive model, PointGrow, which generates realistic point cloud samples from scratch or conditioned from given semantic contexts. Our model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points. Since point cloud object shapes are typically encoded by long-range interpoint dependencies, we augment our model with dedicated self-attention modules to capture these relations. Extensive evaluation demonstrates that PointGrow achieves satisfying performance on both unconditional and conditional point cloud generation tasks, with respect to fidelity, diversity and semantic preservation. Further, conditional PointGrow learns a smooth manifold of given images where 3D shape interpolation and arithmetic calculation can be performed inside.", "output": [ "An autoregressive deep learning model for generating diverse point clouds.", "An approach for generating 3D shapes as point clouds which considers the lexicographic ordering of points according to coordinates and trains a model to predict points in order.", "The paper introduces a generative model for point clouds using a pixel RNN-like auto-regressive model and an attention model to handle longer-range interactions." ] }, { "id": "task668-b3355289abee4dcc8116ad6937875d75", "input": "Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire. In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data. In contrast to prior art that hand-crafts these simulation parameters or adjusts only parts of the available parameters, our approach fully controls the simulator with the actual underlying goal of maximizing accuracy, rather than mimicking the real data distribution or randomly generating a large volume of data. We find that our approach (i) quickly converges to the optimal simulation parameters in controlled experiments and (ii) can indeed discover good sets of parameters for an image rendering simulator in actual computer vision applications.", "output": [ "We propose an algorithm that automatically adjusts parameters of a simulation engine to generate training data for a neural network such that validation accuracy is maximized." ] }, { "id": "task668-3d363c417e49421c99d1e39a6ea1c0af", "input": "This paper proposes a novel approach to train deep neural networks by unlocking the layer-wise dependency of backpropagation training. The approach employs additional modules called local critic networks besides the main network model to be trained, which are used to obtain error gradients without complete feedforward and backward propagation processes. We propose a cascaded learning strategy for these local networks. In addition, the approach is also useful from multi-model perspectives, including structural optimization of neural networks, computationally efficient progressive inference, and ensemble classification for performance improvement. Experimental results show the effectiveness of the proposed approach and suggest guidelines for determining appropriate algorithm parameters.", "output": [ "We propose a new learning algorithm of deep neural networks, which unlocks the layer-wise dependency of backpropagation.", "An alternative training paradigm for DNIs in which the auxiliary module is trained to approximate directly the final output of the original model, offering side benefits.", "Describes a method of training neural networks without update locking." ] }, { "id": "task668-8c9b11d5484a4e65961d73c69c64d2bd", "input": "Learning disentangled representations of data is one of the central themes in unsupervised learning in general and generative modelling in particular. In this work, we tackle a slightly more intricate scenario where the observations are generated from a conditional distribution of some known control variate and some latent noise variate. To this end, we present a hierarchical model and a training method (CZ-GEM) that leverages some of the recent developments in likelihood-based and likelihood-free generative models. We show that by formulation, CZ-GEM introduces the right inductive biases that ensure the disentanglement of the control from the noise variables, while also keeping the components of the control variate disentangled. This is achieved without compromising on the quality of the generated samples. Our approach is simple, general, and can be applied both in supervised and unsupervised settings.", "output": [ "Hierarchical generative model (hybrid of VAE and GAN) that learns a disentangled representation of data without compromising the generative quality." ] }, { "id": "task668-cdfa4d15e11f49d996a7ea8bb43106cc", "input": "We describe an approach to understand the peculiar and counterintuitive generalization properties of deep neural networks. The approach involves going beyond worst-case theoretical capacity control frameworks that have been popular in machine learning in recent years to revisit old ideas in the statistical mechanics of neural networks. Within this approach, we present a prototypical Very Simple Deep Learning (VSDL) model, whose behavior is controlled by two control parameters, one describing an effective amount of data, or load, on the network (that decreases when noise is added to the input), and one with an effective temperature interpretation (that increases when algorithms are early stopped). Using this model, we describe how a very simple application of ideas from the statistical mechanics theory of generalization provides a strong qualitative description of recently-observed empirical results regarding the inability of deep neural networks not to overfit training data, discontinuous learning and sharp transitions in the generalization properties of learning algorithms, etc.", "output": [ "Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior", "The authors suggest that statistical mechanics ideas will help to understand generalization properties of deep neural networks, and give an approach that provides strong qualitative descriptions of empirical results regarding deep neural networks and learning algorithms.", "A set of ideas related to theoretical understanding generalization properties of multilayer neural networks, and a qualitative analogy between behaviours in deep learning and results from quantitative statistical physics analysis of single and two-layer neural networks." ] }, { "id": "task668-1079dfe00dba4d3aaabb7820435a410c", "input": "Inspired by the adaptation phenomenon of biological neuronal firing, we propose regularity normalization: a reparameterization of the activation in the neural network that take into account the statistical regularity in the implicit space. By considering the neural network optimization process as a model selection problem, the implicit space is constrained by the normalizing factor, the minimum description length of the optimal universal code. We introduce an incremental version of computing this universal code as normalized maximum likelihood and demonstrated its flexibility to include data prior such as top-down attention and other oracle information and its compatibility to be incorporated into batch normalization and layer normalization. The preliminary results showed that the proposed method outperforms existing normalization methods in tackling the limited and imbalanced data from a non-stationary distribution benchmarked on computer vision task. As an unsupervised attention mechanism given input data, this biologically plausible normalization has the potential to deal with other complicated real-world scenarios as well as reinforcement learning setting where the rewards are sparse and non-uniform. Further research is proposed to discover these scenarios and explore the behaviors among different variants.", "output": [ "Considering neural network optimization process as a model selection problem, we introduce a biological plausible normalization method that extracts statistical regularity under MDL principle to tackle imbalanced and limited data issue." ] }, { "id": "task668-f026edd2510940878bbee3e4b937ab4e", "input": " Verifying a person's identity based on their voice is a challenging, real-world problem in biometric security. A crucial requirement of such speaker verification systems is to be domain robust. Performance should not degrade even if speakers are talking in languages not seen during training. To this end, we present a flexible and interpretable framework for learning domain invariant speaker embeddings using Generative Adversarial Networks. We combine adversarial training with an angular margin loss function, which encourages the speaker embedding model to be discriminative by directly optimizing for cosine similarity between classes. We are able to beat a strong baseline system using a cosine distance classifier and a simple score-averaging strategy. Our results also show that models with adversarial adaptation perform significantly better than unadapted models. In an attempt to better understand this behavior, we quantitatively measure the degree of invariance induced by our proposed methods using Maximum Mean Discrepancy and Frechet distances. Our analysis shows that our proposed adversarial speaker embedding models significantly reduce the distance between source and target data distributions, while performing similarly on the former and better on the latter.", "output": [ "Speaker verificaiton performance can be significantly improved by adapting the model to in-domain data using Generative Adversarial Networks. Furthermore, the adaptation can be performed in an unsupervised way.", "Propose a number of GAN variants on the task of speaker recognition in the domain mismatched condition." ] }, { "id": "task668-7e4f529a365149a099b77a083f3d23b9", "input": "We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers. Our model builds an object-based scene representation and translates sentences into executable, symbolic programs. To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation. Analogical to human concept learning, the perception module learns visual concepts based on the language description of the object being referred to. Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences. We use curriculum learning to guide the searching over the large compositional space of images and language. Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences. Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains. It also empowers applications including visual question answering and bidirectional image-text retrieval.", "output": [ "We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them." ] }, { "id": "task668-a2eabc267752426290868cb2b067dfb5", "input": "Recordings of neural circuits in the brain reveal extraordinary dynamical richness and high variability. At the same time, dimensionality reduction techniques generally uncover low-dimensional structures underlying these dynamics. What determines the dimensionality of activity in neural circuits? What is the functional role of dimensionality in behavior and task learning? In this work we address these questions using recurrent neural network (RNN) models. We find that, depending on the dynamics of the initial network, RNNs learn to increase and reduce dimensionality in a way that matches task demands. These findings shed light on fundamental dynamical mechanisms by which neural networks solve tasks with robust representations that generalize to new cases.", "output": [ "Recurrent Neural Networks learn to increase and reduce the dimensionality of their internal representation in a way that matches the task, depending on the dynamics of the initial network." ] }, { "id": "task668-d3cbc14c85974afa9629dc7215180298", "input": "We explore the properties of byte-level recurrent language models. When given sufficient amounts of capacity, training data, and compute time, the representations learned by these models include disentangled features corresponding to high-level concepts. Specifically, we find a single unit which performs sentiment analysis. These representations, learned in an unsupervised manner, achieve state of the art on the binary subset of the Stanford Sentiment Treebank. They are also very data efficient. When using only a handful of labeled examples, our approach matches the performance of strong baselines trained on full datasets. We also demonstrate the sentiment unit has a direct influence on the generative process of the model. Simply fixing its value to be positive or negative generates samples with the corresponding positive or negative sentiment.", "output": [ "Byte-level recurrent language models learn high-quality domain specific representations of text." ] }, { "id": "task668-a18dbad561714055994ddc2bbf057673", "input": " Domain adaptation tackles the problem of transferring knowledge from a label-rich source domain to an unlabeled or label-scarce target domain. Recently domain-adversarial training (DAT) has shown promising capacity to learn a domain-invariant feature space by reversing the gradient propagation of a domain classifier. However, DAT is still vulnerable in several aspects including (1) training instability due to the overwhelming discriminative ability of the domain classifier in adversarial training, (2) restrictive feature-level alignment, and (3) lack of interpretability or systematic explanation of the learned feature space. In this paper, we propose a novel Max-margin Domain-Adversarial Training (MDAT) by designing an Adversarial Reconstruction Network (ARN). The proposed MDAT stabilizes the gradient reversing in ARN by replacing the domain classifier with a reconstruction network, and in this manner ARN conducts both feature-level and pixel-level domain alignment without involving extra network structures. Furthermore, ARN demonstrates strong robustness to a wide range of hyper-parameters settings, greatly alleviating the task of model selection. Extensive empirical results validate that our approach outperforms other state-of-the-art domain alignment methods. Additionally, the reconstructed target samples are visualized to interpret the domain-invariant feature space which conforms with our intuition.", "output": [ "A stable domain-adversarial training approach for robust and comprehensive domain adaptation" ] }, { "id": "task668-4d49971834b4449bb5e4dc3c9207b660", "input": "Conventional methods model open domain dialogue generation as a black box through end-to-end learning from large scale conversation data. In this work, we make the first step to open the black box by introducing dialogue acts into open domain dialogue generation. The dialogue acts are generally designed and reveal how people engage in social chat. Inspired by analysis on real data, we propose jointly modeling dialogue act selection and response generation, and perform learning with human-human conversations tagged with a dialogue act classifier and a reinforcement approach to further optimizing the model for long-term conversation. With the dialogue acts, we not only achieve significant improvement over state-of-the-art methods on response quality for given contexts and long-term conversation in both machine-machine simulation and human-machine conversation, but also are capable of explaining why such achievements can be made.", "output": [ "open domain dialogue generation with dialogue acts", "The authors use a distant supervision technique to add dialogue act tags as a conditioning factor for generating responses in open-domain dialogues", "The paper describes a technique to incorporate dialog acts into neural conversational agents" ] }, { "id": "task668-611ddc0eec0d48a59899ba0563b22c03", "input": "Convolutional architectures have recently been shown to be competitive on many\n sequence modelling tasks when compared to the de-facto standard of recurrent neural networks (RNNs) while providing computational and modelling advantages due to inherent parallelism. However, currently, there remains a performance\n gap to more expressive stochastic RNN variants, especially those with several layers of dependent random variables. In this work, we propose stochastic temporal convolutional networks (STCNs), a novel architecture that combines the computational advantages of temporal convolutional networks (TCN) with the representational power and robustness of stochastic latent spaces. In particular, we propose a hierarchy of stochastic latent variables that captures temporal dependencies at different time-scales. The architecture is modular and flexible due to the decoupling of the deterministic and stochastic layers. We show that the proposed architecture achieves state of the art log-likelihoods across several tasks. Finally, the model is capable of predicting high-quality synthetic samples over a long-range temporal horizon in modelling of handwritten text.", "output": [ "We combine the computational advantages of temporal convolutional architectures with the expressiveness of stochastic latent variables." ] }, { "id": "task668-765c8c0e8f294601bfc69384b0353028", "input": "Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques harness neural networks to learn directly from examples of buggy programs and their fixes. In this work, we consider a recently identified class of bugs called variable-misuse bugs. The state-of-the-art solution for variable misuse enumerates potential fixes for all possible bug locations in a program, before selecting the best prediction. We show that it is beneficial to train a model that jointly and directly localizes and repairs variable-misuse bugs. We present multi-headed pointer networks for this purpose, with one head each for localization and repair. The experimental results show that the joint model significantly outperforms an enumerative solution that uses a pointer based model for repair alone.", "output": [ "Multi-headed Pointer Networks for jointly learning to localize and repair Variable Misuse bugs", "Proposes a LSTM based model with pointers to break the problem of VarMisuse down into multiple steps.", "This paper presents an LSTM-based model for bug detection and repair of the VarMisuse bug, and demonstrates significant improvements compared to prior approaches on several datasets." ] }, { "id": "task668-286d377c77ad4785b8b3b11fa5c16256", "input": "In this paper, we explore new approaches to combining information encoded within the learned representations of autoencoders. We explore models that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data. Furthermore, we explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label. We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.", "output": [ "We leverage deterministic autoencoders as generative models by proposing mixing functions which combine hidden states from pairs of images. These mixes are made to look realistic through an adversarial framework." ] }, { "id": "task668-db712f4131d34cd4bcf7ae414201b10a", "input": "The smallest eigenvectors of the graph Laplacian are well-known to provide a succinct representation of the geometry of a weighted graph. In reinforcement learning (RL), where the weighted graph may be interpreted as the state transition process induced by a behavior policy acting on the environment, approximating the eigenvectors of the Laplacian provides a promising approach to state representation learning. However, existing methods for performing this approximation are ill-suited in general RL settings for two main reasons: First, they are computationally expensive, often requiring operations on large matrices. Second, these methods lack adequate justification beyond simple, tabular, finite-state settings. In this paper, we present a fully general and scalable method for approximating the eigenvectors of the Laplacian in a model-free RL context. We systematically evaluate our approach and empirically show that it generalizes beyond the tabular, finite-state setting. Even in tabular, finite-state settings, its ability to approximate the eigenvectors outperforms previous proposals. Finally, we show the potential benefits of using a Laplacian representation learned using our method in goal-achieving RL tasks, providing evidence that our technique can be used to significantly improve the performance of an RL agent.", "output": [ "We propose a scalable method to approximate the eigenvectors of the Laplacian in the reinforcement learning context and we show that the learned representations can improve the performance of an RL agent." ] }, { "id": "task668-13b47631d66c4b538f6b71d287e9b65f", "input": "Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is therefore heteroscedastic. Classical exploration strategies such as upper confidence bound algorithms and Thompson sampling fail to appropriately account for heteroscedasticity, even in the bandit setting. Motivated by recent findings that address this issue in bandits, we propose to use Information-Directed Sampling (IDS) for exploration in reinforcement learning. As our main contribution, we build on recent advances in distributional reinforcement learning and propose a novel, tractable approximation of IDS for deep Q-learning. The resulting exploration strategy explicitly accounts for both parametric uncertainty and heteroscedastic observation noise. We evaluate our method on Atari games and demonstrate a significant improvement over alternative approaches.", "output": [ "We develop a practical extension of Information-Directed Sampling for Reinforcement Learning, which accounts for parametric uncertainty and heteroscedasticity in the return distribution for exploration.", "The authors propose a way of extending Information-Directed Sampling to reinforcement learning by combining two types of uncertainty to obtain a simple exploration strategy based on IDS. ", "This paper investigates sophistical exploration approaches for reinforcement learning built on Information Direct Sampling and on Distributional Reinforcement Learning" ] }, { "id": "task668-e81890bc0c1c47998953ac51694735e0", "input": "Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a ``forgetting event'' to have occurred when an individual training example transitions from being classified correctly to incorrectly over the course of learning. Across several benchmark data sets, we find that: (i) certain examples are forgotten with high frequency, and some not at all; (ii) a data set's (un)forgettable examples generalize across neural architectures; and (iii) based on forgetting dynamics, a significant fraction of examples can be omitted from the training data set while still maintaining state-of-the-art generalization performance.", "output": [ "We show that catastrophic forgetting occurs within what is considered to be a single task and find that examples that are not prone to forgetting can be removed from the training set without loss of generalization.", "Studies the forgetting behavior of training examples during SGD, and shows there exist \"support examples\" in neural network training across different network architectures.", "This paper analyzes the extent to which networks learn to correctly classify specific examples and then forget these examples over the course of training.", "The paper studies whether some examples in training neural networks are harder to learn than others. Such examples are forgotten and relearned multiple times through learning." ] }, { "id": "task668-c50af99494764831ae5bad40f25274ee", "input": "The growing interest to implement Deep Neural Networks (DNNs) on resource-bound hardware has motivated innovation of compression algorithms. Using these algorithms, DNN model sizes can be substantially reduced, with little to no accuracy degradation. This is achieved by either eliminating components from the model, or penalizing complexity during training. While both approaches demonstrate considerable compressions, the former often ignores the loss function during compression while the later produces unpredictable compressions. In this paper, we propose a technique that directly minimizes both the model complexity and the changes in the loss function. In this technique, we formulate compression as a constrained optimization problem, and then present a solution for it. We will show that using this technique, we can achieve competitive results.", "output": [ "Compressing trained DNN models by minimizing their complexity while constraining their loss.", "This paper proposes a method for deep neural network compression under accuracy constraints.", "This paper presents a loss value constrained k-means encoding method for network compression and develops an iterative algorithm for model optimization." ] }, { "id": "task668-bcfc4e9af0544057b82e5943b11a822f", "input": "In information retrieval, learning to rank constructs a machine-based ranking model which given a query, sorts the search results by their degree of relevance or importance to the query. Neural networks have been successfully applied to this problem, and in this paper, we propose an attention-based deep neural network which better incorporates different embeddings of the queries and search results with an attention-based mechanism. This model also applies a decoder mechanism to learn the ranks of the search results in a listwise fashion. The embeddings are trained with convolutional neural networks or the word2vec model. We demonstrate the performance of this model with image retrieval and text querying data sets.", "output": [ "learning to rank with several embeddings and attentions", "Proposes to use attention to combine multiple input representations for both query and search results in the learning to rank task." ] }, { "id": "task668-1b457fa1ded44500aa32c213fe476e29", "input": "Multiagent systems where the agents interact among themselves and with an stochastic environment can be formalized as stochastic games. We study a subclass of these games, named Markov potential games (MPGs), that appear often in economic and engineering applications when the agents share some common resource. We consider MPGs with continuous state-action variables, coupled constraints and nonconvex rewards. Previous analysis followed a variational approach that is only valid for very simple cases (convex rewards, invertible dynamics, and no coupled constraints); or considered deterministic dynamics and provided open-loop (OL) analysis, studying strategies that consist in predefined action sequences, which are not optimal for stochastic environments. We present a closed-loop (CL) analysis for MPGs and consider parametric policies that depend on the current state and where agents adapt to stochastic transitions. We provide easily verifiable, sufficient and necessary conditions for a stochastic game to be an MPG, even for complex parametric functions (e.g., deep neural networks); and show that a closed-loop Nash equilibrium (NE) can be found (or at least approximated) by solving a related optimal control problem (OCP). This is useful since solving an OCP---which is a single-objective problem---is usually much simpler than solving the original set of coupled OCPs that form the game---which is a multiobjective control problem. This is a considerable improvement over the previously standard approach for the CL analysis of MPGs, which gives no approximate solution if no NE belongs to the chosen parametric family, and which is practical only for simple parametric forms. We illustrate the theoretical contributions with an example by applying our approach to a noncooperative communications engineering game. We then solve the game with a deep reinforcement learning algorithm that learns policies that closely approximates an exact variational NE of the game.", "output": [ "We present general closed loop analysis for Markov potential games and show that deep reinforcement learning can be used for learning approximate closed-loop Nash equilibrium." ] }, { "id": "task668-8683a903c56b45dcb1d37afbfdaf77ba", "input": "Large matrix inversions have often been cited as a major impediment to scaling Gaussian process (GP) models. With the use of GPs as building blocks for ever more sophisticated Bayesian deep learning models, removing these impediments is a necessary step for achieving large scale results. We present a variational approximation for a wide range of GP models that does not require a matrix inverse to be performed at each optimisation step. Our bound instead directly parameterises a free matrix, which is an additional variational parameter. At the local maxima of the bound, this matrix is equal to the matrix inverse. We prove that our bound gives the same guarantees as earlier variational approximations. We demonstrate some beneficial properties of the bound experimentally, although significant wall clock time speed improvements will require future improvements in optimisation and implementation.", "output": [ "We present a variational lower bound for GP models that can be optimised without computing expensive matrix operations like inverses, while providing the same guarantees as existing variational approximations." ] }, { "id": "task668-9418ef69d93e4f128a55dccd3698cab9", "input": "We introduce a new normalization technique that exhibits the fast convergence properties of batch normalization using a transformation of layer weights instead of layer outputs. The proposed technique keeps the contribution of positive and negative weights to the layer output in equilibrium. We validate our method on a set of standard benchmarks including CIFAR-10/100, SVHN and ILSVRC 2012 ImageNet.", "output": [ "An alternative normalization technique to batch normalization", "Introduces a normalization technique, which normalizes the weights of convolutional layers. ", "This manuscript introduces a new layer-wise transform, EquiNorm, to improve upon batch normalization that does not modify the inputs to the layers but rather the layer weights." ] }, { "id": "task668-26b63a948eed49eda71fec7af51a552e", "input": "We introduce CGNN, a framework to learn functional causal models as generative neural networks. These networks are trained using backpropagation to minimize the maximum mean discrepancy to the observed data. Unlike previous approaches, CGNN leverages both conditional independences and distributional asymmetries to seamlessly discover bivariate and multivariate \n causal structures, with or without hidden variables. CGNN does not only estimate the causal structure, but a full and differentiable generative model of the data. Throughout an extensive variety of experiments, we illustrate the competitive esults of CGNN w.r.t state-of-the-art alternatives in observational causal discovery on both simulated and real data, in the tasks of cause-effect inference, v-structure identification, and multivariate causal discovery.", "output": [ "Discover the structure of functional causal models with generative neural networks" ] }, { "id": "task668-86f8c7a8a18b437888ac3def2739c9bd", "input": "We propose a novel yet simple neural network architecture for topic modelling. The method is based on training an autoencoder structure where the bottleneck represents the space of the topics distribution and the decoder outputs represent the space of the words distributions over the topics. We exploit an auxiliary decoder to prevent mode collapsing in our model. A key feature for an effective topic modelling method is having sparse topics and words distributions, where there is a trade-off between the sparsity level of topics and words. This feature is implemented in our model by L-2 regularization and the model hyperparameters take care of the trade-off. We show in our experiments that our model achieves competitive results compared to the state-of-the-art deep models for topic modelling, despite its simple architecture and training procedure. The “New York Times” and “20 Newsgroups” datasets are used in the experiments.\n\n", "output": [ "A deep model for topic modelling" ] }, { "id": "task668-0841a0c9ab1c436e96d70818422d9279", "input": "The ability to synthesize realistic patterns of neural activity is crucial for studying neural information processing. Here we used the Generative Adversarial Networks (GANs) framework to simulate the concerted activity of a population of neurons.\n We adapted the Wasserstein-GAN variant to facilitate the generation of unconstrained neural population activity patterns while still benefiting from parameter sharing in the temporal domain.\n We demonstrate that our proposed GAN, which we termed Spike-GAN, generates spike trains that match accurately the first- and second-order statistics of datasets of tens of neurons and also approximates well their higher-order statistics. We applied Spike-GAN to a real dataset recorded from salamander retina and showed that it performs as well as state-of-the-art approaches based on the maximum entropy and the dichotomized Gaussian frameworks. Importantly, Spike-GAN does not require to specify a priori the statistics to be matched by the model, and so constitutes a more flexible method than these alternative approaches.\n Finally, we show how to exploit a trained Spike-GAN to construct 'importance maps' to detect the most relevant statistical structures present in a spike train. \n Spike-GAN provides a powerful, easy-to-use technique for generating realistic spiking neural activity and for describing the most relevant features of the large-scale neural population recordings studied in modern systems neuroscience.\n", "output": [ "Using Wasserstein-GANs to generate realistic neural activity and to detect the most relevant features present in neural population patterns.", "A method for simulating spike trains from populations of neurons which match empirical data using a semi-convolutional GAN.", "The paper proposes to use GANs for synthesizing realistic neural activity patterns" ] }, { "id": "task668-d57060cdc7224229924e13078624c05d", "input": "We introduce a neural network that represents sentences by composing their words according to induced binary parse trees. We use Tree-LSTM as our composition function, applied along a tree structure found by a fully differentiable natural language chart parser. Our model simultaneously optimises both the composition function and the parser, thus eliminating the need for externally-provided parse trees which are normally required for Tree-LSTM. It can therefore be seen as a tree-based RNN that is unsupervised with respect to the parse trees. As it is fully differentiable, our model is easily trained with an off-the-shelf gradient descent method and backpropagation. We demonstrate that it achieves better performance compared to various supervised Tree-LSTM architectures on a textual entailment task and a reverse dictionary task. Finally, we show how performance can be improved with an attention mechanism which fully exploits the parse chart, by attending over all possible subspans of the sentence.", "output": [ "Represent sentences by composing them with Tree-LSTMs according to automatically induced parse trees." ] }, { "id": "task668-c5951d0a1bb142da8fd6925feca444ec", "input": "Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either\n discrete or continuous space. Motivated by the project of design Game AI for King of Glory\n (KOG), one the world’s most popular mobile game, we consider the scenario with the discrete-continuous\n hybrid action space. To directly apply existing DLR frameworks, existing approaches\n either approximate the hybrid space by a discrete set or relaxing it into a continuous set, which is\n usually less efficient and robust. In this paper, we propose a parametrized deep Q-network (P-DQN)\n for the hybrid action space without approximation or relaxation. Our algorithm combines DQN and\n DDPG and can be viewed as an extension of the DQN to hybrid actions. The empirical study on the\n game KOG validates the efficiency and effectiveness of our method.", "output": [ "A DQN and DDPG hybrid algorithm is proposed to deal with the discrete-continuous hybrid action space." ] }, { "id": "task668-b2df5be4223e4d06bf6967a4b89c68f7", "input": "We describe a simple scheme that allows an agent to learn about its environment in an unsupervised manner. Our scheme pits two versions of the same agent, Alice and Bob, against one another. Alice proposes a task for Bob to complete; and then Bob attempts to complete the task. In this work we will focus on two kinds of environments: (nearly) reversible environments and environments that can be reset. Alice will \"propose\" the task by doing a sequence of actions and then Bob must undo or repeat them, respectively. Via an appropriate reward structure, Alice and Bob automatically generate a curriculum of exploration, enabling unsupervised training of the agent. When Bob is deployed on an RL task within the environment, this unsupervised training reduces the number of supervised episodes needed to learn, and in some cases converges to a higher reward.", "output": [ "Unsupervised learning for reinforcement learning using an automatic curriculum of self-play", "A new formulation for exploring the environment in an unsupervised way to aid a specific task later, where one agent proposes increasingly difficult tasks and the learning agent tries to accomplish them.", "A self-play model where one agent learns to propose tasks that are easy for them but difficult for an opponent, creating a moving target of self-play objectives and learning curriculum. " ] }, { "id": "task668-059caef9ee0140bbb8c94f9e3d617911", "input": "Effectively inferring discriminative and coherent latent topics of short texts is a critical task for many real world applications. Nevertheless, the task has been proven to be a great challenge for traditional topic models due to the data sparsity problem induced by the characteristics of short texts. Moreover, the complex inference algorithm also become a bottleneck for these traditional models to rapidly explore variations. In this paper, we propose a novel model called Neural Variational Sparse Topic Model (NVSTM) based on a sparsity-enhanced topic model named Sparse Topical Coding (STC). In the model, the auxiliary word embeddings are utilized to improve the generation of representations. The Variational Autoencoder (VAE) approach is applied to inference the model efficiently, which makes the model easy to explore extensions for its black-box inference process. Experimental results onWeb Snippets, 20Newsgroups, BBC and Biomedical datasets show the effectiveness and efficiency of the model.", "output": [ "a neural sparsity-enhanced topic model based on VAE" ] }, { "id": "task668-19f345d113d94f17838bbe91afbc7608", "input": "Designing neural networks for continuous-time stochastic processes is challenging, especially when observations are made irregularly. In this article, we analyze neural networks from a frame theoretic perspective to identify the sufficient conditions that enable smoothly recoverable representations of signals in L^2(R). Moreover, we show that, under certain assumptions, these properties hold even when signals are irregularly observed. As we converge to the family of (convolutional) neural networks that satisfy these conditions, we show that we can optimize our convolution filters while constraining them so that they effectively compute a Discrete Wavelet Transform. Such a neural network can efficiently divide the time-axis of a signal into orthogonal sub-spaces of different temporal scale and localization. We evaluate the resulting neural network on an assortment of synthetic and real-world tasks: parsimonious auto-encoding, video classification, and financial forecasting.", "output": [ "Neural architectures providing representations of irregularly observed signals that provably enable signal reconstruction.", "Proves that convolutional neural networks with Leaky ReLU activation function are nonlinear frames, with similar results for non-uniformly sampled time-series", "This article considers neural networks over time-series and show that the first convolutional filters can be chosen to represent a discrete wavelet transform." ] }, { "id": "task668-fa298bfc2d514aaaa51961b03c33410c", "input": "Deep latent variable models have seen recent success in many data domains. Lossless compression is an application of these models which, despite having the potential to be highly useful, has yet to be implemented in a practical manner. We present '`Bits Back with ANS' (BB-ANS), a scheme to perform lossless compression with latent variable models at a near optimal rate. We demonstrate this scheme by using it to compress the MNIST dataset with a variational auto-encoder model (VAE), achieving compression rates superior to standard methods with only a simple VAE. Given that the scheme is highly amenable to parallelization, we conclude that with a sufficiently high quality generative model this scheme could be used to achieve substantial improvements in compression rate with acceptable running time. We make our implementation available open source at https://github.com/bits-back/bits-back .", "output": [ "We do lossless compression of large image datasets using a VAE, beat existing compression algorithms." ] }, { "id": "task668-cfc330113c7e49339366133f5627eb7f", "input": "Semi-supervised learning, i.e. jointly learning from labeled an unlabeled samples, is an active research topic due to its key role on relaxing human annotation constraints. In the context of image classification, recent advances to learn from unlabeled samples are mainly focused on consistency regularization methods that encourage invariant predictions for different perturbations of unlabeled samples. We, conversely, propose to learn from unlabeled data by generating soft pseudo-labels using the network predictions. We show that a naive pseudo-labeling overfits to incorrect pseudo-labels due to the so-called confirmation bias and demonstrate that mixup augmentation and setting a minimum number of labeled samples per mini-batch are effective regularization techniques for reducing it. The proposed approach achieves state-of-the-art results in CIFAR-10/100 and Mini-ImageNet despite being much simpler than other state-of-the-art. These results demonstrate that pseudo-labeling can outperform consistency regularization methods, while the opposite was supposed in previous work. Code will be made available.", "output": [ "Pseudo-labeling has shown to be a weak alternative for semi-supervised learning. We, conversely, demonstrate that dealing with confirmation bias with several regularizations makes pseudo-labeling a suitable approach." ] }, { "id": "task668-453c59e1249343a68a6b5cb5b375a6f2", "input": "One of the most notable contributions of deep learning is the application of convolutional neural networks (ConvNets) to structured signal classification, and in particular image classification. Beyond their impressive performances in supervised learning, the structure of such networks inspired the development of deep filter banks referred to as scattering transforms. These transforms apply a cascade of wavelet transforms and complex modulus operators to extract features that are invariant to group operations and stable to deformations. Furthermore, ConvNets inspired recent advances in geometric deep learning, which aim to generalize these networks to graph data by applying notions from graph signal processing to learn deep graph filter cascades. We further advance these lines of research by proposing a geometric scattering transform using graph wavelets defined in terms of random walks on the graph. We demonstrate the utility of features extracted with this designed deep filter bank in graph classification of biochemistry and social network data (incl. state of the art results in the latter case), and in data exploration, where they enable inference of EC exchange preferences in enzyme evolution.", "output": [ "We present a new feed forward graph ConvNet based on generalizing the wavelet scattering transform of Mallat, and demonstrate its utility in graph classification and data exploration tasks." ] }, { "id": "task668-b67a1f0b960449969d11a8b11d1c6a8b", "input": "The growth in the complexity of Convolutional Neural Networks (CNNs) is increasing interest in partitioning a network across multiple accelerators during training and pipelining the backpropagation computations over the accelerators. Existing approaches avoid or limit the use of stale weights through techniques such as micro-batching or weight stashing. These techniques either underutilize of accelerators or increase memory footprint. We explore the impact of stale weights on the statistical efficiency and performance in a pipelined backpropagation scheme that maximizes accelerator utilization and keeps memory overhead modest. We use 4 CNNs (LeNet-5, AlexNet, VGG and ResNet) and show that when pipelining is limited to early layers in a network, training with stale weights converges and results in models with comparable inference accuracies to those resulting from non-pipelined training on MNIST and CIFAR-10 datasets; a drop in accuracy of 0.4%, 4%, 0.83% and 1.45% for the 4 networks, respectively. However, when pipelining is deeper in the network, inference accuracies drop significantly. We propose combining pipelined and non-pipelined training in a hybrid scheme to address this drop. We demonstrate the implementation and performance of our pipelined backpropagation in PyTorch on 2 GPUs using ResNet, achieving speedups of up to 1.8X over a 1-GPU baseline, with a small drop in inference accuracy.", "output": [ "Accelerating CNN training on a Pipeline of Accelerators with Stale Weights" ] }, { "id": "task668-795d4f8401224e10938e80f67c01ecaa", "input": "Formal verification of machine learning models has attracted attention recently, and significant progress has been made on proving simple properties like robustness to small perturbations of the input features. In this context, it has also been observed that folding the verification procedure into training makes it easier to train verifiably robust models. In this paper, we extend the applicability of verified training by extending it to (1) recurrent neural network architectures and (2) complex specifications that go beyond simple adversarial robustness, particularly specifications that capture temporal properties like requiring that a robot periodically visits a charging station or that a language model always produces sentences of bounded length. Experiments show that while models trained using standard training often violate desired specifications, our verified training method produces models that both perform well (in terms of test error or reward) and can be shown to be provably consistent with specifications.", "output": [ "Neural Network Verification for Temporal Properties and Sequence Generation Models", "This paper extends interval bound propagation to recurrent computation and auto-regressive models, introduces and extends Signal Temporal Logic for specifying temporal contraints, and provides proof that STL with bound propagation can ensure neural models conform to temporal specification.", "A way to train time-series regressors verifiably with respect to a set of rules defined by signal temporal logic, and work in deriving bound propagation rules for the STL language." ] }, { "id": "task668-e54d1222cad54a05a4bcd5b470760273", "input": "The aim of this study is to introduce a formal framework for analysis and synthesis of driver assistance systems. It applies formal methods to the verification of a stochastic human driver model built using the cognitive architecture ACT-R, and then bootstraps safety in semi-autonomous vehicles through the design of provably correct Advanced Driver Assistance Systems. The main contributions include the integration of probabilistic ACT-R models in the formal analysis of semi-autonomous systems and an abstraction technique that enables a finite representation of a large dimensional, continuous system in the form of a Markov model. The effectiveness of the method is illustrated in several case studies under various conditions.", "output": [ "Verification of a human driver model based on a cognitive architecture and synthesis of a correct-by-construction ADAS from it." ] }, { "id": "task668-ed15bf193b484bcc840009a66469ce60", "input": "We propose and study a method for learning interpretable representations for the task of regression. Features are represented as networks of multi-type expression trees comprised of activation functions common in neural networks in addition to other elementary functions. Differentiable features are trained via gradient descent, and the performance of features in a linear model is used to weight the rate of change among subcomponents of each representation. The search process maintains an archive of representations with accuracy-complexity trade-offs to assist in generalization and interpretation. We compare several stochastic optimization approaches within this framework. We benchmark these variants on 100 open-source regression problems in comparison to state-of-the-art machine learning approaches. Our main finding is that this approach produces the highest average test scores across problems while producing representations that are orders of magnitude smaller than the next best performing method (gradient boosting). We also report a negative result in which attempts to directly optimize the disentanglement of the representation result in more highly correlated features.", "output": [ "Representing the network architecture as a set of syntax trees and optimizing their structure leads to accurate and concise regression models. " ] }, { "id": "task668-21e393b954d94d1ebe88e25dcb8a82e5", "input": "We consider off-policy policy evaluation when the trajectory data are generated by multiple behavior policies. Recent work has shown the key role played by the state or state-action stationary distribution corrections in the infinite horizon context for off-policy policy evaluation. We propose estimated mixture policy (EMP), a novel class of partially policy-agnostic methods to accurately estimate those quantities. With careful analysis, we show that EMP gives rise to estimates with reduced variance for estimating the state stationary distribution correction while it also offers a useful induction bias for estimating the state-action stationary distribution correction. In extensive experiments with both continuous and discrete environments, we demonstrate that our algorithm offers significantly improved accuracy compared to the state-of-the-art methods.", "output": [ "A new partially policy-agnostic method for infinite-horizon off-policy policy evalution with multiple known or unknown behavior policies.", "An estimated mixture policy which takes ideas from off-policy policy evaluation infinite horizon estimators and regression importance sampling for importance weight, and extends them to many policies and unknown policies.", "An algorithm to solve infinite horizon off policy evaluation with multiple behavior policies by estimating a mixed policy under regression, and theoretical proof that an estimated policy ratio can reduce variance." ] }, { "id": "task668-684222c0c6fe425dbf2f324e6c8b702a", "input": "Abstract In this work, we describe a set of rules for the design and initialization of well-conditioned neural networks, guided by the goal of naturally balancing the diagonal blocks of the Hessian at the start of training. We show how our measure of conditioning of a block relates to another natural measure of conditioning, the ratio of weight gradients to the weights. We prove that for a ReLU-based deep multilayer perceptron, a simple initialization scheme using the geometric mean of the fan-in and fan-out satisfies our scaling rule. For more sophisticated architectures, we show how our scaling principle can be used to guide design choices to produce well-conditioned neural networks, reducing guess-work.", "output": [ "A theory for initialization and scaling of ReLU neural network layers" ] }, { "id": "task668-aef8cbc1b80c46358b46a0302cb56895", "input": "Surrogate models can be used to accelerate approximate Bayesian computation (ABC). In one such framework the discrepancy between simulated and observed data is modelled with a Gaussian process. So far principled strategies have been proposed only for sequential selection of the simulation locations. To address this limitation, we develop Bayesian optimal design strategies to parallellise the expensive simulations. We also touch the problem of quantifying the uncertainty of the ABC posterior due to the limited budget of simulations.", "output": [ "We propose principled batch Bayesian experimental design strategies and a method for uncertainty quantification of the posterior summaries in a Gaussian process surrogate-based approximate Bayesian computation framework." ] }, { "id": "task668-7cea43c0e59b42ed9413757c7a3f54cd", "input": "Text generation is ubiquitous in many NLP tasks, from summarization, to dialogue and machine translation. The dominant parametric approach is based on locally normalized models which predict one word at a time. While these work remarkably well, they are plagued by exposure bias due to the greedy nature of the generation process. In this work, we investigate un-normalized energy-based models (EBMs) which operate not at the token but at the sequence level. In order to make training tractable, we first work in the residual of a pretrained locally normalized language model and second we train using noise contrastive estimation. Furthermore, since the EBM works at the sequence level, we can leverage pretrained bi-directional contextual representations, such as BERT and RoBERTa. Our experiments on two large language modeling datasets show that residual EBMs yield lower perplexity compared to locally normalized baselines. Moreover, generation via importance sampling is very efficient and of higher quality than the baseline models according to human evaluation.", "output": [ "We show that Energy-Based models when trained on the residual of an auto-regressive language model can be used effectively and efficiently to generate text. ", "A proposed Residual Energy-based Model (EBM) for text generation which operates at the sentence level, and can therefore leverage BERT, and achieves lower perplexity and is preferred by human evaluation." ] }, { "id": "task668-38403dc213c14eac8f6c551f0974060f", "input": "We study the convergence of gradient descent (GD) and stochastic gradient descent (SGD) for training $L$-hidden-layer linear residual networks (ResNets). We prove that for training deep residual networks with certain linear transformations at input and output layers, which are fixed throughout training, both GD and SGD with zero initialization on all hidden weights can converge to the global minimum of the training loss. Moreover, when specializing to appropriate Gaussian random linear transformations, GD and SGD provably optimize wide enough deep linear ResNets. Compared with the global convergence result of GD for training standard deep linear networks \\citep{du2019width}, our condition on the neural network width is sharper by a factor of $O(\\kappa L)$, where $\\kappa$ denotes the condition number of the covariance matrix of the training data. In addition, for the first time we establish the global convergence of SGD for training deep linear ResNets and prove a linear convergence rate when the global minimum is $0$.", "output": [ "Under certain condition on the input and output linear transformations, both GD and SGD can achieve global convergence for training deep linear ResNets.", "The authors study the convergence of gradient descent in training deep linear residual networks, and establish a global convergence of GD/SGD and linear convergence rates of SG/SGD.", "Study of convergence properties of GD and SGD on deep linear resnets, and proof that under certain conditions on the input and output transformations and with zero initialization, GD and SGD converges to global minima." ] }, { "id": "task668-6405819a3e954c5c878a6b574beffa38", "input": "In this paper, we investigate learning the deep neural networks for automated optical inspection in industrial manufacturing. Our preliminary result has shown the stunning performance improvement by transfer learning from the completely dissimilar source domain: ImageNet. Further study for demystifying this improvement shows that the transfer learning produces a highly compressible network, which was not the case for the network learned from scratch. The experimental result shows that there is a negligible accuracy drop in the network learned by transfer learning until it is compressed to 1/128 reduction of the number of convolution filters. This result is contrary to the compression without transfer learning which loses more than 5% accuracy at the same compression rate.", "output": [ "We experimentally show that transfer learning makes sparse features in the network and thereby produces a more compressible network. " ] }, { "id": "task668-5337f0b8a0564389adb28d22c41b5b5f", "input": "Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently. A popular approach to meta-learning is to train a recurrent model to read in a training dataset as input and output the parameters of a learned model, or output predictions for new test inputs. Alternatively, a more recent approach to meta-learning aims to acquire deep representations that can be effectively fine-tuned, via standard gradient descent, to new tasks. In this paper, we consider the meta-learning problem from the perspective of universality, formalizing the notion of learning algorithm approximation and comparing the expressive power of the aforementioned recurrent models to the more recent approaches that embed gradient descent into the meta-learner. In particular, we seek to answer the following question: does deep representation combined with standard gradient descent have sufficient capacity to approximate any learning algorithm? We find that this is indeed true, and further find, in our experiments, that gradient-based meta-learning consistently leads to learning strategies that generalize more widely compared to those represented by recurrent models.", "output": [ "Deep representations combined with gradient descent can approximate any learning algorithm." ] }, { "id": "task668-202dee6eb5d845e49dc33de3d15ba4c8", "input": "We develop new approximation and statistical learning theories of convolutional neural networks (CNNs) via the ResNet-type structure where the channel size, filter size, and width are fixed. It is shown that a ResNet-type CNN is a universal approximator and its expression ability is no worse than fully-connected neural networks (FNNs) with a \\textit{block-sparse} structure even if the size of each layer in the CNN is fixed. Our result is general in the sense that we can automatically translate any approximation rate achieved by block-sparse FNNs into that by CNNs. Thanks to the general theory, it is shown that learning on CNNs satisfies optimality in approximation and estimation of several important function classes.\n\n As applications, we consider two types of function classes to be estimated: the Barron class and H\\\"older class. We prove the clipped empirical risk minimization (ERM) estimator can achieve the same rate as FNNs even the channel size, filter size, and width of CNNs are constant with respect to the sample size. This is minimax optimal (up to logarithmic factors) for the H\\\"older class. Our proof is based on sophisticated evaluations of the covering number of CNNs and the non-trivial parameter rescaling technique to control the Lipschitz constant of CNNs to be constructed.", "output": [ "It is shown that ResNet-type CNNs are a universal approximator and its expression ability is not worse than fully connected neural networks (FNNs) with a \\textit{block-sparse} structure even if the size of each layer in the CNN is fixed." ] }, { "id": "task668-85423daac76946ea9acf02b7596206b9", "input": "Autoregressive recurrent neural decoders that generate sequences of tokens one-by-one and left-to-right are the workhorse of modern machine translation. In this work, we propose a new decoder architecture that can generate natural language sequences in an arbitrary order. Along with generating tokens from a given vocabulary, our model additionally learns to select the optimal position for each produced token. The proposed decoder architecture is fully compatible with the seq2seq framework and can be used as a drop-in replacement of any classical decoder. We demonstrate the performance of our new decoder on the IWSLT machine translation task as well as inspect and interpret the learned decoding patterns by analyzing how the model selects new positions for each subsequent token.", "output": [ "new out-of-order decoder for neural machine translation" ] }, { "id": "task668-733bc9c3e0cd4034b0f155fb767f999e", "input": "The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems. In this work, we study the learning to explain the problem in the scope of inductive logic programming (ILP). We propose Neural Logic Inductive Learning (NLIL), an efficient differentiable ILP framework that learns first-order logic rules that can explain the patterns in the data. In experiments, compared with the state-of-the-art models, we find NLIL is able to search for rules that are x10 times longer while remaining x3 times faster. We also show that NLIL can scale to large image datasets, i.e. Visual Genome, with 1M entities.", "output": [ "An efficient differentiable ILP model that learns first-order logic rules that can explain the data." ] }, { "id": "task668-186d0f382e1949e7b801d884687a7fa4", "input": "The reparameterization trick has become one of the most useful tools in the field of variational inference. However, the reparameterization trick is based on the standardization transformation which restricts the scope of application of this method to distributions that have tractable inverse cumulative distribution functions or are expressible as deterministic transformations of such distributions. In this paper, we generalized the reparameterization trick by allowing a general transformation. Unlike other similar works, we develop the generalized transformation-based gradient model formally and rigorously. We discover that the proposed model is a special case of control variate indicating that the proposed model can combine the advantages of CV and generalized reparameterization. Based on the proposed gradient model, we propose a new polynomial-based gradient estimator which has better theoretical performance than the reparameterization trick under certain condition and can be applied to a larger class of variational distributions. In studies of synthetic and real data, we show that our proposed gradient estimator has a significantly lower gradient variance than other state-of-the-art methods thus enabling a faster inference procedure.", "output": [ "We propose a novel generalized transformation-based gradient model and propose a polynomial-based gradient estimator based upon the model." ] }, { "id": "task668-2a336a92b002466dae234e1ddc0bb2b7", "input": "Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly learning a policy for each agent to maximize a common reward. In this paper, we aim to address this from a different angle. In particular, we consider scenarios where there are self-interested agents (i.e., worker agents) which have their own minds (preferences, intentions, skills, etc.) and can not be dictated to perform tasks they do not want to do. For achieving optimal coordination among these agents, we train a super agent (i.e., the manager) to manage them by first inferring their minds based on both current and past observations and then initiating contracts to assign suitable tasks to workers and promise to reward them with corresponding bonuses so that they will agree to work together. The objective of the manager is to maximize the overall productivity as well as minimize payments made to the workers for ad-hoc worker teaming. To train the manager, we propose Mind-aware Multi-agent Management Reinforcement Learning (M^3RL), which consists of agent modeling and policy learning. We have evaluated our approach in two environments, Resource Collection and Crafting, to simulate multi-agent management problems with various task settings and multiple designs for the worker agents. The experimental results have validated the effectiveness of our approach in modeling worker agents' minds online, and in achieving optimal ad-hoc teaming with good generalization and fast adaptation.", "output": [ "We propose Mind-aware Multi-agent Management Reinforcement Learning (M^3RL) for training a manager to motivate self-interested workers to achieve optimal collaboration by assigning suitable contracts to them." ] }, { "id": "task668-7219e142b66849ba94d9552f4360ff4b", "input": "Recent studies show that widely used Deep neural networks (DNNs) are vulnerable to the carefully crafted adversarial examples.\n Many advanced algorithms have been proposed to generate adversarial examples by leveraging the L_p distance for penalizing perturbations.\n Different defense methods have also been explored to defend against such adversarial attacks. \n While the effectiveness of L_p distance as a metric of perceptual quality remains an active research area, in this paper we will instead focus on a different type of perturbation, namely spatial transformation, as opposed to manipulating the pixel values directly as in prior works.\n Perturbations generated through spatial transformation could result in large L_p distance measures, but our extensive experiments show that such spatially transformed adversarial examples are perceptually realistic and more difficult to defend against with existing defense systems. This potentially provides a new direction in adversarial example generation and the design of corresponding defenses.\n We visualize the spatial transformation based perturbation for different examples and show that our technique\n can produce realistic adversarial examples with smooth image deformation.\n Finally, we visualize the attention of deep networks with different types of adversarial examples to better understand how these examples are interpreted.", "output": [ "We propose a new approach for generating adversarial examples based on spatial transformation, which produces perceptually realistic examples compared to existing attacks. " ] }, { "id": "task668-d32b6a9e43d74200b1d7fc8b45c372a9", "input": "Pruning is a popular technique for compressing a neural network: a large pre-trained network is fine-tuned while connections are successively removed. However, the value of pruning has largely evaded scrutiny. In this extended abstract, we examine residual networks obtained through Fisher-pruning and make two interesting observations. First, when time-constrained, it is better to train a simple, smaller network from scratch than prune a large network. Second, it is the architectures obtained through the pruning process --- not the learnt weights --- that prove valuable. Such architectures are powerful when trained from scratch. Furthermore, these architectures are easy to approximate without any further pruning: we can prune once and obtain a family of new, scalable network architectures for different memory requirements.", "output": [ "Training small networks beats pruning, but pruning finds good small networks to train that are easy to copy." ] }, { "id": "task668-a16112653e99490cae582c4ef9c94c0a", "input": "In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem. The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled constrained programming algorithm as a building block in the architecture to enforce constraints. With comprehensive experiments on benchmark datasets, we demonstrate the superior performance of E2Efold: it predicts significantly better structures compared to previous SOTA (29.7% improvement in some cases in F1 scores and even larger improvement for pseudoknotted structures) and runs as efficient as the fastest algorithms in terms of inference time.", "output": [ "A DL model for RNA secondary structure prediction, which uses an unrolled algorithm in the architecture to enforce constraints." ] }, { "id": "task668-718c98b6bfb844ad91dbb00aba4b69d8", "input": "We study how the topology of a data set comprising two components representing two classes of objects in a binary classification problem changes as it passes through the layers of a well-trained neural network, i.e., one with perfect accuracy on training set and a generalization error of less than 1%. The goal is to shed light on two well-known mysteries in deep neural networks: (i) a nonsmooth activation function like ReLU outperforms a smooth one like hyperbolic tangent; (ii) successful neural network architectures rely on having many layers, despite the fact that a shallow network is able to approximate any function arbitrary well. We performed extensive experiments on persistent homology of a range of point cloud data sets. The results consistently demonstrate the following: (1) Neural networks operate by changing topology, transforming a topologically complicated data set into a topologically simple one as it passes through the layers. No matter how complicated the topology of the data set we begin with, when passed through a well-trained neural network, the Betti numbers of both components invariably reduce to their lowest possible values: zeroth Betti number is one and all higher Betti numbers are zero. Furthermore, (2) the reduction in Betti numbers is significantly faster for ReLU activation compared to hyperbolic tangent activation --- consistent with the fact that the former define nonhomeomorphic maps (that change topology) whereas the latter define homeomorphic maps (that preserve topology). Lastly, (3) shallow and deep networks process the same data set differently --- a shallow network operates mainly through changing geometry and changes topology only in its final layers, a deep network spreads topological changes more evenly across all its layers.", "output": [ "We show that neural networks operate by changing topologly of a data set and explore how architectural choices effect this change." ] }, { "id": "task668-82b67b8df86a4d5d84132e7ba12b6732", "input": "This paper studies the undesired phenomena of over-sensitivity of representations learned by deep networks to semantically-irrelevant changes in data. We identify a cause for this shortcoming in the classical Variational Auto-encoder (VAE) objective, the evidence lower bound (ELBO). We show that the ELBO fails to control the behaviour of the encoder out of the support of the empirical data distribution and this behaviour of the VAE can lead to extreme errors in the learned representation. This is a key hurdle in the effective use of representations for data-efficient learning and transfer. To address this problem, we propose to augment the data with specifications that enforce insensitivity of the representation with respect to families of transformations. To incorporate these specifications, we propose a regularization method that is based on a selection mechanism that creates a fictive data point by explicitly perturbing an observed true data point. For certain choices of parameters, our formulation naturally leads to the minimization of the entropy regularized Wasserstein distance between representations. We illustrate our approach on standard datasets and experimentally show that significant improvements in the downstream adversarial accuracy can be achieved by learning robust representations completely in an unsupervised manner, without a reference to a particular downstream task and without a costly supervised adversarial training procedure. \n", "output": [ "We propose a method for computing adversarially robust representations in an entirely unsupervised way." ] }, { "id": "task668-32cdb9d9fbef494ab32d34229d1dc411", "input": "We present methodology for using dynamic evaluation to improve neural sequence models. Models are adapted to recent history via a gradient descent based mechanism, causing them to assign higher probabilities to re-occurring sequential patterns. Dynamic evaluation outperforms existing adaptation approaches in our comparisons. Dynamic evaluation improves the state-of-the-art word-level perplexities on the Penn Treebank and WikiText-2 datasets to 51.1 and 44.3 respectively, and the state-of-the-art character-level cross-entropies on the text8 and Hutter Prize datasets to 1.19 bits/char and 1.08 bits/char respectively.", "output": [ "Paper presents dynamic evaluation methodology for adaptive sequence modelling" ] }, { "id": "task668-43c459521d894f95a26953c4c2f55222", "input": "Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents. Here we explore whether modern deep reinforcement learning can be used to train agents to perform causal reasoning. We adopt a meta-learning approach, where the agent learns a policy for conducting experiments via causal interventions, in order to support a subsequent task which rewards making accurate causal inferences.We also found the agent could make sophisticated counterfactual predictions, as well as learn to draw causal inferences from purely observational data. Though powerful formalisms for causal reasoning have been developed, applying them in real-world domains can be difficult because fitting to large amounts of high dimensional data often requires making idealized assumptions. Our results suggest that causal reasoning in complex settings may benefit from powerful learning-based approaches. More generally, this work may offer new strategies for structured exploration in reinforcement learning, by providing agents with the ability to perform—and interpret—experiments.", "output": [ "meta-learn a learning algorithm capable of causal reasoning" ] }, { "id": "task668-4f89a0a8e10d494eb6c350fc55806326", "input": "An unintended consequence of feature sharing is the model fitting to correlated tasks within the dataset, termed negative transfer. In this paper, we revisit the problem of negative transfer in multitask setting and find that its corrosive effects are applicable to a wide range of linear and non-linear models, including neural networks. We first study the effects of negative transfer in a principled way and show that previously proposed counter-measures are insufficient, particularly for trainable features. We propose an adversarial training approach to mitigate the effects of negative transfer by viewing the problem in a domain adaptation setting. Finally, empirical results on attribute prediction multi-task on AWA and CUB datasets further validate the need for correcting negative sharing in an end-to-end manner.", "output": [ "We look at negative transfer from a domain adaptation point of view to derive an adversarial learning algorithm." ] }, { "id": "task668-b6f4e569ae914e0e9fd3709da02bb20a", "input": "The knowledge that humans hold about a problem often extends far beyond a set of training data and output labels. While the success of deep learning mostly relies on supervised training, important properties cannot be inferred efficiently from end-to-end annotations alone, for example causal relations or domain-specific invariances. We present a general technique to supplement supervised training with prior knowledge expressed as relations between training instances. We illustrate the method on the task of visual question answering to exploit various auxiliary annotations, including relations of equivalence and of logical entailment between questions. Existing methods to use these annotations, including auxiliary losses and data augmentation, cannot guarantee the strict inclusion of these relations into the model since they require a careful balancing against the end-to-end objective. Our method uses these relations to shape the embedding space of the model, and treats them as strict constraints on its learned representations. %The resulting model encodes relations that better generalize across instances. In the context of VQA, this approach brings significant improvements in accuracy and robustness, in particular over the common practice of incorporating the constraints as a soft regularizer. We also show that incorporating this type of prior knowledge with our method brings consistent improvements, independently from the amount of supervised data used. It demonstrates the value of an additional training signal that is otherwise difficult to extract from end-to-end annotations alone.", "output": [ "Training method to enforce strict constraints on learned embeddings during supervised training. Applied to visual question answering.", "The authors propose a framework to incorporate additional semantic prior knowledge into the traditional training of deep learning models to regularize the embedding space instead of the parameter space.", "The paper argues for encoding external knowledge in the linguistic embedding layer of a multimodal neural network, as a set of hard constraints." ] }, { "id": "task668-4bb4804034c64eb691d2dbd02d442d92", "input": "Pruning neural network parameters is often viewed as a means to compress models, but pruning has also been motivated by the desire to prevent overfitting. This motivation is particularly relevant given the perhaps surprising observation that a wide variety of pruning approaches increase test accuracy despite sometimes massive reductions in parameter counts. To better understand this phenomenon, we analyze the behavior of pruning over the course of training, finding that pruning's effect on generalization relies more on the instability it generates (defined as the drops in test accuracy immediately following pruning) than on the final size of the pruned model. We demonstrate that even the pruning of unimportant parameters can lead to such instability, and show similarities between pruning and regularizing by injecting noise, suggesting a mechanism for pruning-based generalization improvements that is compatible with the strong generalization recently observed in over-parameterized networks.", "output": [ "We demonstrate that pruning methods which introduce greater instability into the loss also confer improved generalization, and explore the mechanisms underlying this effect." ] }, { "id": "task668-f6c2086af91b45d8bea76ae06de20e35", "input": "We consider the task of answering complex multi-hop questions using a corpus as a virtual knowledge base (KB). In particular, we describe a neural module, DrKIT, that traverses textual data like a virtual KB, softly following paths of relations between mentions of entities in the corpus. At each step the operation uses a combination of sparse-matrix TFIDF indices and maximum inner product search (MIPS) on a special index of contextual representations. This module is differentiable, so the full system can be trained completely end-to-end using gradient based methods, starting from natural language inputs. We also describe a pretraining scheme for the index mention encoder by generating hard negative examples using existing knowledge bases. We show that DrKIT improves accuracy by 9 points on 3-hop questions in the MetaQA dataset, cutting the gap between text-based and KB-based state-of-the-art by 70%. DrKIT is also very efficient, processing upto 10x more queries per second than existing state-of-the-art QA systems.", "output": [ "Differentiable multi-hop access to a textual knowledge base of indexed contextual representations" ] }, { "id": "task668-49dea11621a7424aa861fd541be40076", "input": "While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. \n We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in another language. XLDA enhances performance of all 14 tested languages of the cross-lingual natural language inference (XNLI) benchmark. With improvements of up to 4.8, training with XLDA achieves state-of-the-art performance for Greek, Turkish, and Urdu. XLDA is in contrast to, and performs markedly better than, a more naive approach that aggregates examples in various languages in a way that each example is solely in one language. On the SQuAD question answering task, we see that XLDA provides a 1.0 performance increase on the English evaluation set. Comprehensive experiments suggest that most languages are effective as cross-lingual augmentors, that XLDA is robust to a wide range of translation quality, and that XLDA is even more effective for randomly initialized models than for pretrained models.", "output": [ "Translating portions of the input during training can improve cross-lingual performance.", "The paper proposes a cross-lingual data augmentation method to improve the language inference and question answering tasks.", "This paper proposes to augment crosslingual data with heuristic swaps using aligned translations, like bilingual humans do in code-switching." ] }, { "id": "task668-d1ca8508134441ef97b785e84164c4a9", "input": "The Variational Auto Encoder (VAE) is a popular generative \nlatent variable model that is often \napplied for representation learning.\n Standard VAEs assume continuous valued \nlatent variables and are trained by maximization\nof the evidence lower bound (ELBO). Conventional methods obtain a \ndifferentiable estimate of the ELBO with reparametrized sampling and\noptimize it with Stochastic Gradient Descend (SGD). However, this is not possible if \nwe want to train VAEs with discrete valued latent variables, \nsince reparametrized sampling is not possible. Till now, there\nexist no simple solutions to circumvent this problem.\n In this paper, we propose an easy method to train VAEs \nwith binary or categorically valued latent representations. Therefore, we use a differentiable\nestimator for the ELBO which is based on importance sampling. In experiments, we verify the approach and\ntrain two different VAEs architectures with Bernoulli and \nCategorically distributed latent representations on two different benchmark\ndatasets.\t", "output": [ "We propose an easy method to train Variational Auto Encoders (VAE) with discrete latent representations, using importance sampling", "Introducting an importance sampling distribution and using samples from distribution to compute importance-weighted estimate of the gradient", "This paper proposes to use important sampling to optimize VAE with discrete latent variables." ] }, { "id": "task668-c057473b76864dd4a55ba92957265f80", "input": "We propose GraphGAN - the first implicit generative model for graphs that enables to mimic real-world networks.\n We pose the problem of graph generation as learning the distribution of biased random walks over a single input graph.\n Our model is based on a stochastic neural network that generates discrete output samples, and is trained using the Wasserstein GAN objective. GraphGAN enables us to generate sibling graphs, which have similar properties yet are not exact replicas of the original graph. Moreover, GraphGAN learns a semantic mapping from the latent input space to the generated graph's properties. We discover that sampling from certain regions of the latent space leads to varying properties of the output graphs, with smooth transitions between them. Strong generalization properties of GraphGAN are highlighted by its competitive performance in link prediction as well as promising results on node classification, even though not specifically trained for these tasks.", "output": [ "Using GANs to generate graphs via random walks.", "The authors proposed a generative model of random walks on graphs that allows for model-agnostic learning, controllable fitting, ensemble graph generation", "Proposes a WGAN formulation for generating graphs based on random walks using node embeddings and an LSTM architecture for modeling." ] }, { "id": "task668-b3a6a9ef206d4a4ba3361746ab5cb18d", "input": "Reinforcement learning agents need to explore their unknown environments to solve the tasks given to them. The Bayes optimal solution to exploration is intractable for complex environments, and while several exploration methods have been proposed as approximations, it remains unclear what underlying objective is being optimized by existing exploration methods, or how they can be altered to incorporate prior knowledge about the task. Moreover, it is unclear how to acquire a single exploration strategy that will be useful for solving multiple downstream tasks. We address these shortcomings by learning a single exploration policy that can quickly solve a suite of downstream tasks in a multi-task setting, amortizing the cost of learning to explore. We recast exploration as a problem of State Marginal Matching (SMM), where we aim to learn a policy for which the state marginal distribution matches a given target state distribution, which can incorporate prior knowledge about the task. We optimize the objective by reducing it to a two-player, zero-sum game between a state density model and a parametric policy. Our theoretical analysis of this approach suggests that prior exploration methods do not learn a policy that does distribution matching, but acquire a replay buffer that performs distribution matching, an observation that potentially explains these prior methods' success in single-task settings. On both simulated and real-world tasks, we demonstrate that our algorithm explores faster and adapts more quickly than prior methods.", "output": [ "We view exploration in RL as a problem of matching a marginal distribution over states." ] }, { "id": "task668-cc413428c7614ee780da6644d19c1858", "input": "Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints. Cloud-based training schemes provide beneficial services but suffer from potential privacy risks due to excessive user data collection. To enable cloud-based DNN training while protecting the data privacy simultaneously, we propose to leverage the intermediate representations of the data, which is achieved by splitting the DNNs and deploying them separately onto local platforms and the cloud. The local neural network (NN) is used to generate the feature representations. To avoid local training and protect data privacy, the local NN is derived from pre-trained NNs. The cloud NN is then trained based on the extracted intermediate representations for the target learning task. We validate the idea of DNN splitting by characterizing the dependency of privacy loss and classification accuracy on the local NN topology for a convolutional NN (CNN) based image classification task. Based on the characterization, we further propose PrivyNet to determine the local NN topology, which optimizes the accuracy of the target learning task under the constraints on privacy loss, local computation, and storage. The efficiency and effectiveness of PrivyNet are demonstrated with CIFAR-10 dataset.", "output": [ "To enable cloud-based DNN training while protecting the data privacy simultaneously, we propose to leverage the intermediate data representations, which is achieved by splitting the DNNs and deploying them separately onto local platforms and the cloud.", "This paper proposes a technique to privatize data by learning a feature representation that is difficult to use for image reconstruction, but helpful for image classification." ] }, { "id": "task668-abde6bd359164bb3997410b5c00d3637", "input": "Knowledge graph has gained increasing attention in recent years for its successful applications of numerous tasks. Despite the rapid growth of knowledge construction, knowledge graphs still suffer from severe incompletion and inevitably involve various kinds of errors. Several attempts have been made to complete knowledge graph as well as to detect noise. However, none of them considers unifying these two tasks even though they are inter-dependent and can mutually boost the performance of each other. In this paper, we proposed to jointly combine these two tasks with a unified Generative Adversarial Networks (GAN) framework to learn noise-aware knowledge graph embedding. Extensive experiments have demonstrated that our approach is superior to existing state-of-the-art algorithms both in regard to knowledge graph completion and error detection.", "output": [ "We proposed a unified Generative Adversarial Networks (GAN) framework to learn noise-aware knowledge graph embedding." ] }, { "id": "task668-4d004a6d727b46b78c1495961387e99a", "input": "In complex transfer learning scenarios new tasks might not be tightly linked to previous tasks. Approaches that transfer information contained only in the final parameters of a source model will therefore struggle. Instead, transfer learning at at higher level of abstraction is needed. We propose Leap, a framework that achieves this by transferring knowledge across learning processes. We associate each task with a manifold on which the training process travels from initialization to final parameters and construct a meta-learning objective that minimizes the expected length of this path. Our framework leverages only information obtained during training and can be computed on the fly at negligible cost. We demonstrate that our framework outperforms competing methods, both in meta-learning and transfer learning, on a set of computer vision tasks. Finally, we demonstrate that Leap can transfer knowledge across learning processes in demanding reinforcement learning environments (Atari) that involve millions of gradient steps.", "output": [ "We propose Leap, a framework that transfers knowledge across learning processes by minimizing the expected distance the training process travels on a task's loss surface.", "The article proposes a novel meta-learning objective aimed at outperforming state-of-the-art approaches when dealing with collections of tasks that exhibit substantial between-task diversity" ] }, { "id": "task668-e07b436e1f4540df8e17db8b06d63a7c", "input": "Learned representations of source code enable various software developer tools, e.g., to detect bugs or to predict program properties. At the core of code representations often are word embeddings of identifier names in source code, because identifiers account for the majority of source code vocabulary and convey important semantic information. Unfortunately, there currently is no generally accepted way of evaluating the quality of word embeddings of identifiers, and current evaluations are biased toward specific downstream tasks. This paper presents IdBench, the first benchmark for evaluating to what extent word embeddings of identifiers represent semantic relatedness and similarity. The benchmark is based on thousands of ratings gathered by surveying 500 software developers. We use IdBench to evaluate state-of-the-art embedding techniques proposed for natural language, an embedding technique specifically designed for source code, and lexical string distance functions, as these are often used in current developer tools. Our results show that the effectiveness of embeddings varies significantly across different embedding techniques and that the best available embeddings successfully represent semantic relatedness. On the downside, no existing embedding provides a satisfactory representation of semantic similarities, e.g., because embeddings consider identifiers with opposing meanings as similar, which may lead to fatal mistakes in downstream developer tools. IdBench provides a gold standard to guide the development of novel embeddings that address the current limitations.\n", "output": [ "A benchmark to evaluate neural embeddings of identifiers in source code." ] }, { "id": "task668-0eed9af5e15a42319189b40d7068c434", "input": "Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech. Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable. In particular, there is no sufficient evidence that deep learning machinery allows constructing methods that outperform gradient boosting decision trees (GBDT), which are often the top choice for tabular problems. In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data. In a nutshell, the proposed NODE architecture generalizes ensembles of oblivious decision trees, but benefits from both end-to-end gradient-based optimization and the power of multi-layer hierarchical representation learning. With an extensive experimental comparison to the leading GBDT packages on a large number of tabular datasets, we demonstrate the advantage of the proposed NODE architecture, which outperforms the competitors on most of the tasks. We open-source the PyTorch implementation of NODE and believe that it will become a universal framework for machine learning on tabular data.", "output": [ "We propose a new DNN architecture for deep learning on tabular data" ] }, { "id": "task668-bb0fae51b866451783d0e36142839919", "input": "A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient. Recently, Jin et al. (2018) proposed a Q-learning algorithm with UCB exploration policy, and proved it has nearly optimal regret bound for finite-horizon episodic MDP. In this paper, we adapt Q-learning with UCB-exploration bonus to infinite-horizon MDP with discounted rewards \\emph{without} accessing a generative model. We show that the \\textit{sample complexity of exploration} of our algorithm is bounded by $\\tilde{O}({\\frac{SA}{\\epsilon^2(1-\\gamma)^7}})$. This improves the previously best known result of $\\tilde{O}({\\frac{SA}{\\epsilon^4(1-\\gamma)^8}})$ in this setting achieved by delayed Q-learning (Strehlet al., 2006),, and matches the lower bound in terms of $\\epsilon$ as well as $S$ and $A$ up to logarithmic factors.", "output": [ "We adapt Q-learning with UCB-exploration bonus to infinite-horizon MDP with discounted rewards without accessing a generative model, and improves the previously best known result.", "This paper considered a Q-learning algorithm with UCB exploration policy for infinite-horizon MDP." ] }, { "id": "task668-028f12442ff740a8a79c09534bd68913", "input": "Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input. Current approaches relying on substitute model training, gradient estimation or genetic algorithms often require an excessive number of queries. Therefore, they are not suitable for real-world systems where the maximum query number is limited due to cost. We propose a query-efficient black-box attack which uses Bayesian optimisation in combination with Bayesian model selection to optimise over the adversarial perturbation and the optimal degree of search space dimension reduction. We demonstrate empirically that our method can achieve comparable success rates with 2-5 times fewer queries compared to previous state-of-the-art black-box attacks.", "output": [ "We propose a query-efficient black-box attack which uses Bayesian optimisation in combination with Bayesian model selection to optimise over the adversarial perturbation and the optimal degree of search space dimension reduction. ", "The authors propose to use Bayesian optimization with a GP surrogate for adversarial image generation, by exploiting additive structure and using Bayesian model selection to determine an optimal dimensionality reduction." ] }, { "id": "task668-82bf9e547f3440dbb1f8d4c2a7b3328c", "input": "Super Resolution (SR) is a fundamental and important low-level computer vision (CV) task. Different from traditional SR models, this study concentrates on a specific but realistic SR issue: How can we obtain satisfied SR results from compressed JPG (C-JPG) image, which widely exists on the Internet. In general, C-JPG can release storage space while keeping considerable quality in visual. However, further image processing operations, e.g., SR, will suffer from enlarging inner artificial details and result in unacceptable outputs. To address this problem, we propose a novel SR structure with two specifically designed components, as well as a cycle loss. In short, there are mainly three contributions to this paper. First, our research can generate high-qualified SR images for prevalent C-JPG images. Second, we propose a functional sub-model to recover information for C-JPG images, instead of the perspective of noise elimination in traditional SR approaches. Third, we further integrate cycle loss into SR solver to build a hybrid loss function for better SR generation. Experiments show that our approach achieves outstanding performance among state-of-the-art methods.", "output": [ "We solve the specific SR issue of low-quality JPG images by functional sub-models." ] }, { "id": "task668-56cd428a7b2840fd905d049f8a2b43f3", "input": "We study SGD and Adam for estimating a rank one signal planted in matrix or tensor noise. The extreme simplicity of the problem setup allows us to isolate the effects of various factors: signal to noise ratio, density of critical points, stochasticity and initialization. We observe a surprising phenomenon: Adam seems to get stuck in local minima as soon as polynomially many critical points appear (matrix case), while SGD escapes those. However, when the number of critical points degenerates to exponentials (tensor case), then both algorithms get trapped. Theory tells us that at fixed SNR the problem becomes intractable for large $d$ and in our experiments SGD does not escape this. We exhibit the benefits of warm starting in those situations. We conclude that in this class of problems, warm starting cannot be replaced by stochasticity in gradients to find the basin of attraction.", "output": [ "SGD and Adam under single spiked model for tensor PCA" ] }, { "id": "task668-6cb2c619c39c4a81bc7467ca4ef61207", "input": "In this paper, we study deep diagonal circulant neural networks, that is deep neural networks in which weight matrices are the product of diagonal and circulant ones.\n Besides making a theoretical analysis of their expressivity, we introduced principled techniques for training these models: we devise an initialization scheme and proposed a smart use of non-linearity functions in order to train deep diagonal circulant networks. \n Furthermore, we show that these networks outperform recently introduced deep networks with other types of structured layers. We conduct a thorough experimental study to compare the performance of deep diagonal circulant networks with state of the art models based on structured matrices and with dense models. We show that our models achieve better accuracy than other structured approaches while required 2x fewer weights as the next best approach. Finally we train deep diagonal circulant networks to build a compact and accurate models on a real world video classification dataset with over 3.8 million training examples.", "output": [ "We train deep neural networks based on diagonal and circulant matrices, and show that this type of networks are both compact and accurate on real world applications.", "The authors provide a theoretical analysis of the expressive power of diagonal circulant neural networks (DCNN) and propose an initialization scheme for deep DCNNs." ] }, { "id": "task668-942953a13ebb4bb9a4a7c53c2fa639f5", "input": "We introduce the open-ended, modular, self-improving Omega AI unification architecture which is a refinement of Solomonoff's Alpha architecture, as considered from first principles. The architecture embodies several crucial principles of general intelligence including diversity of representations, diversity of data types, integrated memory, modularity, and higher-order cognition. We retain the basic design of a fundamental algorithmic substrate called an ``AI kernel'' for problem solving and basic cognitive functions like memory, and a larger, modular architecture that re-uses the kernel in many ways. Omega includes eight representation languages and six classes of neural networks, which are briefly introduced. The architecture is intended to initially address data science automation, hence it includes many problem solving methods for statistical tasks. We review the broad software architecture, higher-order cognition, self-improvement, modular neural architectures, intelligent agents, the process and memory hierarchy, hardware abstraction, peer-to-peer computing, and data abstraction facility.", "output": [ "It's a new AGI architecture for trans-sapient performance.This is a high-level overview of the Omega AGI architecture which is the basis of a data science automation system. Submitted to a workshop. " ] }, { "id": "task668-680add77d18c4f80b74a3b95d5ee25ec", "input": "Modern Convolutional Neural Networks (CNNs) are complex, encompassing millions of parameters. Their deployment exerts computational, storage and energy demands, particularly on embedded platforms. Existing approaches to prune or sparsify CNNs require retraining to maintain inference accuracy. Such retraining is not feasible in some contexts. In this paper, we explore the sparsification of CNNs by proposing three model-independent methods. Our methods are applied on-the-fly and require no retraining. We show that the state-of-the-art models' weights can be reduced by up to 73% (compression factor of 3.7x) without incurring more than 5% loss in Top-5 accuracy. Additional fine-tuning gains only 8% in sparsity, which indicates that our fast on-the-fly methods are effective.", "output": [ "In this paper, we develop fast retraining-free sparsification methods that can be deployed for on-the-fly sparsification of CNNs in many industrial contexts.", "This paper proposes approaches for pruning CNNs without retraining by introducing three schemes to determine the thresholds of pruning weights.", "This paper describes a method for sparsification of CNNs without retraining." ] }, { "id": "task668-ec2a6d17ebd14da38610dbcf65342b7a", "input": "We introduce MTLAB, a new algorithm for learning multiple related tasks with strong theoretical guarantees. Its key idea is to perform learning sequentially over the data of all tasks, without interruptions or restarts at task boundaries. Predictors for individual tasks are derived from this process by an additional online-to-batch conversion step.\n\n By learning across task boundaries, MTLAB achieves a sublinear regret of true risks in the number of tasks. In the lifelong learning setting, this leads to an improved generalization bound that converges with the total number of samples across all observed tasks, instead of the number of examples per tasks or the number of tasks independently. At the same time, it is widely applicable: it can handle finite sets of tasks, as common in multi-task learning, as well as stochastic task sequences, as studied in lifelong learning.", "output": [ "A new algorithm for online multi-task learning that learns without restarts at the task borders" ] }, { "id": "task668-53154cd1dada40bfa0c8ce323cac87b7", "input": "The primate visual system builds robust, multi-purpose representations of the external world in order to support several diverse downstream cortical processes. Such representations are required to be invariant to the sensory inconsistencies caused by dynamically varying lighting, local texture distortion, etc. A key architectural feature combating such environmental irregularities is ‘long-range horizontal connections’ that aid the perception of the global form of objects. In this work, we explore the introduction of such horizontal connections into standard deep convolutional networks; we present V1Net -- a novel convolutional-recurrent unit that models linear and nonlinear horizontal inhibitory and excitatory connections inspired by primate visual cortical connectivity. We introduce the Texturized Challenge -- a new benchmark to evaluate object recognition performance under perceptual noise -- which we use to evaluate V1Net against an array of carefully selected control models with/without recurrent processing. Additionally, we present results from an ablation study of V1Net demonstrating the utility of diverse neurally inspired horizontal connections for state-of-the-art AI systems on the task of object boundary detection from natural images. We also present the emergence of several biologically plausible horizontal connectivity patterns, namely center-on surround-off, association fields and border-ownership connectivity patterns in a V1Net model trained to perform boundary detection on natural images from the Berkeley Segmentation Dataset 500 (BSDS500). Our findings suggest an increased representational similarity between V1Net and biological visual systems, and highlight the importance of neurally inspired recurrent contextual processing principles for learning visual representations that are robust to perceptual noise and furthering the state-of-the-art in computer vision.", "output": [ "In this work, we present V1Net -- a novel recurrent neural network modeling cortical horizontal connections that give rise to robust visual representations through perceptual grouping.", "The authors propose to modify a convolutional variant of LSTM to include horizontal connections inspired by known interactions in visual cortex." ] }, { "id": "task668-3dffebd9453f45e48e38f827482c2142", "input": "In deep learning, performance is strongly affected by the choice of architecture\n and hyperparameters. While there has been extensive work on automatic hyperpa-\n rameter optimization for simple spaces, complex spaces such as the space of deep\n architectures remain largely unexplored. As a result, the choice of architecture is\n done manually by the human expert through a slow trial and error process guided\n mainly by intuition. In this paper we describe a framework for automatically\n designing and training deep models. We propose an extensible and modular lan-\n guage that allows the human expert to compactly represent complex search spaces\n over architectures and their hyperparameters. The resulting search spaces are tree-\n structured and therefore easy to traverse. Models can be automatically compiled to\n computational graphs once values for all hyperparameters have been chosen. We\n can leverage the structure of the search space to introduce different model search\n algorithms, such as random search, Monte Carlo tree search (MCTS), and sequen-\n tial model-based optimization (SMBO). We present experiments comparing the\n different algorithms on CIFAR-10 and show that MCTS and SMBO outperform\n random search. We also present experiments on MNIST, showing that the same\n search space achieves near state-of-the-art performance with a few samples. These\n experiments show that our framework can be used effectively for model discov-\n ery, as it is possible to describe expressive search spaces and discover competitive\n models without much effort from the human expert. Code for our framework and\n experiments has been made publicly available", "output": [ "We describe a modular and composable language for describing expressive search spaces over architectures and simple model search algorithms applied to these search spaces. " ] }, { "id": "task668-3ef557dc6ecc4f35bffc55b125498c41", "input": "A widely observed phenomenon in deep learning is the degradation problem: increasing\n the depth of a network leads to a decrease in performance on both test and training data. Novel architectures such as ResNets and Highway networks have addressed this issue by introducing various flavors of skip-connections or gating mechanisms. However, the degradation problem persists in the context of plain feed-forward networks. In this work we propose a simple method to address this issue. The proposed method poses the learning of weights in deep networks as a constrained optimization problem where the presence of skip-connections is penalized by Lagrange multipliers. This allows for skip-connections to be introduced during the early stages of training and subsequently phased out in a principled manner. We demonstrate the benefits of such an approach with experiments on MNIST, fashion-MNIST, CIFAR-10 and CIFAR-100 where the proposed method is shown to greatly decrease the degradation effect (compared to plain networks) and is often competitive with ResNets.", "output": [ "Phasing out skip-connections in a principled manner avoids degradation in deep feed-forward networks.", "The authors present a new training strategy, VAN, for training very deep feed-forward networks without skip connections", "The paper introduces an architecture that linearly interpolates between ResNets and vanilla deep nets without skip connections." ] }, { "id": "task668-62a0c002ade140ecb64c1c02bfcbc5ea", "input": "There are many differences between convolutional networks and the ventral visual streams of primates. For example, standard convolutional networks lack recurrent and lateral connections, cell dynamics, etc. However, their feedforward architectures are somewhat similar to the ventral stream, and warrant a more detailed comparison. A recent study found that the feedforward architecture of the visual cortex could be closely approximated as a convolutional network, but the resulting architecture differed from widely used deep networks in several ways. The same study also found, somewhat surprisingly, that training the ventral stream of this network for object recognition resulted in poor performance. This paper examines the performance of this network in more detail. In particular, I made a number of changes to the ventral-stream-based architecture, to make it more like a DenseNet, and tested performance at each step. I chose DenseNet because it has a high BrainScore, and because it has some cortex-like architectural features such as large in-degrees and long skip connections. Most of the changes (which made the cortex-like network more like DenseNet) improved performance. Further work is needed to better understand these results. One possibility is that details of the ventral-stream architecture may be ill-suited to feedforward computation, simple processing units, and/or backpropagation, which could suggest differences between the way high-performance deep networks and the brain approach core object recognition.", "output": [ "An approximation of primate ventral stream as a convolutional network performs poorly on object recognition, and multiple architectural features contribute to this. " ] }, { "id": "task668-a36c21f771264e6f8387c826f4dd5373", "input": "To communicate, to ground hypotheses, to analyse data, neuroscientists often refer to divisions of the brain. Here we consider atlases used to parcellate the brain when studying brain function. We discuss the meaning and the validity of these parcellations, from a conceptual point of view as well as by running various analytical tasks on popular functional brain parcellations.", "output": [ "All functional brain parcellations are wrong, but some are useful" ] }, { "id": "task668-5dbc9843055a4cc3ace55a462f08cd34", "input": "Knowledge graph embedding research has overlooked the problem of probability calibration. We show popular embedding models are indeed uncalibrated. That means probability estimates associated to predicted triples are unreliable. We present a novel method to calibrate a model when ground truth negatives are not available, which is the usual case in knowledge graphs. We propose to use Platt scaling and isotonic regression alongside our method. Experiments on three datasets with ground truth negatives show our contribution leads to well calibrated models when compared to the gold standard of using negatives. We get significantly better results than the uncalibrated models from all calibration methods. We show isotonic regression offers the best the performance overall, not without trade-offs. We also show that calibrated models reach state-of-the-art accuracy without the need to define relation-specific decision thresholds.", "output": [ "We propose a novel method to calibrate knowledge graph embedding models without the need of negative examples." ] }, { "id": "task668-5f6993488ce84557adea4c4b70b9137e", "input": "Curriculum learning and Self paced learning are popular topics in the machine learning that suggest to put the training samples in order by considering their difficulty levels. Studies in these topics show that starting with a small training set and adding new samples according to difficulty levels improves the learning performance. In this paper we experimented that we can also obtain good results by adding the samples randomly without a meaningful order. We compared our method with classical training, Curriculum learning, Self paced learning and their reverse ordered versions. Results of the statistical tests show that the proposed method is better than classical method and similar with the others. These results point a new training regime that removes the process of difficulty level determination in Curriculum and Self paced learning and as successful as these methods.", "output": [ "We propose that training with growing sets stage-by-stage provides an optimization for neural networks.", "The authors compare curriculum learning to learning in a random order with stages that add a new sample of examples to the previously, randomly constructed set", "This paper studies the influence of ordering in the Curriculum and Self paced learning, and shows that to some extent the ordering of training instances is not important." ] }, { "id": "task668-98a2c1cb417b42d0b8a934f1c35f46eb", "input": "In this paper, we consider the specific problem of word-level language modeling and investigate strategies for regularizing and optimizing LSTM-based models. We propose the weight-dropped LSTM, which uses DropConnect on hidden-to-hidden weights, as a form of recurrent regularization. Further, we introduce NT-ASGD, a non-monotonically triggered (NT) variant of the averaged stochastic gradient method (ASGD), wherein the averaging trigger is determined using a NT condition as opposed to being tuned by the user. Using these and other regularization strategies, our ASGD Weight-Dropped LSTM (AWD-LSTM) achieves state-of-the-art word level perplexities on two data sets: 57.3 on Penn Treebank and 65.8 on WikiText-2. In exploring the effectiveness of a neural cache in conjunction with our proposed model, we achieve an even lower state-of-the-art perplexity of 52.8 on Penn Treebank and 52.0 on WikiText-2. We also explore the viability of the proposed regularization and optimization strategies in the context of the quasi-recurrent neural network (QRNN) and demonstrate comparable performance to the AWD-LSTM counterpart. The code for reproducing the results is open sourced and is available at https://github.com/salesforce/awd-lstm-lm.", "output": [ "Effective regularization and optimization strategies for LSTM-based language models achieves SOTA on PTB and WT2. " ] }, { "id": "task668-c51a7765e30b4f73b311a8550b5fa049", "input": "In this paper, we propose the Associative Conversation Model that generates visual information from textual information and uses it for generating sentences in order to utilize visual information in a dialogue system without image input. In research on Neural Machine Translation, there are studies that generate translated sentences using both images and sentences, and these studies show that visual information improves translation performance. However, it is not possible to use sentence generation algorithms using images for the dialogue systems since many text-based dialogue systems only accept text input. Our approach generates (associates) visual information from input text and generates response text using context vector fusing associative visual information and sentence textual information. A comparative experiment between our proposed model and a model without association showed that our proposed model is generating useful sentences by associating visual information related to sentences. Furthermore, analysis experiment of visual association showed that our proposed model generates (associates) visual information effective for sentence generation.", "output": [ "Proposal of the sentence generation method based on fusion between textual information and visual information associated with the textual information", "This work describes a deep learning model for dialogue systems that takes advantage of visual information.", "This paper proposes a novel dataset for grounded dialog and makes a computational observation that it could help to reason about vision even when performing text-based dialog.", "Proposes to augment traditional text-based sentence generation/dialogue approaches by incorporating visual information by collecting a bunch of data consisting of both text and associated images or video" ] }, { "id": "task668-2159ec8dfb1142d4be663fc810143d62", "input": "We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible. The algorithm decouples acting from learning: the actors interact with their own instances of the environment by selecting actions according to a shared neural network, and accumulate the resulting experience in a shared experience replay memory; the learner replays samples of experience and updates the neural network. The architecture relies on prioritized experience replay to focus only on the most significant data generated by the actors. Our architecture substantially improves the state of the art on the Arcade Learning Environment, achieving better final performance in a fraction of the wall-clock training time.", "output": [ "A distributed architecture for deep reinforcement learning at scale, using parallel data-generation to improve the state of the art on the Arcade Learning Environment benchmark in a fraction of the wall-clock training time of previous approaches.", "Examines a distirbuted Deep RL system in which experiences, rather than gradients, are shared between the parallel works and the cetralized learner", "A parallel approach to DQN training, based on the idea of having multiple actors collecting data in parallel while a single learner trains the model from experiences sampled from central replay memory.", "This paper proposes a distributed architecture for deep reinforcement learning at scale, focusing on adding parallelization in actor algorithm in Prioritized Experience Replay framework" ] }, { "id": "task668-2921767ee56148629b7d36ee16d42b01", "input": "In this work, we face the problem of unsupervised domain adaptation with a novel deep learning approach which leverages our finding that entropy minimization is induced by the optimal alignment of second order statistics between source and target domains. We formally demonstrate this hypothesis and, aiming at achieving an optimal alignment in practical cases, we adopt a more principled strategy which, differently from the current Euclidean approaches, deploys alignment along geodesics. Our pipeline can be implemented by adding to the standard classification loss (on the labeled source domain), a source-to-target regularizer that is weighted in an unsupervised and data-driven fashion. We provide extensive experiments to assess the superiority of our framework on standard domain and modality adaptation benchmarks.", "output": [ "A new unsupervised deep domain adaptation technique which efficiently unifies correlation alignment and entropy minimization", "Improves the correlation alignment approach to domain adaptation by replacing the Euclidean distance with the geodesic Log-Euclidean distance between two covariance matices, and automatically selecting the balancing cost by the entropy on the target domain.", "Proposal for minimal-entropy correlation alignment, an unsupervised domain adaptation algorithm which links together entropy minimization and correlation alignment methods." ] }, { "id": "task668-bf9efc7c86e647fe9209fa754e7d73dc", "input": "First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks. Second-order methods, despite their better convergence rate, are rarely used in practice due to the pro- hibitive computational cost in calculating the second-order information. In this paper, we propose a novel Gram-Gauss-Newton (GGN) algorithm to train deep neural networks for regression problems with square loss. Our method draws inspiration from the connection between neural network optimization and kernel regression of neural tangent kernel (NTK). Different from typical second-order methods that have heavy computational cost in each iteration, GGN only has minor overhead compared to first-order methods such as SGD. We also give theoretical results to show that for sufficiently wide neural networks, the convergence rate of GGN is quadratic. Furthermore, we provide convergence guarantee for mini-batch GGN algorithm, which is, to our knowledge, the first convergence result for the mini-batch version of a second-order method on overparameterized neural net- works. Preliminary experiments on regression tasks demonstrate that for training standard networks, our GGN algorithm converges much faster and achieves better performance than SGD.", "output": [ "A novel Gram-Gauss-Newton method to train neural networks, inspired by neural tangent kernel and Gauss-Newton method, with fast convergence speed both theoretically and experimentally." ] }, { "id": "task668-aa1ef4c2391c423fbef716f3d41010ec", "input": "There are two major paradigms of white-box adversarial attacks that attempt to impose input perturbations. The first paradigm, called the fix-perturbation attack, crafts adversarial samples within a given perturbation level. The second paradigm, called the zero-confidence attack, finds the smallest perturbation needed to cause misclassification, also known as the margin of an input feature. While the former paradigm is well-resolved, the latter is not. Existing zero-confidence attacks either introduce significant approximation errors, or are too time-consuming. We therefore propose MarginAttack, a zero-confidence attack framework that is able to compute the margin with improved accuracy and efficiency. Our experiments show that MarginAttack is able to compute a smaller margin than the state-of-the-art zero-confidence attacks, and matches the state-of-the-art fix-perturbation attacks. In addition, it runs significantly faster than the Carlini-Wagner attack, currently the most accurate zero-confidence attack algorithm.", "output": [ "This paper introduces MarginAttack, a stronger and faster zero-confidence adversarial attack." ] }, { "id": "task668-4c2b9befac8b40e4ae6c0c029fe660aa", "input": "We present a method for evaluating the sensitivity of deep reinforcement learning (RL) policies. We also formulate a zero-sum dynamic game for designing robust deep reinforcement learning policies. Our approach mitigates the brittleness of policies when agents are trained in a simulated environment and are later exposed to the real world where it is hazardous to employ RL policies. This framework for training deep RL policies involve a zero-sum dynamic game against an adversarial agent, where the goal is to drive the system dynamics to a saddle region. Using a variant of the guided policy search algorithm, our agent learns to adopt robust policies that require less samples for learning the dynamics and performs better than the GPS algorithm. Without loss of generality, we demonstrate that deep RL policies trained in this fashion will be maximally robust to a ``worst\" possible adversarial disturbances.", "output": [ "This paper demonstrates how H-infinity control theory can help better design robust deep policies for robot motor taks", "Proposes to incorporate elements of robust control into guided policy research in order to devise a method that is resilient to perturbations and model mismatch.", "The paper presents a method for evaluating the sensitivity and robustness of deep RL policies, and proposes a dynamic game approach for learning robust policies." ] }, { "id": "task668-304c60f942fd468a9bf7983f54d4e0f4", "input": "The use of AR in an industrial context could help for the training of new operators. To be able to use an AR guidance system, we need a tool to quickly create a 3D representation of the assembly line and of its AR annotations. This tool should be very easy to use by an operator who is not an AR or VR specialist: typically the manager of the assembly line. This is why we proposed WAAT, a 3D authoring tool allowing user to quickly create 3D models of the workstations, and also test the AR guidance placement. WAAT makes on-site authoring possible, which should really help to have an accurate 3D representation of the assembly line. The verification of AR guidance should also be very useful to make sure everything is visible and doesn't interfere with technical tasks. In addition to these features, our future work will be directed in the deployment of WAAT into a real boiler assembly line to assess the usability of this solution.", "output": [ "This paper describe a 3D authoring tool for providing AR in assembly lines of industry 4.0", "The paper addresses how AR authoring tools support training of assembly line systems and proposes an approach", "An AR guidance system for industrial assembly lines that allows for on-site authoring of AR content.", "Presents a system that allows factory workers to be trained more efficiently using augmented reality system. " ] }, { "id": "task668-97f92953a935460cbb105128613ab731", "input": "The adversarial training procedure proposed by Madry et al. (2018) is one of the most effective methods to defend against adversarial examples in deep neural net- works (DNNs). In our paper, we shed some lights on the practicality and the hardness of adversarial training by showing that the effectiveness (robustness on test set) of adversarial training has a strong correlation with the distance between a test point and the manifold of training data embedded by the network. Test examples that are relatively far away from this manifold are more likely to be vulnerable to adversarial attacks. Consequentially, an adversarial training based defense is susceptible to a new class of attacks, the “blind-spot attack”, where the input images reside in “blind-spots” (low density regions) of the empirical distri- bution of training data but is still on the ground-truth data manifold. For MNIST, we found that these blind-spots can be easily found by simply scaling and shifting image pixel values. Most importantly, for large datasets with high dimensional and complex data manifold (CIFAR, ImageNet, etc), the existence of blind-spots in adversarial training makes defending on any valid test examples difficult due to the curse of dimensionality and the scarcity of training data. Additionally, we find that blind-spots also exist on provable defenses including (Kolter & Wong, 2018) and (Sinha et al., 2018) because these trainable robustness certificates can only be practically optimized on a limited set of training data.", "output": [ "We show that even the strongest adversarial training methods cannot defend against adversarial examples crafted on slightly scaled and shifted test images." ] }, { "id": "task668-8522155cea3e45f5b88e32606854773a", "input": "Human observers can learn to recognize new categories of objects from a handful of examples, yet doing so with machine perception remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable, as suggested by recent perceptual evidence. We therefore revisit and improve Contrastive Predictive Coding, a recently-proposed unsupervised learning framework, and arrive at a representation which enables generalization from small amounts of labeled data. When provided with only 1% of ImageNet labels (i.e. 13 per class), this model retains a strong classification performance, 73% Top-5 accuracy, outperforming supervised networks by 28% (a 65% relative improvement) and state-of-the-art semi-supervised methods by 14%. We also find this representation to serve as a useful substrate for object detection on the PASCAL-VOC 2007 dataset, approaching the performance of representations trained with a fully annotated ImageNet dataset.", "output": [ "Unsupervised representations learned with Contrastive Predictive Coding enable data-efficient image classification." ] }, { "id": "task668-3c7f8fb923af4956ac247db4f5d6d5bb", "input": "Convolutional neural networks (CNNs) have achieved state of the art performance on recognizing and representing audio, images, videos and 3D volumes; that is, domains where the input can be characterized by a regular graph structure. \n However, generalizing CNNs to irregular domains like 3D meshes is challenging. Additionally, training data for 3D meshes is often limited. In this work, we generalize convolutional autoencoders to mesh surfaces. We perform spectral decomposition of meshes and apply convolutions directly in frequency space. In addition, we use max pooling and introduce upsampling within the network to represent meshes in a low dimensional space. We construct a complex dataset of 20,466 high resolution meshes with extreme facial expressions and encode it using our Convolutional Mesh Autoencoder. Despite limited training data, our method outperforms state-of-the-art PCA models of faces with 50% lower error, while using 75% fewer parameters.", "output": [ "Convolutional autoencoders generalized to mesh surfaces for encoding and reconstructing extreme 3D facial expressions." ] }, { "id": "task668-508c9b12007e41589b90cac3f468c5a1", "input": "Reinforcement learning in environments with large state-action spaces is challenging, as exploration can be highly inefficient. Even if the dynamics are simple, the optimal policy can be combinatorially hard to discover. In this work, we propose a hierarchical approach to structured exploration to improve the sample efficiency of on-policy exploration in large state-action spaces. The key idea is to model a stochastic policy as a hierarchical latent variable model, which can learn low-dimensional structure in the state-action space, and to define exploration by sampling from the low-dimensional latent space. This approach enables lower sample complexity, while preserving policy expressivity. In order to make learning tractable, we derive a joint learning and exploration strategy by combining hierarchical variational inference with actor-critic learning. The benefits of our learning approach are that 1) it is principled, 2) simple to implement, 3) easily scalable to settings with many actions and 4) easily composable with existing deep learning approaches. We demonstrate the effectiveness of our approach on learning a deep centralized multi-agent policy, as multi-agent environments naturally have an exponentially large state-action space. In this setting, the latent hierarchy implements a form of multi-agent coordination during exploration and execution (MACE). We demonstrate empirically that MACE can more efficiently learn optimal policies in challenging multi-agent games with a large number (~20) of agents, compared to conventional baselines. Moreover, we show that our hierarchical structure leads to meaningful agent coordination.", "output": [ "Make deep reinforcement learning in large state-action spaces more efficient using structured exploration with deep hierarchical policies.", "A method to coordinate agent behaviour by using policies that have shared latent structure, a variational policy optimization method to optimize the coordinated policies, and a derivation of the authors' variational, hierarchical update.", "This paper suggests an algorithmic innovation consisting of hierarchical latent variables for coordinated exploration in multi-agent settings" ] }, { "id": "task668-f352f801c89a4e9c8e4647454371a369", "input": "Characterization of the representations learned in intermediate layers of deep networks can provide valuable insight into the nature of a task and can guide the development of well-tailored learning strategies. Here we study convolutional neural network-based acoustic models in the context of automatic speech recognition. Adapting a method proposed by Yosinski et al. [2014], we measure the transferability of each layer between German and English to assess the their language-specifity. We observe three distinct regions of transferability: (1) the first two layers are entirely transferable between languages, (2) layers 2–8 are also highly transferable but we find evidence of some language specificity, (3) the subsequent fully connected layers are more language specific but can be successfully finetuned to the target language. To further probe the effect of weight freezing, we performed follow-up experiments using freeze-training [Raghu et al., 2017]. Our results are consistent with the observation that CCNs converge 'bottom up' during training and demonstrate the benefit of freeze training, especially for transfer learning.", "output": [ "All but the first two layers of our CNNs based acoustic models demonstrated some degree of language-specificity but freeze training enabled successful transfer between languages.", "The paper measures the transferability of features for each layer in CNN-based acoustic models across languages, concluding that AMs trained with 'the freeze training' technique outperformed other transferred models." ] }, { "id": "task668-00048f68f4334aa487831fd5709a5033", "input": "Image translation between two domains is a class of problems aiming to learn mapping from an input image in the source domain to an output image in the target domain. It has been applied to numerous applications, such as data augmentation, domain adaptation, and unsupervised training. When paired training data is not accessible, image translation becomes an ill-posed problem. We constrain the problem with the assumption that the translated image needs to be perceptually similar to the original image and also appears to be drawn from the new domain, and propose a simple yet effective image translation model consisting of a single generator trained with a self-regularization term and an adversarial term. We further notice that existing image translation techniques are agnostic to the subjects of interest and often introduce unwanted changes or artifacts to the input. Thus we propose to add an attention module to predict an attention map to guide the image translation process. The module learns to attend to key parts of the image while keeping everything else unaltered, essentially avoiding undesired artifacts or changes. The predicted attention map also opens door to applications such as unsupervised segmentation and saliency detection. Extensive experiments and evaluations show that our model while being simpler, achieves significantly better performance than existing image translation methods.", "output": [ "We propose a simple generative model for unsupervised image translation and saliency detection." ] }, { "id": "task668-51b3cb88bf0a4718a89059837e9232ad", "input": "Despite promising progress on unimodal data imputation (e.g. image inpainting), models for multimodal data imputation are far from satisfactory. In this work, we propose variational selective autoencoder (VSAE) for this task. Learning only from partially-observed data, VSAE can model the joint distribution of observed/unobserved modalities and the imputation mask, resulting in a unified model for various down-stream tasks including data generation and imputation. Evaluation on synthetic high-dimensional and challenging low-dimensional multimodal datasets shows significant improvement over state-of-the-art imputation models.", "output": [ "We propose a novel VAE-based framework learning from partially-observed data for imputation and generation. " ] }, { "id": "task668-0e37d583ae6a4d34aa09e5d218c10557", "input": "Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unlabeled) data. Convergence problems during training are overcome by Wasserstein GANs which minimize the distance between the model and the empirical distribution in terms of a different metric, but thereby introduce a Lipschitz constraint into the optimization problem. A simple way to enforce the Lipschitz constraint on the class of functions, which can be modeled by the neural network, is weight clipping. Augmenting the loss by a regularization term that penalizes the deviation of the gradient norm of the critic (as a function of the network's input) from one, was proposed as an alternative that improves training. We present theoretical arguments why using a weaker regularization term enforcing the Lipschitz constraint is preferable. These arguments are supported by experimental results on several data sets.", "output": [ "A new regularization term can improve your training of wasserstein gans", "The paper proposes a regularization scheme for Wasserstein GAN based on relaxation of the constraints on the Lipschitz constant of 1.", "The article deals with regularization/penalization in the fitting of GANs, when based on a L_1 Wasserstein metric." ] }, { "id": "task668-37a4540d25824f8990d5cb253c185c9e", "input": "Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models for learning prevalent patterns in natural language. Yet language generated by RNNs often shows several degenerate characteristics that are uncommon in human language; while fluent, RNN language production can be overly generic, repetitive, and even self-contradictory. We postulate that the objective function optimized by RNN language models, which amounts to the overall perplexity of a text, is not expressive enough to capture the abstract qualities of good generation such as Grice’s Maxims. In this paper, we introduce a general learning framework that can construct a decoding objective better suited for generation. Starting with a generatively trained RNN language model, our framework learns to construct a substantially stronger generator by combining several discriminatively trained models that can collectively address the limitations of RNN generation. Human evaluation demonstrates that text generated by the resulting generator is preferred over that of baselines by a large margin and significantly enhances the overall coherence, style, and information content of the generated text.", "output": [ "We build a stronger natural language generator by discriminatively training scoring functions that rank candidate generations with respect to various qualities of good writing.", "This paper proposes to bring together multiple inductive biases that hope to correct for inconsistencies in sequence decoding and proposes to optimize for the parameters of a pre-defined combination of various sub-objectives. ", "This paper combines RNN language model with several discriminatively trained models to improve the language generation.", "This paper proposes to improve RNN language model generation using augmented objectives inspired by Grice's maxims of communication." ] }, { "id": "task668-92b5262308874ca787359dda9dfd072b", "input": "Unsupervised bilingual dictionary induction (UBDI) is useful for unsupervised machine translation and for cross-lingual transfer of models into low-resource languages. One approach to UBDI is to align word vector spaces in different languages using Generative adversarial networks (GANs) with linear generators, achieving state-of-the-art performance for several language pairs. For some pairs, however, GAN-based induction is unstable or completely fails to align the vector spaces. We focus on cases where linear transformations provably exist, but the performance of GAN-based UBDI depends heavily on the model initialization. We show that the instability depends on the shape and density of the vector sets, but not on noise; it is the result of local optima, but neither over-parameterization nor changing the batch size or the learning rate consistently reduces instability. Nevertheless, we can stabilize GAN-based UBDI through best-of-N model selection, based on an unsupervised stopping criterion.", "output": [ "An empirical investigation of GAN-based alignment of word vector spaces, focusing on cases, where linear transformations provably exist, but training is unstable." ] }, { "id": "task668-60b98d653d824780be2adedaccf611fa", "input": "In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize.", "output": [ "We propose ImageNet-C to measure classifier corruption robustness and ImageNet-P to measure perturbation robustness" ] }, { "id": "task668-16c4ea3b9e3840d6bc68e179fc3f0ed4", "input": "Training generative models like Generative Adversarial Network (GAN) is challenging for noisy data. A novel curriculum learning algorithm pertaining to clustering is proposed to address this issue in this paper. The curriculum construction is based on the centrality of underlying clusters in data points. The data points of high centrality takes priority of being fed into generative models during training. To make our algorithm scalable to large-scale data, the active set is devised, in the sense that every round of training proceeds only on an active subset containing a small fraction of already trained data and the incremental data of lower centrality. Moreover, the geometric analysis is presented to interpret the necessity of cluster curriculum for generative models. The experiments on cat and human-face data validate that our algorithm is able to learn the optimal generative models (e.g. ProGAN) with respect to specified quality metrics for noisy data. An interesting finding is that the optimal cluster curriculum is closely related to the critical point of the geometric percolation process formulated in the paper.", "output": [ "A novel cluster-based algorithm of curriculum learning is proposed to solve the robust training of generative models." ] }, { "id": "task668-284c2841404548e5a4b77535618f15b9", "input": "Deep neural networks, in particular convolutional neural networks, have become highly effective tools for compressing images and solving inverse problems including denoising, inpainting, and reconstruction from few and noisy measurements. This success can be attributed in part to their ability to represent and generate natural images well. Contrary to classical tools such as wavelets, image-generating deep neural networks have a large number of parameters---typically a multiple of their output dimension---and need to be trained on large datasets. \n In this paper, we propose an untrained simple image model, called the deep decoder, which is a deep neural network that can generate natural images from very few weight parameters.\n The deep decoder has a simple architecture with no convolutions and fewer weight parameters than the output dimensionality. This underparameterization enables the deep decoder to compress images into a concise set of network weights, which we show is on par with wavelet-based thresholding. Further, underparameterization provides a barrier to overfitting, allowing the deep decoder to have state-of-the-art performance for denoising. The deep decoder is simple in the sense that each layer has an identical structure that consists of only one upsampling unit, pixel-wise linear combination of channels, ReLU activation, and channelwise normalization. This simplicity makes the network amenable to theoretical analysis, and it sheds light on the aspects of neural networks that enable them to form effective signal representations.", "output": [ "We introduce an underparameterized, nonconvolutional, and simple deep neural network that can, without training, effectively represent natural images and solve image processing tasks like compression and denoising competitively." ] }, { "id": "task668-590f8d5805ff4cb3aff66d4afdd7058c", "input": "We present a novel method to precisely impose tree-structured category information onto word-embeddings, resulting in ball embeddings in higher dimensional spaces (N-balls for short). Inclusion relations among N-balls implicitly encode subordinate relations among categories. The similarity measurement in terms of the cosine function is enriched by category information. Using a geometric construction method instead of back-propagation, we create large N-ball embeddings that satisfy two conditions: (1) category trees are precisely imposed onto word embeddings at zero energy cost; (2) pre-trained word embeddings are well preserved. A new benchmark data set is created for validating the category of unknown words. Experiments show that N-ball embeddings, carrying category information, significantly outperform word embeddings in the test of nearest neighborhoods, and demonstrate surprisingly good performance in validating categories of unknown words. Source codes and data-sets are free for public access \\url{https://github.com/gnodisnait/nball4tree.git} and \\url{https://github.com/gnodisnait/bp94nball.git}.", "output": [ "we show a geometric method to perfectly encode categroy tree information into pre-trained word-embeddings.", "The paper proposes N-ball embedding for taxonomic data where an N-ball is a pair of a centroid vector and the radius from the center.", "The paper presents a method for tweaking existing vector embeddings of categorical objects (such as words), to convert them to ball embeddings that follow hierarchies.", "Focuses on adjusting the pretrained word embeddings so that they respect the hypernymy/hyponymy relationship by appropriate n-ball encapsulation." ] }, { "id": "task668-dfff79ad80ad48078f25b9ad351f50a9", "input": "Survival Analysis (time-to-event analysis) in the presence of multiple possible adverse events, i.e., competing risks, is a challenging, yet very important problem in medicine, finance, manufacturing, etc. Extending classical survival analysis to competing risks is not trivial since only one event (e.g. one cause of death) is observed and hence, the incidence of an event of interest is often obscured by other related competing events. This leads to the nonidentifiability of the event times’ distribution parameters, which makes the problem significantly more challenging. In this work we introduce Siamese Survival Prognosis Network, a novel Siamese Deep Neural Network architecture that is able to effectively learn from data in the presence of multiple adverse events. The Siamese Survival Network is especially crafted to issue pairwise concordant time-dependent risks, in which longer event times are assigned lower risks. Furthermore, our architecture is able to directly optimize an approximation to the C-discrimination index, rather than relying on well-known metrics of cross-entropy etc., and which are not able to capture the unique requirements of survival analysis with competing risks. Our results show consistent performance improvements on a number of publicly available medical datasets over both statistical and deep learning state-of-the-art methods.", "output": [ "In this work we introduce a novel Siamese Deep Neural Network architecture that is able to effectively learn from data in the presence of multiple adverse events.", "This paper introduces siamese neural networks to the competing risks framework by optimizing for the c-index directly", "The authors address issues of estimating risk in a survival analysis setting with competing risks and propose directly optimizing the time-dependent discrimination index using a siamese survival network" ] }, { "id": "task668-6a60b6b1577a413eb35addc7a07149ac", "input": "We propose an end-to-end framework for training domain specific models (DSMs) to obtain both high accuracy and computational efficiency for object detection tasks. DSMs are trained with distillation and focus on achieving high accuracy at a limited domain (e.g. fixed view of an intersection). We argue that DSMs can capture essential features well even with a small model size, enabling higher accuracy and efficiency than traditional techniques. In addition, we improve the training efficiency by reducing the dataset size by culling easy to classify images from the training set. For the limited domain, we observed that compact DSMs significantly surpass the accuracy of COCO trained models of the same size. By training on a compact dataset, we show that with an accuracy drop of only 3.6%, the training time can be reduced by 93%.", "output": [ "High object-detection accuracy can be obtained by training domain specific compact models and the training can be very short." ] }, { "id": "task668-5e863a3eb6a44ed6a0aaca68fdc94f21", "input": "Neural network quantization has become an important research area due to its great impact on deployment of large models on resource constrained devices. In order to train networks that can be effectively discretized without loss of performance, we introduce a differentiable quantization procedure. Differentiability can be achieved by transforming continuous distributions over the weights and activations of the network to categorical distributions over the quantization grid. These are subsequently relaxed to continuous surrogates that can allow for efficient gradient-based optimization. We further show that stochastic rounding can be seen as a special case of the proposed approach and that under this formulation the quantization grid itself can also be optimized with gradient descent. We experimentally validate the performance of our method on MNIST, CIFAR 10 and Imagenet classification.", "output": [ "We introduce a technique that allows for gradient based training of quantized neural networks.", "Proposes a unified and general way of training neural networks with reduced precision quantized synaptic weights and activations.", "A new approach to quantizing activations which is state of the art or competitive on several real image problems.", "A method for learning neural networks with quantized weights and activations by stochastically quantizing values and replacing the resulting categotical distribution with a continuous relaxation" ] }, { "id": "task668-dd51bf6158dc411685b61c6e52ad2764", "input": "Modern applications from Autonomous Vehicles to Video Surveillance generate massive amounts of image data. In this work we propose a novel image outlier detection approach (IOD for short) that leverages the cutting-edge image classifier to discover outliers without using any labeled outlier. We observe that although intuitively the confidence that a convolutional neural network (CNN) has that an image belongs to a particular class could serve as outlierness measure to each image, directly applying this confidence to detect outlier does not work well. This is because CNN often has high confidence on an outlier image that does not belong to any target class due to its generalization ability that ensures the high accuracy in classification. To solve this issue, we propose a Deep Neural Forest-based approach that harmonizes the contradictory requirements of accurately classifying images and correctly detecting the outlier images. Our experiments using several benchmark image datasets including MNIST, CIFAR-10, CIFAR-100, and SVHN demonstrate the effectiveness of our IOD approach for outlier detection, capturing more than 90% of outliers generated by injecting one image dataset into another, while still preserving the classification accuracy of the multi-class classification problem.", "output": [ "A novel approach that detects outliers from image data, while preserving the classification accuracy of image classification" ] }, { "id": "task668-24fe7f4f518844c1bf940f46b3287c76", "input": "Network pruning is widely used for reducing the heavy computational cost of deep models. A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model), pruning and fine-tuning. In this work, we make a rather surprising observation: fine-tuning a pruned model only gives comparable or even worse performance than training that model with randomly initialized weights. Our results have several implications: 1) training a large, over-parameterized model is not necessary to obtain an efficient final model, 2) learned \"important\" weights of the large model are not necessarily useful for the small pruned model, 3) the pruned architecture itself, rather than a set of inherited weights, is what leads to the efficiency benefit in the final model, which suggests that some pruning algorithms could be seen as performing network architecture search.", "output": [ "In network pruning, fine-tuning a pruned model only gives comparable or worse performance than training it from scratch. This advocate a rethinking of existing pruning algorithms." ] }, { "id": "task668-2708d29b3a994163891c81170de4c6d4", "input": "In this paper, we propose Continuous Graph Flow, a generative continuous flow based method that aims to model complex distributions of graph-structured data. Once learned, the model can be applied to an arbitrary graph, defining a probability density over the random variables represented by the graph. It is formulated as an ordinary differential equation system with shared and reusable functions that operate over the graphs. This leads to a new type of neural graph message passing scheme that performs continuous message passing over time. This class of models offers several advantages: a flexible representation that can generalize to variable data dimensions; ability to model dependencies in complex data distributions; reversible and memory-efficient; and exact and efficient computation of the likelihood of the data. We demonstrate the effectiveness of our model on a diverse set of generation tasks across different domains: graph generation, image puzzle generation, and layout generation from scene graphs. Our proposed model achieves significantly better performance compared to state-of-the-art models.", "output": [ "Graph generative models based on generalization of message passing to continuous time using ordinary differential equations " ] }, { "id": "task668-0da6c561d7a74f70af939441698e38ca", "input": "Point clouds are an important type of geometric data and have widespread use in computer graphics and vision. However, learning representations for point clouds is particularly challenging due to their nature as being an unordered collection of points irregularly distributed in 3D space. Graph convolution, a generalization of the convolution operation for data defined over graphs, has been recently shown to be very successful at extracting localized features from point clouds in supervised or semi-supervised tasks such as classification or segmentation. This paper studies the unsupervised problem of a generative model exploiting graph convolution. We focus on the generator of a GAN and define methods for graph convolution when the graph is not known in advance as it is the very output of the generator. The proposed architecture learns to generate localized features that approximate graph embeddings of the output geometry. We also study the problem of defining an upsampling layer in the graph-convolutional generator, such that it learns to exploit a self-similarity prior on the data distribution to sample more effectively.", "output": [ "A GAN using graph convolution operations with dynamically computed graphs from hidden features", "The paper proposes a version of GANs specifically designed for generating point clouds with the core contribution of the work the upsampling operation.", "This paper proposes graph-convolutional GANs for irregular 3D point clouds that learn domain and features at the same time." ] }, { "id": "task668-46e1f78de2e54ddb88a222b8c0580d67", "input": "With innovations in architecture design, deeper and wider neural network models deliver improved performance on a diverse variety of tasks. But the increased memory footprint of these models presents a challenge during training, when all intermediate layer activations need to be stored for back-propagation. Limited GPU memory forces practitioners to make sub-optimal choices: either train inefficiently with smaller batches of examples; or limit the architecture to have lower depth and width, and fewer layers at higher spatial resolutions. This work introduces an approximation strategy that significantly reduces a network's memory footprint during training, but has negligible effect on training performance and computational expense. During the forward pass, we replace activations with lower-precision approximations immediately after they have been used by subsequent layers, thus freeing up memory. The approximate activations are then used during the backward pass. This approach limits the accumulation of errors across the forward and backward pass---because the forward computation across the network still happens at full precision, and the approximation has a limited effect when computing gradients to a layer's input. Experiments, on CIFAR and ImageNet, show that using our approach with 8- and even 4-bit fixed-point approximations of 32-bit floating-point activations has only a minor effect on training and validation performance, while affording significant savings in memory usage.", "output": [ "An algorithm to reduce the amount of memory required for training deep networks, based on an approximation strategy." ] }, { "id": "task668-3f040e940dd74cf1bf22845b272a7bee", "input": "We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programming. By considering the structure of the counterfactual query, one can significantly optimise the inference process. We also consider design choices to enable further optimisations. We introduce MultiVerse, a probabilistic programming prototype engine for approximate causal reasoning. We provide experimental results and compare with Pyro, an existing probabilistic programming framework with some of causal reasoning tools.", "output": [ "Probabilistic Programming that Natively Supports Causal, Counterfactual Inference" ] }, { "id": "task668-788e9c1811ea42148b9050e0f7fc7452", "input": "While Bayesian optimization (BO) has achieved great success in optimizing expensive-to-evaluate black-box functions, especially tuning hyperparameters of neural networks, methods such as random search (Li et al., 2016) and multi-fidelity BO (e.g. Klein et al. (2017)) that exploit cheap approximations, e.g. training on a smaller training data or with fewer iterations, can outperform standard BO approaches that use only full-fidelity observations. In this paper, we propose a novel Bayesian optimization algorithm, the continuous-fidelity knowledge gradient (cfKG) method, that can be used when fidelity is controlled by one or more continuous settings such as training data size and the number of training iterations. cfKG characterizes the value of the information gained by sampling a point at a given fidelity, choosing to sample at the point and fidelity with the largest value per unit cost. Furthermore, cfKG can be generalized, following Wu et al. (2017), to settings where derivatives are available in the optimization process, e.g. large-scale kernel learning, and where more than one point can be evaluated simultaneously. Numerical experiments show that cfKG outperforms state-of-art algorithms when optimizing synthetic functions, tuning convolutional neural networks (CNNs) on CIFAR-10 and SVHN, and in large-scale kernel learning.", "output": [ "We propose a Bayes-optimal Bayesian optimization algorithm for hyperparameter tuning by exploiting cheap approximations.", "Studies hyperparameter-optimization by Bayesian optimization, using the Knowledge Gradient framework and allowing the Bayesian optimizer to tune fidelity against cost." ] }, { "id": "task668-dfe255bb7ac041f1922b7d9025a9eddb", "input": "Reinforcement learning (RL) is a powerful framework for solving problems by exploring and learning from mistakes. However, in the context of autonomous vehicle (AV) control, requiring an agent to make mistakes, or even allowing mistakes, can be quite dangerous and costly in the real world. For this reason, AV RL is generally only viable in simulation. Because these simulations have imperfect representations, particularly with respect to graphics, physics, and human interaction, we find motivation for a framework similar to RL, suitable to the real world. To this end, we formulate a learning framework that learns from restricted exploration by having a human demonstrator do the exploration. Existing work on learning from demonstration typically either assumes the collected data is performed by an optimal expert, or requires potentially dangerous exploration to find the optimal policy. We propose an alternative framework that learns continuous control from only safe behavior. One of our key insights is that the problem becomes tractable if the feedback score that rates the demonstration applies to the atomic action, as opposed to the entire sequence of actions. We use human experts to collect driving data as well as to label the driving data through a framework we call ``Backseat Driver'', giving us state-action pairs matched with scalar values representing the score for the action. We call the more general learning framework ReNeg, since it learns a regression from states to actions given negative as well as positive examples. We empirically validate several models in the ReNeg framework, testing on lane-following with limited data. We find that the best solution in this context outperforms behavioral cloning has strong connections to stochastic policy gradient approaches.", "output": [ "We introduce a novel framework for learning from demonstration that uses continuous human feedback; we evaluate this framework on continuous control for autonomous vehicles." ] }, { "id": "task668-908278a4050347c1a24a06758518481a", "input": "Deep learning has shown that learned functions can dramatically outperform hand-designed functions on perceptual tasks. Analogously, this suggests that learned update functions may similarly outperform current hand-designed optimizers, especially for specific tasks. However, learned optimizers are notoriously difficult to train and have yet to demonstrate wall-clock speedups over hand-designed optimizers, and thus are rarely used in practice. Typically, learned optimizers are trained by truncated backpropagation through an unrolled optimization process. The resulting gradients are either strongly biased (for short truncations) or have exploding norm (for long truncations). In this work we propose a training scheme which overcomes both of these difficulties, by dynamically weighting two unbiased gradient estimators for a variational loss on optimizer performance. This allows us to train neural networks to perform optimization faster than well tuned first-order methods. Moreover, by training the optimizer against validation loss, as opposed to training loss, we are able to use it to train models which generalize better than those trained by first order methods. We demonstrate these results on problems where our learned optimizer trains convolutional networks in a fifth of the wall-clock time compared to tuned first-order methods, and with an improvement", "output": [ "We analyze problems when training learned optimizers, address those problems via variational optimization using two complementary gradient estimators, and train optimizers that are 5x faster in wall-clock time than baseline optimizers (e.g. Adam).", "This paper uses un-rolled optimization to learn neural networks for optimization.", "This paper tackles the problem of learning an optimizer, specifically the authors focus on obtaining cleaner gradients from the unrolled training procedure.", "Presents a method for \"learning an optimizer\" by using a Variational Optimization for the \"outer\" optimizer loss and proposes the idea to combine both the reparametrized gradient and the score-function estimator for the Variational Objective and weights them using a product of Gaussians formula for the mean." ] }, { "id": "task668-40ce557075cc4efd8e8f31e4562dfd78", "input": "The knowledge regarding the function of proteins is necessary as it gives a clear picture of biological processes. Nevertheless, there are many protein sequences found and added to the databases but lacks functional annotation. The laboratory experiments take a considerable amount of time for annotation of the sequences. This arises the need to use computational techniques to classify proteins based on their functions. In our work, we have collected the data from Swiss-Prot containing 40433 proteins which is grouped into 30 families. We pass it to recurrent neural network(RNN), long short term memory(LSTM) and gated recurrent unit(GRU) model and compare it by applying trigram with deep neural network and shallow neural network on the same dataset. Through this approach, we could achieve maximum of around 78% accuracy for the classification of protein families. \n", "output": [ "Proteins, amino-acid sequences, machine learning, deep learning, recurrent neural network(RNN), long short term memory(LSTM), gated recurrent unit(GRU), deep neural networks" ] }, { "id": "task668-7c89668dfcd042ea8def10c8499c9b89", "input": "Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs). Because these networks are optimized for object recognition, they learn where to attend using only a weak form of supervision derived from image class labels. Here, we demonstrate the benefit of using stronger supervisory signals by teaching DCNs to attend to image regions that humans deem important for object recognition. We first describe a large-scale online experiment (ClickMe) used to supplement ImageNet with nearly half a million human-derived \"top-down\" attention maps. Using human psychophysics, we confirm that the identified top-down features from ClickMe are more diagnostic than \"bottom-up\" saliency features for rapid image categorization. As a proof of concept, we extend a state-of-the-art attention network and demonstrate that adding ClickMe supervision significantly improves its accuracy and yields visual features that are more interpretable and more similar to those used by human observers.", "output": [ "A large-scale dataset for training attention models for object recognition leads to more accurate, interpretable, and human-like object recognition.", "Argues recent gains in visual recognition stem from using visual attention mechanisms in deep convolutional networks, which learn where to focus through a weak form of supervision based on image class labels.", "Presents a new take on attention in which a large attention dataset is collected and used to train a NN in a supervised manner to exploit self-reported human attention.", "This paper proposes a new approach to use more informative signals, specifically, regions humans deem important on images, to improve deep convolutional neural networks." ] }, { "id": "task668-3e7f1940370142e3914441c5635c3a61", "input": "The attention layer in a neural network model provides insights into the model’s reasoning behind its prediction, which are usually criticized for being opaque. Recently, seemingly contradictory viewpoints have emerged about the interpretability of attention weights (Jain & Wallace, 2019; Vig & Belinkov, 2019). Amid such confusion arises the need to understand attention mechanism more systematically. In this work, we attempt to fill this gap by giving a comprehensive explanation which justifies both kinds of observations (i.e., when is attention interpretable and when it is not). Through a series of experiments on diverse NLP tasks, we validate our observations and reinforce our claim of interpretability of attention through manual evaluation.", "output": [ "Analysis of attention mechanism across diverse NLP tasks." ] }, { "id": "task668-fd7cc53a41a34b5d9d9fc707ebadff55", "input": "In this paper, we propose a neural network framework called neuron hierarchical network (NHN), that evolves beyond the hierarchy in layers, and concentrates on the hierarchy of neurons. We observe mass redundancy in the weights of both handcrafted and randomly searched architectures. Inspired by the development of human brains, we prune low-sensitivity neurons in the model and add new neurons to the graph, and the relation between individual neurons are emphasized and the existence of layers weakened. We propose a process to discover the best base model by random architecture search, and discover the best locations and connections of the added neurons by evolutionary search. Experiment results show that the NHN achieves higher test accuracy on Cifar-10 than state-of-the-art handcrafted and randomly searched architectures, while requiring much fewer parameters and less searching time.", "output": [ "By breaking the layer hierarchy, we propose a 3-step approach to the construction of neuron-hierarchy networks that outperform NAS, SMASH and hierarchical representation with fewer parameters and shorter searching time." ] }, { "id": "task668-6c5b1b42ae6946ebb9c8d4cf82f00795", "input": "Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables. However, the inability to effectively visualize these relationships has led to DNNs being characterized as black boxes and consequently limited their applications. To ameliorate this problem, we introduce the use of hierarchical interpretations to explain DNN predictions through our proposed method: agglomerative contextual decomposition (ACD). Given a prediction from a trained DNN, ACD produces a hierarchical clustering of the input features, along with the contribution of each cluster to the final prediction. This hierarchy is optimized to identify clusters of features that the DNN learned are predictive. We introduce ACD using examples from Stanford Sentiment Treebank and ImageNet, in order to diagnose incorrect predictions, identify dataset bias, and extract polarizing phrases of varying lengths. Through human experiments, we demonstrate that ACD enables users both to identify the more accurate of two DNNs and to better trust a DNN's outputs. We also find that ACD's hierarchy is largely robust to adversarial perturbations, implying that it captures fundamental aspects of the input and ignores spurious noise.", "output": [ "We introduce and validate hierarchical local interpretations, the first technique to automatically search for and display important interactions for individual predictions made by LSTMs and CNNs.", "A novel approach to explain neural network predictions by learning hierarchical representations of groups of input features and their contribution to the final prediction", "Extends an existing feature interpretation method for LSTMs to more generic DNNs and introduces a hierarchical clustering of the input features and the contributions of each cluster to the final prediction.", "This paper proposes a hierarchical extension of contextual decomposition." ] }, { "id": "task668-40313e353322425ba0917ea08ab3149c", "input": "Learning effective text representations is a key foundation for numerous machine learning and NLP applications. While the celebrated Word2Vec technique yields semantically rich word representations, it is less clear whether sentence or document representations should be built upon word representations or from scratch. Recent work has demonstrated that a distance measure between documents called \\emph{Word Mover's Distance} (WMD) that aligns semantically similar words, yields unprecedented KNN classification accuracy. However, WMD is very expensive to compute, and is harder to apply beyond simple KNN than feature embeddings. In this paper, we propose the \\emph{Word Mover's Embedding } (WME), a novel approach to building an unsupervised document (sentence) embedding from pre-trained word embeddings. Our technique extends the theory of \\emph{Random Features} to show convergence of the inner product between WMEs to a positive-definite kernel that can be interpreted as a soft version of (inverse) WMD. The proposed embedding is more efficient and flexible than WMD in many situations. As an example, WME with a simple linear classifier reduces the computational cost of WMD-based KNN \\emph{from cubic to linear} in document length and \\emph{from quadratic to linear} in number of samples, while simultaneously improving accuracy. In experiments on 9 benchmark text classification datasets and 22 textual similarity tasks the proposed technique consistently matches or outperforms state-of-the-art techniques, with significantly higher accuracy on problems of short length.", "output": [ "A novel approach to building an unsupervised document (sentence) embeddings from pre-trainedword embeddings" ] }, { "id": "task668-2bb976583a424142b351277fa5773a76", "input": "In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters. Optimal transport metrics, such as the Wasserstein distance, allow incorporating semantic side information such as word embeddings. Using W. barycenters to find the consensus between models allows us to balance confidence and semantics in finding the agreement between the models. We show applications of Wasserstein ensembling in attribute-based classification, multilabel learning and image captioning generation. These results show that the W. ensembling is a viable alternative to the basic geometric or arithmetic mean ensembling.", "output": [ "we propose to use Wasserstein barycenters for semantic model ensembling" ] }, { "id": "task668-d618461eb9b64228b30253d9f374e7a0", "input": "In this work, we propose a novel formulation of planning which views it as a probabilistic inference problem over future optimal trajectories. This enables us to use sampling methods, and thus, tackle planning in continuous domains using a fixed computational budget. We design a new algorithm, Sequential Monte Carlo Planning, by leveraging classical methods in Sequential Monte Carlo and Bayesian smoothing in the context of control as inference. Furthermore, we show that Sequential Monte Carlo Planning can capture multimodal policies and can quickly learn continuous control tasks.", "output": [ "Leveraging control as inference and Sequential Monte Carlo methods, we proposed a probabilistic planning algorithm." ] }, { "id": "task668-c6b9e82c286e40068ce8efc7256eca15", "input": "Generative models forsource code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs. We present a novel model for this problem that uses a graph to represent the intermediate state of the generated output. Our model generates code by interleaving grammar-driven expansion steps with graph augmentation and neural message passing steps. An experimental evaluation shows that our new model can generate semantically meaningful expressions, outperforming a range of strong baselines.", "output": [ "Representing programs as graphs including semantics helps when generating programs" ] }, { "id": "task668-84ed7509754e43fb8293ddb310c57134", "input": "Transferring representations from large-scale supervised tasks to downstream tasks have shown outstanding results in Machine Learning in both Computer Vision and natural language processing (NLP). One particular example can be sequence-to-sequence models for Machine Translation (Neural Machine Translation - NMT). It is because, once trained in a multilingual setup, NMT systems can translate between multiple languages and are also capable of performing zero-shot translation between unseen source-target pairs at test time. In this paper, we first investigate if we can extend the zero-shot transfer capability of multilingual NMT systems to cross-lingual NLP tasks (tasks other than MT, e.g. sentiment classification and natural language inference). We demonstrate a simple framework by reusing the encoder from a multilingual NMT system, a multilingual Encoder-Classifier, achieves remarkable zero-shot cross-lingual classification performance, almost out-of-the-box on three downstream benchmark tasks - Amazon Reviews, Stanford sentiment treebank (SST) and Stanford natural language inference (SNLI). In order to understand the underlying factors contributing to this finding, we conducted a series of analyses on the effect of the shared vocabulary, the training data type for NMT models, classifier complexity, encoder representation power, and model generalization on zero-shot performance. Our results provide strong evidence that the representations learned from multilingual NMT systems are widely applicable across languages and tasks, and the high, out-of-the-box classification performance is correlated with the generalization capability of such systems.", "output": [ "Zero-shot cross-lingual transfer by using multilingual neural machine translation " ] }, { "id": "task668-d6ef860791234d2fbf8fcd720a007751", "input": "Machine learning workloads are often expensive to train, taking weeks to converge. The current generation of frameworks relies on custom back-ends in order to achieve efficiency, making it impractical to train models on less common hardware where no such back-ends exist. Knossos builds on recent work that avoids the need for hand-written libraries, instead compiles machine learning models in much the same way one would compile other kinds of software. In order to make the resulting code efficient, the Knossos complier directly optimises the abstract syntax tree of the program. However in contrast to traditional compilers that employ hand-written optimisation passes, we take a rewriting approach driven by the $A^\\star$ search algorithm and a learn value function that evaluates future potential cost reduction of taking various rewriting actions to the program. We show that Knossos can automatically learned optimisations that past compliers had to implement by hand. Furthermore, we demonstrate that Knossos can achieve wall time reduction compared to a hand-tuned compiler on a suite of machine learning programs, including basic linear algebra and convolutional networks. The Knossos compiler has minimal dependencies and can be used on any architecture that supports a \\Cpp toolchain. \n Since cost model the proposed algorithm optimises can be tailored to a particular hardware architecture, the proposed approach can potentially applied to a variety of hardware.", "output": [ "We combine A* search with reinforcement learning to speed up machine learning code" ] }, { "id": "task668-57f698ed86d14a40919319f952433496", "input": "In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our method explicitly models the phrase structures in output sequences using Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence modeling method. To mitigate the monotonic alignment requirement of SWAN, we introduce a new layer to perform (soft) local reordering of input sequences. Different from existing neural machine translation (NMT) approaches, NPMT does not use attention-based decoding mechanisms. Instead, it directly outputs phrases in a sequential order and can decode in linear time. Our experiments show that NPMT achieves superior performances on IWSLT 2014 German-English/English-German and IWSLT 2015 English-Vietnamese machine translation tasks compared with strong NMT baselines. We also observe that our method produces meaningful phrases in output languages.", "output": [ "Neural phrase-based machine translation with linear decoding time" ] }, { "id": "task668-b3bf63a43df8474cbcf787d95b91b3ed", "input": "We consider reinforcement learning and bandit structured prediction problems with very sparse loss feedback: only at the end of an episode. We introduce a novel algorithm, RESIDUAL LOSS PREDICTION (RESLOPE), that solves such problems by automatically learning an internal representation of a denser reward function. RESLOPE operates as a reduction to contextual bandits, using its learned loss representation to solve the credit assignment problem, and a contextual bandit oracle to trade-off exploration and exploitation. RESLOPE enjoys a no-regret reduction-style theoretical guarantee and outperforms state of the art reinforcement learning algorithms in both MDP environments and bandit structured prediction settings.", "output": [ "We present a novel algorithm for solving reinforcement learning and bandit structured prediction problems with very sparse loss feedback." ] }, { "id": "task668-5537a7640328461c9341d334f05b80cd", "input": "One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds of Transcription Factors (TFs) as labels, genomic-sequence based TFBS prediction is a challenging multi-label classification task. There are two major biological mechanisms for TF binding: (1) sequence-specific binding patterns on genomes known as “motifs” and (2) interactions among TFs known as co-binding effects. In this paper, we propose a novel deep architecture, the Prototype Matching Network (PMN) to mimic the TF binding mechanisms. Our PMN model automatically extracts prototypes (“motif”-like features) for each TF through a novel prototype-matching loss. Borrowing ideas from few-shot matching models, we use the notion of support set of prototypes and an LSTM to learn how TFs interact and bind to genomic sequences. On a reference TFBS dataset with 2.1 million genomic sequences, PMN significantly outperforms baselines and validates our design choices empirically. To our knowledge, this is the first deep learning architecture that introduces prototype learning and considers TF-TF interactions for large scale TFBS prediction. Not only is the proposed architecture accurate, but it also models the underlying biology.", "output": [ "We combine the matching network framework for few shot learning into a large scale multi-label model for genomic sequence classification." ] }, { "id": "task668-f8fd417827544c619b99181f9ef7e057", "input": "Value-based methods constitute a fundamental methodology in planning and deep reinforcement learning (RL). In this paper, we propose to exploit the underlying structures of the state-action value function, i.e., Q function, for both planning and deep RL. In particular, if the underlying system dynamics lead to some global structures of the Q function, one should be capable of inferring the function better by leveraging such structures. Specifically, we investigate the low-rank structure, which widely exists for big data matrices. We verify empirically the existence of low-rank Q functions in the context of control and deep RL tasks (Atari games). As our key contribution, by leveraging Matrix Estimation (ME) techniques, we propose a general framework to exploit the underlying low-rank structure in Q functions, leading to a more efficient planning procedure for classical control, and additionally, a simple scheme that can be applied to any value-based RL techniques to consistently achieve better performance on ''low-rank'' tasks. Extensive experiments on control tasks and Atari games confirm the efficacy of our approach.", "output": [ "We propose a generic framework that allows for exploiting the low-rank structure in both planning and deep reinforcement learning." ] }, { "id": "task668-186d8b0faec44fba87e02942728d755d", "input": "This paper proposes the use of spectral element methods \\citep{canuto_spectral_1988} for fast and accurate training of Neural Ordinary Differential Equations (ODE-Nets; \\citealp{Chen2018NeuralOD}) for system identification. This is achieved by expressing their dynamics as a truncated series of Legendre polynomials. The series coefficients, as well as the network weights, are computed by minimizing the weighted sum of the loss function and the violation of the ODE-Net dynamics. The problem is solved by coordinate descent that alternately minimizes, with respect to the coefficients and the weights, two unconstrained sub-problems using standard backpropagation and gradient methods. The resulting optimization scheme is fully time-parallel and results in a low memory footprint. Experimental comparison to standard methods, such as backpropagation through explicit solvers and the adjoint technique \\citep{Chen2018NeuralOD}, on training surrogate models of small and medium-scale dynamical systems shows that it is at least one order of magnitude faster at reaching a comparable value of the loss function. The corresponding testing MSE is one order of magnitude smaller as well, suggesting generalization capabilities increase.", "output": [ "This paper proposes the use of spectral element methods for fast and accurate training of Neural Ordinary Differential Equations for system identification." ] }, { "id": "task668-e8e2f31ecfc043a384f5c788e4f1873e", "input": "Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven notoriously difficult to understand how they reach their decisions. We here introduce a high-performance DNN architecture on ImageNet whose decisions are considerably easier to explain. Our model, a simple variant of the ResNet-50 architecture called BagNet, classifies an image based on the occurrences of small local image features without taking into account their spatial ordering. This strategy is closely related to the bag-of-feature (BoF) models popular before the onset of deep learning and reaches a surprisingly high accuracy on ImageNet (87.6% top-5 for 32 x 32 px features and Alexnet performance for 16 x16 px features). The constraint on local features makes it straight-forward to analyse how exactly each part of the image influences the classification. Furthermore, the BagNets behave similar to state-of-the art deep neural networks such as VGG-16, ResNet-152 or DenseNet-169 in terms of feature sensitivity, error distribution and interactions between image parts. This suggests that the improvements of DNNs over previous bag-of-feature classifiers in the last few years is mostly achieved by better fine-tuning rather than by qualitatively different decision strategies.", "output": [ "Aggregating class evidence from many small image patches suffices to solve ImageNet, yields more interpretable models and can explain aspects of the decision-making of popular DNNs.", "This paper suggests a novel and compact neural network architecture which uses the information within bag-of-words features. The proposed algorithm only uses the patch information independently and performs majority voting using independently classified patches." ] }, { "id": "task668-a94295327ffe433fa7cd2f45032ee9de", "input": "Recent pretrained transformer-based language models have set state-of-the-art performances on various NLP datasets. However, despite their great progress, they suffer from various structural and syntactic biases. In this work, we investigate the lexical overlap bias, e.g., the model classifies two sentences that have a high lexical overlap as entailing regardless of their underlying meaning. To improve the robustness, we enrich input sentences of the training data with their automatically detected predicate-argument structures. This enhanced representation allows the transformer-based models to learn different attention patterns by focusing on and recognizing the major semantically and syntactically important parts of the sentences. We evaluate our solution for the tasks of natural language inference and grounded commonsense inference using the BERT, RoBERTa, and XLNET models. We evaluate the models' understanding of syntactic variations, antonym relations, and named entities in the presence of lexical overlap. Our results show that the incorporation of predicate-argument structures during fine-tuning considerably improves the robustness, e.g., about 20pp on discriminating different named entities, while it incurs no additional cost at the test time and does not require changing the model or the training procedure.", "output": [ "Enhancing the robustness of pretrained transformer models against the lexical overlap bias by extending the input sentences of the training data with their corresponding predicate-argument structures " ] }, { "id": "task668-1571503b88ce4abbbb951201b5c7b167", "input": "Recent advances in computing technology and sensor design have made it easier to collect longitudinal or time series data from patients, resulting in a gigantic amount of available medical data. Most of the medical time series lack annotations or even when the annotations are available they could be subjective and prone to human errors. Earlier works have developed natural language processing techniques to extract concept annotations and/or clinical narratives from doctor notes. However, these approaches are slow and do not use the accompanying medical time series data. To address this issue, we introduce the problem of concept annotation for the medical time series data, i.e., the task of predicting and localizing medical concepts by using the time series data as input. We propose Relational Multi-Instance Learning (RMIL) - a deep Multi Instance Learning framework based on recurrent neural networks, which uses pooling functions and attention mechanisms for the concept annotation tasks. Empirical results on medical datasets show that our proposed models outperform various multi-instance learning models.", "output": [ "We propose a deep Multi Instance Learning framework based on recurrent neural networks which uses pooling functions and attention mechanisms for the concept annotation tasks.", "The paper addresses the classification of medical time-series data and proposes to model the temporal relationship between the instances in each series using a recurrent neural network architecture. ", "Proposes a novel Multiple Instance Learning (MIL) formulation called Relation MIL (RMIL), and discussed a number of its variants with LSTM, Bi-LSTM, S2S, etc. and explores integrating RMIL with various attention mechanisms, and demonstrates its usage on medical concept prediction from time series data. " ] }, { "id": "task668-cc368dd9420f480d930639504bdbc5a4", "input": "Existing black-box attacks on deep neural networks (DNNs) so far have largely focused on transferability, where an adversarial instance generated for a locally trained model can “transfer” to attack other learning models. In this paper, we propose novel Gradient Estimation black-box attacks for adversaries with query access to the target model’s class probabilities, which do not rely on transferability. We also propose strategies to decouple the number of queries required to generate each adversarial sample from the dimensionality of the input. An iterative variant of our attack achieves close to 100% adversarial success rates for both targeted and untargeted attacks on DNNs. We carry out extensive experiments for a thorough comparative evaluation of black-box attacks and show that the proposed Gradient Estimation attacks outperform all transferability based black-box attacks we tested on both MNIST and CIFAR-10 datasets, achieving adversarial success rates similar to well known, state-of-the-art white-box attacks. We also apply the Gradient Estimation attacks successfully against a real-world content moderation classifier hosted by Clarifai. Furthermore, we evaluate black-box attacks against state-of-the-art defenses. We show that the Gradient Estimation attacks are very effective even against these defenses.", "output": [ "Query-based black-box attacks on deep neural networks with adversarial success rates matching white-box attacks" ] }, { "id": "task668-1520ca02227649fe8993e4831a4ab0bf", "input": "We propose a new architecture termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are proposed to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD). Additionally, adversarial training is adopted to boost model generalization. We examine the effects of different components in DATNet across domains and languages and show that significant improvement can be obtained especially for low-resource data. Without augmenting any additional hand-crafted features, we achieve new state-of-the-art performances on CoNLL and Twitter NER---88.16% F1 for Spanish, 53.43% F1 for WNUT-2016, and 42.83% F1 for WNUT-2017.", "output": [ "We propose a new architecture termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER) and achieve new state-of-the-art performances on CoNLL and Twitter NER." ] }, { "id": "task668-2539776aa7e145e9928c0768856ddecb", "input": "Distributed computing can significantly reduce the training time of neural networks. Despite its potential, however, distributed training has not been widely adopted: scaling the training process is difficult, and existing SGD methods require substantial tuning of hyperparameters and learning schedules to achieve sufficient accuracy when increasing the number of workers. In practice, such tuning can be prohibitively expensive given the huge number of potential hyperparameter configurations and the effort required to test each one.\n \n We propose DANA, a novel approach that scales out-of-the-box to large clusters using the same hyperparameters and learning schedule optimized for training on a single worker, while maintaining similar final accuracy without additional overhead. DANA estimates the future value of model parameters by adapting Nesterov Accelerated Gradient to a distributed setting, and so mitigates the effect of gradient staleness, one of the main difficulties in scaling SGD to more workers.\n\n Evaluation on three state-of-the-art network architectures and three datasets shows that DANA scales as well as or better than existing work without having to tune any hyperparameters or tweak the learning schedule. For example, DANA achieves 75.73% accuracy on ImageNet when training ResNet-50 with 16 workers, similar to the non-distributed baseline.", "output": [ "A new distributed asynchronous SGD algorithm that achieves state-of-the-art accuracy on existing architectures without any additional tuning or overhead.", "Proposes an improvement to existing ASGD approaches at mid-size scaling using momentum with SGD for asynchronous training across a distributed worker pool.", "This paper addresses the gradient staleness vs parallel performance problem in distributed deep learning training, and proposes an approach to estimate future model parameters at the slaves to reduce communication latency effects." ] }, { "id": "task668-42aa4567a2ef4f5a8516bce4f1002f33", "input": "Transformer networks have lead to important progress in language modeling and machine translation. These models include two consecutive modules, a feed-forward layer and a self-attention layer. The latter allows the network to capture long term dependencies and are often regarded as the key ingredient in the success of Transformers. Building upon this intuition, we propose a new model that solely consists of attention layers. More precisely, we augment the self-attention layers with persistent memory vectors that play a similar role as the feed-forward layer. Thanks to these vectors, we can remove the feed-forward layer without degrading the performance of a transformer. Our evaluation shows the benefits brought by our model on standard character and word level language modeling benchmarks.", "output": [ "A novel attention layer that combines self-attention and feed-forward sublayers of Transformer networks.", "This paper proposes a modification to the Transformer model by incorporating attention over \"persistent\" memory vectors into the self-attention layer, resulting in performance on par with existing models while using fewer parameters." ] }, { "id": "task668-c4876caebd494c4bab4f77884d51a8b7", "input": "Our work addresses two important issues with recurrent neural networks: (1) they are over-parameterized, and (2) the recurrent weight matrix is ill-conditioned. The former increases the sample complexity of learning and the training time. The latter causes the vanishing and exploding gradient problem. We present a flexible recurrent neural network model called Kronecker Recurrent Units (KRU). KRU achieves parameter efficiency in RNNs through a Kronecker factored recurrent matrix. It overcomes the ill-conditioning of the recurrent matrix by enforcing soft unitary constraints on the factors. Thanks to the small dimensionality of the factors, maintaining these constraints is computationally efficient. Our experimental results on seven standard data-sets reveal that KRU can reduce the number of parameters by three orders of magnitude in the recurrent weight matrix compared to the existing recurrent models, without trading the statistical performance. These results in particular show that while there are advantages in having a high dimensional recurrent space, the capacity of the recurrent part of the model can be dramatically reduced.", "output": [ "Out work presents a Kronecker factorization of recurrent weight matrices for parameter efficient and well conditioned recurrent neural networks." ] }, { "id": "task668-1ff6bbf0a8b644b690a53fc2ce00cb58", "input": "In this paper, we study the representational power of deep neural networks (DNN) that belong to the family of piecewise-linear (PWL) functions, based on PWL activation units such as rectifier or maxout. We investigate the complexity of such networks by studying the number of linear regions of the PWL function. Typically, a PWL function from a DNN can be seen as a large family of linear functions acting on millions of such regions. We directly build upon the work of Mont´ufar et al. (2014), Mont´ufar (2017), and Raghu et al. (2017) by refining the upper and lower bounds on the number of linear regions for rectified and maxout networks. In addition to achieving tighter bounds, we also develop a novel method to perform exact numeration or counting of the number of linear regions with a mixed-integer linear formulation that maps the input space to output. We use this new capability to visualize how the number of linear regions change while training DNNs. ", "output": [ "We empirically count the number of linear regions of rectifier networks and refine upper and lower bounds.", "This paper presents improved bounds for counting the number of linear regions in ReLU networks." ] }, { "id": "task668-ef3eeaedf30b40888ce940ece35f6091", "input": "Deep neural networks have achieved impressive performance in handling complicated semantics in natural language, while mostly treated as black boxes. To explain how the model handles compositional semantics of words and phrases, we study the hierarchical explanation problem. We highlight the key challenge is to compute non-additive and context-independent importance for individual words and phrases. We show some prior efforts on hierarchical explanations, e.g. contextual decomposition, do not satisfy the desired properties mathematically, leading to inconsistent explanation quality in different models. In this paper, we propose a formal way to quantify the importance of each word or phrase to generate hierarchical explanations. We modify contextual decomposition algorithms according to our formulation, and propose a model-agnostic explanation algorithm with competitive performance. Human evaluation and automatic metrics evaluation on both LSTM models and fine-tuned BERT Transformer models on multiple datasets show that our algorithms robustly outperform prior works on hierarchical explanations. We show our algorithms help explain compositionality of semantics, extract classification rules, and improve human trust of models.", "output": [ "We propose measurement of phrase importance and algorithms for hierarchical explanation of neural sequence model predictions" ] }, { "id": "task668-28ebcdc385444119b39b05f97e448fef", "input": "Learning distributed representations for nodes in graphs is a crucial primitive in network analysis with a wide spectrum of applications. Linear graph embedding methods learn such representations by optimizing the likelihood of both positive and negative edges while constraining the dimension of the embedding vectors. We argue that the generalization performance of these methods is not due to the dimensionality constraint as commonly believed, but rather the small norm of embedding vectors. Both theoretical and empirical evidence are provided to support this argument: (a) we prove that the generalization error of these methods can be bounded by limiting the norm of vectors, regardless of the embedding dimension; (b) we show that the generalization performance of linear graph embedding methods is correlated with the norm of embedding vectors, which is small due to the early stopping of SGD and the vanishing gradients. We performed extensive experiments to validate our analysis and showcased the importance of proper norm regularization in practice.", "output": [ "We argue that the generalization of linear graph embedding is not due to the dimensionality constraint but rather the small norm of embedding vectors.", "The authors show that the generalization error of linear graph embedding methods is bounded by the norm of embedding vectors rather than dimensionality constraints", "The authors propose a theoretical bound on the generalization performance of learning graph embeddings and argue that the norm of the coordinates determines the success of the learnt representation." ] }, { "id": "task668-e6253a39f27f4078a6eda5702e1e6918", "input": "We present a graph neural network assisted Monte Carlo Tree Search approach for the classical traveling salesman problem (TSP). We adopt a greedy algorithm framework to construct the optimal solution to TSP by adding the nodes successively. A graph neural network (GNN) is trained to capture the local and global graph structure and give the prior probability of selecting each vertex every step. The prior probability provides a heuristics for MCTS, and the MCTS output is an improved probability for selecting the successive vertex, as it is the feedback information by fusing the prior with the scouting procedure. Experimental results on TSP up to 100 nodes demonstrate that the proposed method obtains shorter tours than other learning-based methods.", "output": [ "A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem" ] }, { "id": "task668-b095d0d008394fc99b621af484fa68c4", "input": "Backprop is the primary learning algorithm used in many machine learning algorithms. In practice, however, Backprop in deep neural networks is a highly sensitive learning algorithm and successful learning depends on numerous conditions and constraints. One set of constraints is to avoid weights that lead to saturated units. The motivation for avoiding unit saturation is that gradients vanish and as a result learning comes to a halt. Careful weight initialization and re-scaling schemes such as batch normalization ensure that input activity to the neuron is within the linear regime where gradients are not vanished and can flow. Here we investigate backpropagating error terms only linearly. That is, we ignore the saturation that arise by ensuring gradients always flow. We refer to this learning rule as Linear Backprop since in the backward pass the network appears to be linear. In addition to ensuring persistent gradient flow, Linear Backprop is also favorable when computation is expensive since gradients are never computed. Our early results suggest that learning with Linear Backprop is competitive with Backprop and saves expensive gradient computations.", "output": [ "We ignore non-linearities and do not compute gradients in the backward pass to save computation and to ensure gradients always flow. ", "The author proposed linear backprop algorithms to ensure gradients flow for all parts during backpropagation." ] }, { "id": "task668-1c5f84a510a14643852fe91f693dbbf2", "input": "We present a new method for black-box adversarial attack. Unlike previous methods that combined transfer-based and scored-based methods by using the gradient or initialization of a surrogate white-box model, this new method tries to learn a low-dimensional embedding using a pretrained model, and then performs efficient search within the embedding space to attack an unknown target network. The method produces adversarial perturbations with high level semantic patterns that are easily transferable. We show that this approach can greatly improve the query efficiency of black-box adversarial attack across different target network architectures. We evaluate our approach on MNIST, ImageNet and Google Cloud Vision API, resulting in a significant reduction on the number of queries. We also attack adversarially defended networks on CIFAR10 and ImageNet, where our method not only reduces the number of queries, but also improves the attack success rate.", "output": [ "We present a new method that combines transfer-based and scored black-box adversarial attack, improving the success rate and query efficiency of black-box adversarial attack across different network architectures." ] }, { "id": "task668-34b1a62abc4a449193f25f288d26c45a", "input": "Estimating covariances between financial assets plays an important role in risk management. In practice, when the sample size is small compared to the number of variables, the empirical estimate is known to be very unstable. Here, we propose a novel covariance estimator based on the Gaussian Process Latent Variable Model (GP-LVM). Our estimator can be considered as a non-linear extension of standard factor models with readily interpretable parameters reminiscent of market betas. Furthermore, our Bayesian treatment naturally shrinks the sample covariance matrix towards a more structured matrix given by the prior and thereby systematically reduces estimation errors. Finally, we discuss some financial applications of the GP-LVM model.", "output": [ "Covariance matrix estimation of financial assets with Gaussian Process Latent Variable Models", "Illustrates how the Gaussian Process Latent Variable Model (GP-LVM) can replace classical linear factor models for the estimation of covariance matrices in portfolio optimization problems.", "This paper uses standard GPLVMs to model the covariance structure and a latent space representation of S&P500 financial time series, to optimize portfolios and predict missing values.", "This paper proposes to use a GPLVM to model financial returns" ] }, { "id": "task668-dd4725b677b746cf8e04a94f3edaeb2b", "input": "Most approaches to learning action planning models heavily rely on a significantly large volume of training samples or plan observations. In this paper, we adopt a different approach based on deductive learning from domain-specific knowledge, specifically from logic formulae that specify constraints about the possible states of a given domain. The minimal input observability required by our approach is a single example composed of a full initial state and a partial goal state. We will show that exploiting specific domain knowledge enables to constrain the space of possible action models as well as to complete partial observations, both of which turn out helpful to learn good-quality action models.", "output": [ "Hybrid approach to model acquisition that compensates a lack of available data with domain specific knowledge provided by experts", "A domain acquisition approach that considers using a different representation for the partial domain model by using schematic mutex relations in place of pre/post conditions." ] }, { "id": "task668-9ca4f441105344c5a1d73cb2c4c7a2e8", "input": "Effective training of neural networks requires much data. In the low-data regime,\n parameters are underdetermined, and learnt networks generalise poorly. Data\n Augmentation (Krizhevsky et al., 2012) alleviates this by using existing data\n more effectively. However standard data augmentation produces only limited\n plausible alternative data. Given there is potential to generate a much broader set\n of augmentations, we design and train a generative model to do data augmentation.\n The model, based on image conditional Generative Adversarial Networks, takes\n data from a source domain and learns to take any data item and generalise it\n to generate other within-class data items. As this generative process does not\n depend on the classes themselves, it can be applied to novel unseen classes of data.\n We show that a Data Augmentation Generative Adversarial Network (DAGAN)\n augments standard vanilla classifiers well. We also show a DAGAN can enhance\n few-shot learning systems such as Matching Networks. We demonstrate these\n approaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and\n VGG-Face data. In our experiments we can see over 13% increase in accuracy in\n the low-data regime experiments in Omniglot (from 69% to 82%), EMNIST (73.9%\n to 76%) and VGG-Face (4.5% to 12%); in Matching Networks for Omniglot we\n observe an increase of 0.5% (from 96.9% to 97.4%) and an increase of 1.8% in\n EMNIST (from 59.5% to 61.3%).", "output": [ "Conditional GANs trained to generate data augmented samples of their conditional inputs used to enhance vanilla classification and one shot learning systems such as matching networks and pixel distance", "The authors propose a method to conduct data augmentation where the cross-class transformations are mapped to a low dimensional latent space using conditional GAN" ] }, { "id": "task668-dad516512027488da7b0b1d211424a2c", "input": "Many biological learning systems such as the mushroom body, hippocampus, and cerebellum are built from sparsely connected networks of neurons. For a new understanding of such networks, we study the function spaces induced by sparse random features and characterize what functions may and may not be learned. A network with d inputs per neuron is found to be equivalent to an additive model of order d, whereas with a degree distribution the network combines additive terms of different orders. We identify three specific advantages of sparsity: additive function approximation is a powerful inductive bias that limits the curse of dimensionality, sparse networks are stable to outlier noise in the inputs, and sparse random features are scalable. Thus, even simple brain architectures can be powerful function approximators. Finally, we hope that this work helps popularize kernel theories of networks among computational neuroscientists.", "output": [ "We advocate for random features as a theory of biological neural networks, focusing on sparsely connected networks" ] }, { "id": "task668-2e85a6db0d414cd1a10b48db854263d0", "input": "We present a new unsupervised method for learning general-purpose sentence embeddings.\n Unlike existing methods which rely on local contexts, such as words\n inside the sentence or immediately neighboring sentences, our method selects, for\n each target sentence, influential sentences in the entire document based on a document\n structure. We identify a dependency structure of sentences using metadata\n or text styles. Furthermore, we propose a novel out-of-vocabulary word handling\n technique to model many domain-specific terms, which were mostly discarded by\n existing sentence embedding methods. We validate our model on several tasks\n showing 30% precision improvement in coreference resolution in a technical domain,\n and 7.5% accuracy increase in paraphrase detection compared to baselines.", "output": [ "To train a sentence embedding using technical documents, our approach considers document structure to find broader context and handle out-of-vocabulary words.", "Presents ideas for improving sentence embedding by drawing from more context.", "Learning sentence representations with sentences dependencies information", "Extends the idea of forming an unsupervised representation of sentences used in the SkipThough approach by using a broader set of evidence for forming the representation of a sentence" ] }, { "id": "task668-56600b83633d4cfe9d1f7217afe63164", "input": "We present a simple approach based on pixel-wise nearest neighbors to understand and interpret the functioning of state-of-the-art neural networks for pixel-level tasks. We aim to understand and uncover the synthesis/prediction mechanisms of state-of-the-art convolutional neural networks. To this end, we primarily analyze the synthesis process of generative models and the prediction mechanism of discriminative models. The main hypothesis of this work is that convolutional neural networks for pixel-level tasks learn a fast compositional nearest neighbor synthesis/prediction function. Our experiments on semantic segmentation and image-to-image translation show qualitative and quantitative evidence supporting this hypothesis.", "output": [ "Convolutional Neural Networks behave as Compositional Nearest Neighbors!" ] }, { "id": "task668-ba66053f33c64a2b9febffc4026c6ed9", "input": "Neural language models (NLMs) are generative, and they model the distribution of grammatical sentences. Trained on huge corpus, NLMs are pushing the limit of modeling accuracy. Besides, they have also been applied to supervised learning tasks that decode text, e.g., automatic speech recognition (ASR). By re-scoring the n-best list, NLM can select grammatically more correct candidate among the list, and significantly reduce word/char error rate. However, the generative nature of NLM may not guarantee a discrimination between “good” and “bad” (in a task-specific sense) sentences, resulting in suboptimal performance. This work proposes an approach to adapt a generative NLM to a discriminative one. Different from the commonly used maximum likelihood objective, the proposed method aims at enlarging the margin between the “good” and “bad” sentences. It is trained end-to-end and can be widely applied to tasks that involve the re-scoring of the decoded text. Significant gains are observed in both ASR and statistical machine translation (SMT) tasks.", "output": [ "Enhance the language model for supervised learning task " ] }, { "id": "task668-a4a9e43f63a14e0ea3732b6d80fad5d8", "input": "We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data. We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables using neural networks. This extension allows to model complex interactions while being more global in its search compared to other greedy approaches. In addition to comparing our method to existing continuous optimization methods, we provide missing empirical comparisons to nonlinear greedy search methods. On both synthetic and real-world data sets, this new method outperforms current continuous methods on most tasks while being competitive with existing greedy search methods on important metrics for causal inference.", "output": [ "We are proposing a new score-based approach to structure/causal learning leveraging neural networks and a recent continuous constrained formulation to this problem" ] }, { "id": "task668-395fc5a7e0524876ac16fb05209fcb30", "input": "Quantum machine learning methods have the potential to facilitate learning using extremely large datasets. While the availability of data for training machine learning models is steadily increasing, oftentimes it is much easier to collect feature vectors that to obtain the corresponding labels. One of the approaches for addressing this issue is to use semi-supervised learning, which leverages not only the labeled samples, but also unlabeled feature vectors. Here, we present a quantum machine learning algorithm for training Semi-Supervised Kernel Support Vector Machines. The algorithm uses recent advances in quantum sample-based Hamiltonian simulation to extend the existing Quantum LS-SVM algorithm to handle the semi-supervised term in the loss, while maintaining the same quantum speedup as the Quantum LS-SVM.", "output": [ "We extend quantum SVMs to semi-supervised setting, to deal with the likely problem of many missing class labels in huge datasets." ] }, { "id": "task668-5c3f32db72894764b77e187325b35d82", "input": "Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world. For example, it can be used to simulate the environment, or to infer the state of parts of the world that are currently unobserved. In order to match real-world conditions this causal knowledge must be learned without access to supervised data. To address this problem we present a novel method that learns to discover objects and model their physical interactions from raw visual images in a purely unsupervised fashion. It incorporates prior knowledge about the compositional nature of human perception to factor interactions between object-pairs and learn efficiently. On videos of bouncing balls we show the superior modelling capabilities of our method compared to other unsupervised neural approaches that do not incorporate such prior knowledge. We demonstrate its ability to handle occlusion and show that it can extrapolate learned knowledge to scenes with different numbers of objects.", "output": [ "We introduce a novel approach to common-sense physical reasoning that learns to discover objects and model their physical interactions from raw visual images in a purely unsupervised fashion" ] }, { "id": "task668-e771a7c8f28444bbad282747d928759a", "input": "We consider the problem of improving kernel approximation via feature maps. These maps arise as Monte Carlo approximation to integral representations of kernel functions and scale up kernel methods for larger datasets. We propose to use more efficient numerical integration technique to obtain better estimates of the integrals compared to the state-of-the-art methods. Our approach allows to use information about the integrand to enhance approximation and facilitates fast computations. We derive the convergence behavior and conduct an extensive empirical study that supports our hypothesis.", "output": [ "Quadrature rules for kernel approximation.", "The paper proposes improving the kernel approximation of random features by using quadrature rules like stochastic spherical-radial rules.", "The authors propose a novel version of the random feature map approach to approximately solve large-scale kernel problems.", "This paper shows that techniques due to Genz & Monahan (1998) can be used to achieve low kernel approximation error under the framework of random fourier feature, a new way to apply quadrature rules to improve kernel approximation." ] }, { "id": "task668-54033408f41349ca84917eba7bd32053", "input": "We propose a new approach, known as the iterative regularized dual averaging (iRDA), to improve the efficiency of convolutional neural networks (CNN) by significantly reducing the redundancy of the model without reducing its accuracy. The method has been tested for various data sets, and proven to be significantly more efficient than most existing compressing techniques in the deep learning literature. For many popular data sets such as MNIST and CIFAR-10, more than 95% of the weights can be zeroed out without losing accuracy. In particular, we are able to make ResNet18 with 95% sparsity to have an accuracy that is comparable to that of a much larger model ResNet50 with the best 60% sparsity as reported in the literature.", "output": [ "A sparse optimization algorithm for deep CNN models." ] }, { "id": "task668-8a07751d67a6410baa2cdd31f172b448", "input": "Contextualized word representations, such as ELMo and BERT, were shown to perform well on a various of semantic and structural (syntactic) task. In this work, we tackle the task of unsupervised disentanglement between semantics and structure in neural language representations: we aim to learn a transformation of the contextualized vectors, that discards the lexical semantics, but keeps the structural information. To this end, we automatically generate groups of sentences which are structurally similar but semantically different, and use metric-learning approach to learn a transformation that emphasizes the structural component that is encoded in the vectors. We demonstrate that our transformation clusters vectors in space by structural properties, rather than by lexical semantics. Finally, we demonstrate the utility of our distilled representations by showing that they outperform the original contextualized representations in few-shot parsing setting.", "output": [ "We distill language models representations for syntax by unsupervised metric learning" ] }, { "id": "task668-0447a95e8a674e11a2a84e51fabba893", "input": "Training a model to perform a task typically requires a large amount of data from the domains in which the task will be applied. However, it is often the case that data are abundant in some domains but scarce in others. Domain adaptation deals with the challenge of adapting a model trained from a data-rich source domain to perform well in a data-poor target domain. In general, this requires learning plausible mappings between domains. CycleGAN is a powerful framework that efficiently learns to map inputs from one domain to another using adversarial training and a cycle-consistency constraint. However, the conventional approach of enforcing cycle-consistency via reconstruction may be overly restrictive in cases where one or more domains have limited training data. In this paper, we propose an augmented cyclic adversarial learning model that enforces the cycle-consistency constraint via an external task specific model, which encourages the preservation of task-relevant content as opposed to exact reconstruction. This task specific model both relaxes the cycle-consistency constraint and complements the role of the discriminator during training, serving as an augmented information source for learning the mapping. We explore adaptation in speech and visual domains in low resource in supervised setting. In speech domains, we adopt a speech recognition model from each domain as the task specific model. Our approach improves absolute performance of speech recognition by 2% for female speakers in the TIMIT dataset, where the majority of training samples are from male voices. In low-resource visual domain adaptation, the results show that our approach improves absolute performance by 14% and 4% when adapting SVHN to MNIST and vice versa, respectively, which outperforms unsupervised domain adaptation methods that require high-resource unlabeled target domain. \n", "output": [ "A robust domain adaptation by employing a task specific loss in cyclic adversarial learning" ] }, { "id": "task668-f6d5b8d0830c429492fbaf8fb2f7d98c", "input": "Bayesian inference promises to ground and improve the performance of deep neural networks. It promises to be robust to overfitting, to simplify the training procedure and the space of hyperparameters, and to provide a calibrated measure of uncertainty that can enhance decision making, agent exploration and prediction fairness.\n Markov Chain Monte Carlo (MCMC) methods enable Bayesian inference by generating samples from the posterior distribution over model parameters.\n Despite the theoretical advantages of Bayesian inference and the similarity between MCMC and optimization methods, the performance of sampling methods has so far lagged behind optimization methods for large scale deep learning tasks.\n We aim to fill this gap and introduce ATMC, an adaptive noise MCMC algorithm that estimates and is able to sample from the posterior of a neural network.\n ATMC dynamically adjusts the amount of momentum and noise applied to each parameter update in order to compensate for the use of stochastic gradients.\n We use a ResNet architecture without batch normalization to test ATMC on the Cifar10 benchmark and the large scale ImageNet benchmark and show that, despite the absence of batch normalization, ATMC outperforms a strong optimization baseline in terms of both classification accuracy and test log-likelihood. We show that ATMC is intrinsically robust to overfitting on the training data and that ATMC provides a better calibrated measure of uncertainty compared to the optimization baseline.", "output": [ "We scale Bayesian Inference to ImageNet classification and achieve competitive results accuracy and uncertainty calibration.", "An adaptive noise MCMC algorithm for image classification that dynamically adjusts the momentum and noise applied to each parameter update, and is robust to overfitting and provides an uncertainty measure with predictions. " ] }, { "id": "task668-aa1b8bee38514d97844acac5011ce29f", "input": "One of the long-standing challenges in Artificial Intelligence for learning goal-directed behavior is to build a single agent which can solve multiple tasks. Recent progress in multi-task learning for goal-directed sequential problems has been in the form of distillation based learning wherein a student network learns from multiple task-specific expert networks by mimicking the task-specific policies of the expert networks. While such approaches offer a promising solution to the multi-task learning problem, they require supervision from large expert networks which require extensive data and computation time for training.\n In this work, we propose an efficient multi-task learning framework which solves multiple goal-directed tasks in an on-line setup without the need for expert supervision. Our work uses active learning principles to achieve multi-task learning by sampling the harder tasks more than the easier ones. We propose three distinct models under our active sampling framework. An adaptive method with extremely competitive multi-tasking performance. A UCB-based meta-learner which casts the problem of picking the next task to train on as a multi-armed bandit problem. A meta-learning method that casts the next-task picking problem as a full Reinforcement Learning problem and uses actor-critic methods for optimizing the multi-tasking performance directly. We demonstrate results in the Atari 2600 domain on seven multi-tasking instances: three 6-task instances, one 8-task instance, two 12-task instances and one 21-task instance.", "output": [ "Letting a meta-learner decide the task to train on for an agent in a multi-task setting improves multi-tasking ability substantially" ] }, { "id": "task668-803f1381aaaa4155b0e8cd92e1931498", "input": "Stochastic gradient descent (SGD) is widely believed to perform implicit regularization when used to train deep neural networks, but the precise manner in which this occurs has thus far been elusive. We prove that SGD minimizes an average potential over the posterior distribution of weights along with an entropic regularization term. This potential is however not the original loss function in general. So SGD does perform variational inference, but for a different loss than the one used to compute the gradients. Even more surprisingly, SGD does not even converge in the classical sense: we show that the most likely trajectories of SGD for deep networks do not behave like Brownian motion around critical points. Instead, they resemble closed loops with deterministic components. We prove that such out-of-equilibrium behavior is a consequence of highly non-isotropic gradient noise in SGD; the covariance matrix of mini-batch gradients for deep networks has a rank as small as 1% of its dimension. We provide extensive empirical validation of these claims, proven in the appendix.", "output": [ "SGD implicitly performs variational inference; gradient noise is highly non-isotropic, so SGD does not even converge to critical points of the original loss", "This paper provides a variational analysis of SGD as a non-equilibrium process.", "This paper discusses the regularized objective function minimized by standard SGD in the context of neural nets, and provide a variational inference perspective using the Fokker-Planck equation.", "Develops a theory to study the impact of stocastic gradient noise for SGD, especially for deep neural network models" ] }, { "id": "task668-b3f2ed27373949a0b90d7d594f7c1ac5", "input": "Few shot image classification aims at learning a classifier from limited labeled data. Generating the classification weights has been applied in many meta-learning approaches for few shot image classification due to its simplicity and effectiveness. However, we argue that it is difficult to generate the exact and universal classification weights for all the diverse query samples from very few training samples. In this work, we introduce Attentive Weights Generation for few shot learning via Information Maximization (AWGIM), which addresses current issues by two novel contributions. i) AWGIM generates different classification weights for different query samples by letting each of query samples attends to the whole support set. ii) To guarantee the generated weights adaptive to different query sample, we re-formulate the problem to maximize the lower bound of mutual information between generated weights and query as well as support data. As far as we can see, this is the first attempt to unify information maximization into few shot learning. Both two contributions are proved to be effective in the extensive experiments and we show that AWGIM is able to achieve state-of-the-art performance on benchmark datasets.", "output": [ "A novel few shot learning method to generate query-specific classification weights via information maximization." ] }, { "id": "task668-b23666a81f12429e949077f92de754fe", "input": "The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which oftentimes involves cohesive clustering with a focus on instance relations. To overcome the limitations of flat clustering, we introduce hierarchically clustered representation learning (HCRL), which simultaneously optimizes representation learning and hierarchical clustering in the embedding space. Specifically, we place a nonparametric Bayesian prior on embeddings to handle dynamic mixture hierarchies under the variational autoencoder framework, and to adopt the generative process of a hierarchical-versioned Gaussian mixture model. Compared with a few prior works focusing on unifying representation learning and hierarchical clustering, HCRL is the first model to consider a generation of deep embeddings from every component of the hierarchy, not just leaf components. This generation process enables more meaningful separations and mergers of clusters via branches in a hierarchy. In addition to obtaining hierarchically clustered embeddings, we can reconstruct data by the various abstraction levels, infer the intrinsic hierarchical structure, and learn the level-proportion features. We conducted evaluations with image and text domains, and our quantitative analyses showed competent likelihoods and the best accuracies compared with the baselines.", "output": [ "We introduce hierarchically clustered representation learning (HCRL), which simultaneously optimizes representation learning and hierarchical clustering in the embedding space.", "The paper proposes using the nested CRP as a clustering model rather than a topic model", "Presents a novel hierarchical clustering method over an embedding space where both the embedding space and the heirarchical clustering are simultaneously learnt" ] }, { "id": "task668-a532510a7d9e4d0382ee6ed4944666b1", "input": "In this work, we approach one-shot and few-shot learning problems as methods for finding good prototypes for each class, where these prototypes are generalizable to new data samples and classes. We propose a metric learner that learns a Bregman divergence by learning its underlying convex function. Bregman divergences are a good candidate for this framework given they are the only class of divergences with the property that the best representative of a set of points is given by its mean. We propose a flexible extension to prototypical networks to enable joint learning of the embedding and the divergence, while preserving computational efficiency. Our preliminary results are comparable with the prior work on the Omniglot and Mini-ImageNet datasets, two standard benchmarks for one-shot and few-shot learning. We argue that our model can be used for other tasks that involve metric learning or tasks that require approximate convexity such as structured prediction and data completion.", "output": [ "Bregman divergence learning for few-shot learning. " ] }, { "id": "task668-589c659a4ff04c7583d2deb02bc92618", "input": "Distributional Semantics Models(DSM) derive word space from linguistic items\n in context. Meaning is obtained by defining a distance measure between vectors\n corresponding to lexical entities. Such vectors present several problems. This\n work concentrates on quality of word embeddings, improvement of word embedding\n vectors, applicability of a novel similarity metric used ‘on top’ of the\n word embeddings. In this paper we provide comparison between two methods\n for post process improvements to the baseline DSM vectors. The counter-fitting\n method which enforces antonymy and synonymy constraints into the Paragram\n vector space representations recently showed improvement in the vectors’ capability\n for judging semantic similarity. The second method is our novel RESM\n method applied to GloVe baseline vectors. By applying the hubness reduction\n method, implementing relational knowledge into the model by retrofitting synonyms\n and providing a new ranking similarity definition RESM that gives maximum\n weight to the top vector component values we equal the results for the ESL\n and TOEFL sets in comparison with our calculations using the Paragram and Paragram\n + Counter-fitting methods. For SIMLEX-999 gold standard since we cannot\n use the RESM the results using GloVe and PPDB are significantly worse compared\n to Paragram. Apparently, counter-fitting corrects hubness. The Paragram\n or our cosine retrofitting method are state-of-the-art results for the SIMLEX-999\n gold standard. They are 0.2 better for SIMLEX-999 than word2vec with sense\n de-conflation (that was announced to be state-of the-art method for less reliable\n gold standards). Apparently relational knowledge and counter-fitting is more important\n for judging semantic similarity than sense determination for words. It is to\n be mentioned, though that Paragram hyperparameters are fitted to SIMLEX-999\n results. The lesson is that many corrections to word embeddings are necessary\n and methods with more parameters and hyperparameters perform better.\n", "output": [ "Paper provides a description of a procedure to enhance word vector space model with an evaluation of Paragram and GloVe models for Similarity Benchmarks.", "This paper suggests a new algorithm that adjusts GloVe word vectors and then uses a non-Euclidean similarity function between them.", "The authors present observations on the weaknesses of the existing vector space models and list a 6-step approach for refining existing word vectors" ] }, { "id": "task668-98a39e19e26946bea0e4613b2fbadbcc", "input": "Reinforcement learning is a promising framework for solving control problems, but its use in practical situations is hampered by the fact that reward functions are often difficult to engineer. Specifying goals and tasks for autonomous machines, such as robots, is a significant challenge: conventionally, reward functions and goal states have been used to communicate objectives. But people can communicate objectives to each other simply by describing or demonstrating them. How can we build learning algorithms that will allow us to tell machines what we want them to do? In this work, we investigate the problem of grounding language commands as reward functions using inverse reinforcement learning, and argue that language-conditioned rewards are more transferable than language-conditioned policies to new environments. We propose language-conditioned reward learning (LC-RL), which grounds language commands as a reward function represented by a deep neural network. We demonstrate that our model learns rewards that transfer to novel tasks and environments on realistic, high-dimensional visual environments with natural language commands, whereas directly learning a language-conditioned policy leads to poor performance.", "output": [ "We ground language commands in a high-dimensional visual environment by learning language-conditioned rewards using inverse reinforcement learning." ] }, { "id": "task668-0e61535db60a490c860c24264c582b66", "input": "We've seen tremendous success of image generating models these years. Generating images through a neural network is usually pixel-based, which is fundamentally different from how humans create artwork using brushes. To imitate human drawing, interactions between the environment and the agent is required to allow trials. However, the environment is usually non-differentiable, leading to slow convergence and massive computation. In this paper we try to address the discrete nature of software environment with an intermediate, differentiable simulation. We present StrokeNet, a novel model where the agent is trained upon a well-crafted neural approximation of the painting environment. With this approach, our agent was able to learn to write characters such as MNIST digits faster than reinforcement learning approaches in an unsupervised manner. Our primary contribution is the neural simulation of a real-world environment. Furthermore, the agent trained with the emulated environment is able to directly transfer its skills to real-world software.", "output": [ "StrokeNet is a novel architecture where the agent is trained to draw by strokes on a differentiable simulation of the environment, which could effectively exploit the power of back-propagation." ] }, { "id": "task668-1cde33648e30428586218a5dc1abf46e", "input": "Activity of populations of sensory neurons carries stimulus information in both the temporal and the spatial dimensions. This poses the question of how to compactly represent all the information that the population codes carry across all these dimensions. Here, we developed an analytical method to factorize a large number of retinal ganglion cells' spike trains into a robust low-dimensional representation that captures efficiently both their spatial and temporal information. In particular, we extended previously used single-trial space-by-time tensor decomposition based on non-negative matrix factorization to efficiently discount pre-stimulus baseline activity. On data recorded from retinal ganglion cells with strong pre-stimulus baseline, we showed that in situations were the stimulus elicits a strong change in firing rate, our extensions yield a boost in stimulus decoding performance. Our results thus suggest that taking into account the baseline can be important for finding a compact information-rich representation of neural activity.", "output": [ "We extended single-trial space-by-time tensor decomposition based on non-negative matrix factorization to efficiently discount pre-stimulus baseline activity that improves decoding performance on data with non-negligible baselines." ] }, { "id": "task668-3b0a1af21bc04ca3a1d1edc42c321124", "input": "Data augmentation is commonly used to encode invariances in learning methods. However, this process is often performed in an inefficient manner, as artificial examples are created by applying a number of transformations to all points in the training set. The resulting explosion of the dataset size can be an issue in terms of storage and training costs, as well as in selecting and tuning the optimal set of transformations to apply. In this work, we demonstrate that it is possible to significantly reduce the number of data points included in data augmentation while realizing the same accuracy and invariance benefits of augmenting the entire dataset. We propose a novel set of subsampling policies, based on model influence and loss, that can achieve a 90% reduction in augmentation set size while maintaining the accuracy gains of standard data augmentation.", "output": [ "Selectively augmenting difficult to classify points results in efficient training.", "The authors study the problem of identifying subsampling strategies for data augmentation and propose strategies based on model influence and loss as well as empirical benchmarking of the proposed methods.", "The authors propose to use influence or loss-based methods to select a subset of points to use in augmenting data sets for training models where the loss is additive over data points." ] }, { "id": "task668-14a02e3933884540aec44d75c33d1ca8", "input": "We consider the problem of unconstrained minimization of a smooth objective\n function in $\\mathbb{R}^d$ in setting where only function evaluations are possible. We propose and analyze stochastic zeroth-order method with heavy ball momentum. In particular, we propose, SMTP, a momentum version of the stochastic three-point method (STP) Bergou et al. (2019). We show new complexity results for non-convex, convex and strongly convex functions. We test our method on a collection of learning to continuous control tasks on several MuJoCo Todorov et al. (2012) environments with varying difficulty and compare against STP, other state-of-the-art derivative-free optimization algorithms and against policy gradient methods. SMTP significantly outperforms STP and all other methods that we considered in our numerical experiments. Our second contribution is SMTP with importance sampling which we call SMTP_IS. We provide convergence analysis of this method for non-convex, convex and strongly convex objectives.", "output": [ "We develop and analyze a new derivative free optimization algorithm with momentum and importance sampling with applications to continuous control." ] }, { "id": "task668-9826ba258e83479bb07c2d16825e2ff4", "input": "In an explanation generation problem, an agent needs to identify and explain the reasons for its decisions to another agent. Existing work in this area is mostly confined to planning-based systems that use automated planning approaches to solve the problem. In this paper, we approach this problem from a new perspective, where we propose a general logic-based framework for explanation generation. In particular, given a knowledge base $KB_1$ that entails a formula $\\phi$ and a second knowledge base $KB_2$ that does not entail $\\phi$, we seek to identify an explanation $\\epsilon$ that is a subset of $KB_1$ such that the union of $KB_2$ and $\\epsilon$ entails $\\phi$. We define two types of explanations, model- and proof-theoretic explanations, and use cost functions to reflect preferences between explanations. Further, we present our algorithm implemented for propositional logic that compute such explanations and empirically evaluate it in random knowledge bases and a planning domain.", "output": [ "A general framework for explanation generation using Logic.", "This paper researches explanation generation from a KR point of view and conducts experiments measuring explanation size and runtime on random formulas and formulas from a Blocksworld instance.", "This paper provides a perspective on explanations between two knowledge bases, and runs parallel to work on model reconciliation in planning literature." ] }, { "id": "task668-7e6989f78bfd451286162d7ad4f290de", "input": "The softmax function is widely used to train deep neural networks for multi-class classification. Despite its outstanding performance in classification tasks, the features derived from the supervision of softmax are usually sub-optimal in some scenarios where Euclidean distances apply in feature spaces. To address this issue, we propose a new loss, dubbed the isotropic loss, in the sense that the overall distribution of data points is regularized to approach the isotropic normal one. Combined with the vanilla softmax, we formalize a novel criterion called the isotropic softmax, or isomax for short, for supervised learning of deep neural networks. By virtue of the isomax, the intra-class features are penalized by the isotropic loss while inter-class distances are well kept by the original softmax loss. Moreover, the isomax loss does not require any additional modifications to the network, mini-batches or the training process. Extensive experiments on classification and clustering are performed to demonstrate the superiority and robustness of the isomax loss.", "output": [ "The discriminative capability of softmax for learning feature vectors of objects is effectively enhanced by virture of isotropic normalization on global distribution of data points." ] }, { "id": "task668-0a248346361c4fd1bece91d196ffd13f", "input": " Binarized Neural networks (BNNs) have been shown to be effective in improving network efficiency during the inference phase, after the network has been trained. However, BNNs only binarize the model parameters and activations during propagations. Therefore, BNNs do not offer significant efficiency improvements during training, since the gradients are still propagated and used with high precision. \n \n We show there is no inherent difficulty in training BNNs using \"Binarized BackPropagation\" (BBP), in which we also binarize the gradients. To avoid significant degradation in test accuracy, we simply increase the number of filter maps in a each convolution layer. Using BBP on dedicated hardware can potentially significantly improve the execution efficiency (\\emph{e.g.}, reduce dynamic memory footprint, memory bandwidth and computational energy) and speed up the training process with an appropriate hardware support, even after such an increase in network size. Moreover, our method is ideal for distributed learning as it reduces the communication costs significantly (e.g., by ~32). Using this method, we demonstrate a minimal loss in classification accuracy on several datasets and topologies.", "output": [ "Binarized Back-Propagation all you need for completely binarized training is to is to inflate the size of the network" ] }, { "id": "task668-1b6de7d21b614634a27c518fe8516d32", "input": "A large number of natural language processing tasks exist to analyze syntax, semantics, and information content of human language. These seemingly very different tasks are usually solved by specially designed architectures. In this paper, we provide the simple insight that a great variety of tasks can be represented in a single unified format consisting of labeling spans and relations between spans, thus a single task-independent model can be used across different tasks. We perform extensive experiments to test this insight on 10 disparate tasks as broad as dependency parsing (syntax), semantic role labeling (semantics), relation extraction (information content), aspect based sentiment analysis (sentiment), and many others, achieving comparable performance as state-of-the-art specialized models. We further demonstrate benefits in multi-task learning. We convert these datasets into a unified format to build a benchmark, which provides a holistic testbed for evaluating future models for generalized natural language analysis.", "output": [ "We use a single model to solve a great variety of natural language analysis tasks by formulating them in a unified span-relation format.", "This paper generalizes a wide range of natural language processing tasks as a single span-based framework and proposes a general architecture to solve all these problems.", "This work presents a unified formulation of various phrase and token level NLP tasks." ] }, { "id": "task668-fa857757e6e441b39498278ba473f9c2", "input": "Stereo matching is one of the important basic tasks in the computer vision field. In recent years, stereo matching algorithms based on deep learning have achieved excellent performance and become the mainstream research direction. Existing algorithms generally use deep convolutional neural networks (DCNNs) to extract more abstract semantic information, but we believe that the detailed information of the spatial structure is more important for stereo matching tasks. Based on this point of view, this paper proposes a shallow feature extraction network with a large receptive field. The network consists of three parts: a primary feature extraction module, an atrous spatial pyramid pooling (ASPP) module and a feature fusion module. The primary feature extraction network contains only three convolution layers. This network utilizes the basic feature extraction ability of the shallow network to extract and retain the detailed information of the spatial structure. In this paper, the dilated convolution and atrous spatial pyramid pooling (ASPP) module is introduced to increase the size of receptive field. In addition, a feature fusion module is designed, which integrates the feature maps with multiscale receptive fields and mutually complements the feature information of different scales. We replaced the feature extraction part of the existing stereo matching algorithms with our shallow feature extraction network, and achieved state-of-the-art performance on the KITTI 2015 dataset. Compared with the reference network, the number of parameters is reduced by 42%, and the matching accuracy is improved by 1.9%.", "output": [ "We introduced a shallow featrue extraction network with a large receptive field for stereo matching tasks, which uses a simple structure to get better performance." ] }, { "id": "task668-2e5f74f240764fe4bc302335e66443f9", "input": "Bayesian neural networks, which both use the negative log-likelihood loss function and average their predictions using a learned posterior over the parameters, have been used successfully across many scientific fields, partly due to their ability to `effortlessly' extract desired representations from many large-scale datasets. However, generalization bounds for this setting is still missing.\n In this paper, we present a new PAC-Bayesian generalization bound for the negative log-likelihood loss which utilizes the \\emph{Herbst Argument} for the log-Sobolev inequality to bound the moment generating function of the learners risk.", "output": [ "We derive a new PAC-Bayesian Bound for unbounded loss functions (e.g. Negative Log-Likelihood). " ] }, { "id": "task668-cc3e640d90e74d01a7fb60abe6936586", "input": "Natural language processing (NLP) models often require a massive number of parameters for word embeddings, resulting in a large storage or memory footprint. Deploying neural NLP models to mobile devices requires compressing the word embeddings without any significant sacrifices in performance. For this purpose, we propose to construct the embeddings with few basis vectors. For each word, the composition of basis vectors is determined by a hash code. To maximize the compression rate, we adopt the multi-codebook quantization approach instead of binary coding scheme. Each code is composed of multiple discrete numbers, such as (3, 2, 1, 8), where the value of each component is limited to a fixed range. We propose to directly learn the discrete codes in an end-to-end neural network by applying the Gumbel-softmax trick. Experiments show the compression rate achieves 98% in a sentiment analysis task and 94% ~ 99% in machine translation tasks without performance loss. In both tasks, the proposed method can improve the model performance by slightly lowering the compression rate. Compared to other approaches such as character-level segmentation, the proposed method is language-independent and does not require modifications to the network architecture.", "output": [ "Compressing the word embeddings over 94% without hurting the performance." ] }, { "id": "task668-6748bceaebb7443faf1d60499c2fc9dc", "input": "Capturing high-level structure in audio waveforms is challenging because a single second of audio spans tens of thousands of timesteps. While long-range dependencies are difficult to model directly in the time domain, we show that they can be more tractably modelled in two-dimensional time-frequency representations such as spectrograms. By leveraging this representational advantage, in conjunction with a highly expressive probabilistic model and a multiscale generation procedure, we design a model capable of generating high-fidelity audio samples which capture structure at timescales which time-domain models have yet to achieve. We demonstrate that our model captures longer-range dependencies than time-domain models such as WaveNet across a diverse set of unconditional generation tasks, including single-speaker speech generation, multi-speaker speech generation, and music generation.", "output": [ "We introduce an autoregressive generative model for spectrograms and demonstrate applications to speech and music generation" ] }, { "id": "task668-f6a0b3949db6471f8d62bc8926995079", "input": "In this paper, we propose a new loss function for performing principal component analysis (PCA) using linear autoencoders (LAEs). Optimizing the standard L2 loss results in a decoder matrix that spans the principal subspace of the sample covariance of the data, but fails to identify the exact eigenvectors. This downside originates from an invariance that cancels out in the global map. Here, we prove that our loss function eliminates this issue, i.e. the decoder converges to the exact ordered unnormalized eigenvectors of the sample covariance matrix. For this new loss, we establish that all local minima are global optima and also show that computing the new loss (and also its gradients) has the same order of complexity as the classical loss. We report numerical results on both synthetic simulations, and a real-data PCA experiment on MNIST (i.e., a 60,000 x784 matrix), demonstrating our approach to be practically applicable and rectify previous LAEs' downsides.", "output": [ "A new loss function for PCA with linear autoencoders that provably yields ordered exact eigenvectors " ] }, { "id": "task668-3f5612fbaf2144ff9084a0dac9eca804", "input": "Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware. In this paper we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP. Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice.", "output": [ "We quantify the energy cost in terms of money (cloud credits) and carbon footprint of training recently successful neural network models for NLP. Costs are high." ] }, { "id": "task668-2304a80fef294402831e4bb0727c6cb6", "input": "The concepts of unitary evolution matrices and associative memory have boosted the field of Recurrent Neural Networks (RNN) to state-of-the-art performance in a variety of sequential tasks. However, RNN still has a limited capacity to manipulate long-term memory. To bypass this weakness the most successful applications of RNN use external techniques such as attention mechanisms. In this paper we propose a novel RNN model that unifies the state-of-the-art approaches: Rotational Unit of Memory (RUM). The core of RUM is its rotational operation, which is, naturally, a unitary matrix, providing architectures with the power to learn long-term dependencies by overcoming the vanishing and exploding gradients problem. Moreover, the rotational unit also serves as associative memory. We evaluate our model on synthetic memorization, question answering and language modeling tasks. RUM learns the Copying Memory task completely and improves the state-of-the-art result in the Recall task. RUM’s performance in the bAbI Question Answering task is comparable to that of models with attention mechanism. We also improve the state-of-the-art result to 1.189 bits-per-character (BPC) loss in the Character Level Penn Treebank (PTB) task, which is to signify the applications of RUM to real-world sequential data. The universality of our construction, at the core of RNN, establishes RUM as a promising approach to language modeling, speech recognition and machine translation.", "output": [ "A novel RNN model which outperforms significantly the current frontier of models in a variety of sequential tasks." ] }, { "id": "task668-099fa99a06e24ed189eaf2e2947a7db2", "input": "This work studies the problem of modeling non-linear visual processes by leveraging deep generative architectures for learning linear, Gaussian models of observed sequences. We propose a joint learning framework, combining a multivariate autoregressive model and deep convolutional generative networks. After justification of theoretical assumptions of inearization, we propose an architecture that allows Variational Autoencoders and Generative Adversarial Networks to simultaneously learn the non-linear observation as well as the linear state-transition model from a sequence of observed frames. Finally, we demonstrate our approach on conceptual toy examples and dynamic textures.", "output": [ "We model non-linear visual processes as autoregressive noise via generative deep learning.", "Proposes a new method that models non-linear visual process with a deep version of a linear process (Markov process).", "This paper proposes a new deep generative model for sequences, particularly image sequences and video, which uses a linear structure in part of the model." ] }, { "id": "task668-9e9bf2a97d5e43a2ad990ac7ece164d2", "input": "We propose a model that is able to perform physical parameter estimation of systems from video, where the differential equations governing the scene dynamics are known, but labeled states or objects are not available. Existing physical scene understanding methods require either object state supervision, or do not integrate with differentiable physics to learn interpretable system parameters and states. We address this problem through a \\textit{physics-as-inverse-graphics} approach that brings together vision-as-inverse-graphics and differentiable physics engines, where objects and explicit state and velocity representations are discovered by the model. This framework allows us to perform long term extrapolative video prediction, as well as vision-based model-predictive control. Our approach significantly outperforms related unsupervised methods in long-term future frame prediction of systems with interacting objects (such as ball-spring or 3-body gravitational systems), due to its ability to build dynamics into the model as an inductive bias. We further show the value of this tight vision-physics integration by demonstrating data-efficient learning of vision-actuated model-based control for a pendulum system. We also show that the controller's interpretability provides unique capabilities in goal-driven control and physical reasoning for zero-data adaptation.", "output": [ "We propose a model that is able to perform physical parameter estimation of systems from video, where the differential equations governing the scene dynamics are known, but labeled states or objects are not available." ] }, { "id": "task668-f6470f06485f449bac286e0911504ea0", "input": "Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret. Especially, little is known about how they represent language in their intermediate layers. In an attempt to understand the representations of deep convolutional networks trained on language tasks, we show that individual units are selectively responsive to specific morphemes, words, and phrases, rather than responding to arbitrary and uninterpretable patterns. In order to quantitatively analyze such intriguing phenomenon, we propose a concept alignment method based on how units respond to replicated text. We conduct analyses with different architectures on multiple datasets for classification and translation tasks and provide new insights into how deep models understand natural language.", "output": [ "We show that individual units in CNN representations learned in NLP tasks are selectively responsive to specific natural language concepts.", "Uses grammatical units of natural language that preserve meanings to show that the units of deep CNNs learned in NLP tasks could act as a natural language concept detector." ] }, { "id": "task668-d907099228f64234870d3afb5c9d2443", "input": "Existing unsupervised video-to-video translation methods fail to produce translated videos which are frame-wise realistic, semantic information preserving and video-level consistent. In this work, we propose a novel unsupervised video-to-video translation model. Our model decomposes the style and the content, uses specialized encoder-decoder structure and propagates the inter-frame information through bidirectional recurrent neural network (RNN) units. The style-content decomposition mechanism enables us to achieve long-term style-consistent video translation results as well as provides us with a good interface for modality flexible translation. In addition, by changing the input frames and style codes incorporated in our translation, we propose a video interpolation loss, which captures temporal information within the sequence to train our building blocks in a self-supervised manner. Our model can produce photo-realistic, spatio-temporal consistent translated videos in a multimodal way. Subjective and objective experimental results validate the superiority of our model over the existing methods.", "output": [ "A temporally consistent and modality flexible unsupervised video-to-video translation framework trained in a self-supervised manner." ] }, { "id": "task668-eec116379f124d72b54df58badf14c8e", "input": "This paper presents preliminary ideas of our work for auto- mated learning of Hierarchical Goal Networks in nondeter- ministic domains. We are currently implementing the ideas expressed in this paper.", "output": [ "Learning HGNs, ND domains" ] }, { "id": "task668-179f4590e0f1455eaf34814097933c3a", "input": "Deep Neural Networks (DNNs) thrive in recent years in which Batch Normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the reduction operations. In this paper, we propose alleviating the BN’s cost by using only a small fraction of data for mean & variance estimation at each iteration. The key challenge to reach this goal is how to achieve a satisfactory balance between normalization effectiveness and execution efficiency. We identify that the effectiveness expects less data correlation while the efficiency expects regular execution pattern. To this end, we propose two categories of approach: sampling or creating few uncorrelated data for statistics’ estimation with certain strategy constraints. The former includes “Batch Sampling (BS)” that randomly selects few samples from each batch and “Feature Sampling (FS)” that randomly selects a small patch from each feature map of all samples, and the latter is “Virtual Dataset Normalization (VDN)” that generates few synthetic random samples. Accordingly, multi-way strategies are designed to reduce the data correlation for accurate estimation and optimize the execution pattern for running acceleration in the meantime. All the proposed methods are comprehensively evaluated on various DNN models, where an overall training speedup by up to 21.7% on modern GPUs can be practically achieved without the support of any specialized libraries, and the loss of model accuracy and convergence rate are negligible. Furthermore, our methods demonstrate powerful performance when solving the well-known “micro-batch normalization” problem in the case of tiny batch size.", "output": [ "We propose accelerating Batch Normalization (BN) through sampling less correlated data for reduction operations with regular execution pattern, which achieves up to 2x and 20% speedup for BN itself and the overall training, respectively." ] }, { "id": "task668-814c0efad96b45fd89a1b385ab86e5fa", "input": "This paper presents a novel two-step approach for the fundamental problem of learning an optimal map from one distribution to another. First, we learn an optimal transport (OT) plan, which can be thought as a one-to-many map between the two distributions. To that end, we propose a stochastic dual approach of regularized OT, and show empirically that it scales better than a recent related approach when the amount of samples is very large. Second, we estimate a Monge map as a deep neural network learned by approximating the barycentric projection of the previously-obtained OT plan. This parameterization allows generalization of the mapping outside the support of the input measure. We prove two theoretical stability results of regularized OT which show that our estimations converge to the OT and Monge map between the underlying continuous measures. We showcase our proposed approach on two applications: domain adaptation and generative modeling.", "output": [ "Learning optimal mapping with deepNN between distributions along with theoretical guarantees." ] }, { "id": "task668-bbd1d2c57a194338b9792bf95145d667", "input": "There has recently been a heated debate (e.g. Schwartz-Ziv & Tishby (2017), Saxe et al. (2018), Noshad et al. (2018), Goldfeld et al. (2018)) about measuring the information flow in Deep Neural Networks using techniques from information theory. It is claimed that Deep Neural Networks in general have good generalization capabilities since they not only learn how to map from an input to an output but also how to compress information about the training data input (Schwartz-Ziv & Tishby, 2017). That is, they abstract the input information and strip down any unnecessary or over-specific information. If so, the message compression method, Information Bottleneck (IB), could be used as a natural comparator for network performance, since this method gives an optimal information compression boundary. This claim was then later denounced as well as reaffirmed (e.g. Saxe et al. (2018), Achille et al. (2017), Noshad et al. (2018)), as the employed method of mutual information measuring is not actually measuring information but clustering of the internal layer representations (Goldfeld et al. (2018)). In this paper, we will present a detailed explanation of the development in the Information Plain (IP), which is a plot-type that compares mutual information to judge compression (Schwartz-Ziv & Tishby (2017)), when noise is retroactively added (using binning estimation). We also explain why different activation functions show different trajectories on the IP. Further, we have looked into the effect of clustering on the network loss through early and perfect stopping using the Information Plane and how clustering can be used to help network pruning.", "output": [ "We give a detailed explanation of the trajectories in the information plane and investigate its usage for neural network design (pruning)" ] }, { "id": "task668-1f082a8055684fb4ae73d0d4c47b29f0", "input": "Neural Tangents is a library for working with infinite-width neural networks. It provides a high-level API for specifying complex and hierarchical neural network architectures. These networks can then be trained and evaluated either at finite-width as usual, or in their infinite-width limit. For the infinite-width networks, Neural Tangents performs exact inference either via Bayes' rule or gradient descent, and generates the corresponding Neural Network Gaussian Process and Neural Tangent kernels. Additionally, Neural Tangents provides tools to study gradient descent training dynamics of wide but finite networks. \n\n The entire library runs out-of-the-box on CPU, GPU, or TPU. All computations can be automatically distributed over multiple accelerators with near-linear scaling in the number of devices. \n\n In addition to the repository below, we provide an accompanying interactive Colab notebook at\n https://colab.sandbox.google.com/github/google/neural-tangents/blob/master/notebooks/neural_tangents_cookbook.ipynb\n", "output": [ "Keras for infinite neural networks." ] }, { "id": "task668-b9fbb0609f4e4b92942480ec02b70eb3", "input": "Techniques such as ensembling and distillation promise model quality improvements when paired with almost any base model. However, due to increased test-time cost (for ensembles) and increased complexity of the training pipeline (for distillation), these techniques are challenging to use in industrial settings. In this paper we explore a variant of distillation which is relatively straightforward to use as it does not require a complicated multi-stage setup or many new hyperparameters. Our first claim is that online distillation enables us to use extra parallelism to fit very large datasets about twice as fast. Crucially, we can still speed up training even after we have already reached the point at which additional parallelism provides no benefit for synchronous or asynchronous stochastic gradient descent. Two neural networks trained on disjoint subsets of the data can share knowledge by encouraging each model to agree with the predictions the other model would have made. These predictions can come from a stale version of the other model so they can be safely computed using weights that only rarely get transmitted. Our second claim is that online distillation is a cost-effective way to make the exact predictions of a model dramatically more reproducible. We support our claims using experiments on the Criteo Display Ad Challenge dataset, ImageNet, and the largest to-date dataset used for neural language modeling, containing $6\\times 10^{11}$ tokens and based on the Common Crawl repository of web data.", "output": [ "We perform large scale experiments to show that a simple online variant of distillation can help us scale distributed neural network training to more machines.", "Proposes a method to scale distributed training beyond the current limits of mini-batch stochastic gradient descent", "Proposal for an online distillation method called co-distillation, applied at scale, where two different models are trained to match predictions of the other model in addition to minimizing its own loss.", "Online distillation technique is introduced to accelerate traditional algorithms for large-scaled distributed neural network training" ] }, { "id": "task668-50409a9c6f2c4a5e96ba6341f049f5d3", "input": "The use of deep learning models as priors for compressive sensing tasks presents new potential for inexpensive seismic data acquisition. An appropriately designed Wasserstein generative adversarial network is designed based on a generative adversarial network architecture trained on several historical surveys, capable of learning the statistical properties of the seismic wavelets. The usage of validating and performance testing of compressive sensing are three steps. First, the existence of a sparse representation with different compression rates for seismic surveys is studied. Then, non-uniform samplings are studied, using the proposed methodology. Finally, recommendations for non-uniform seismic survey grid, based on the evaluation of reconstructed seismic images and metrics, is proposed. The primary goal of the proposed deep learning model is to provide the foundations of an optimal design for seismic acquisition, with less loss in imaging quality. Along these lines, a compressive sensing design of a non-uniform grid over an asset in Gulf of Mexico, versus a traditional seismic survey grid which collects data uniformly at every few feet, is suggested, leveraging the proposed method.", "output": [ "Improved a GAN based pixel inpainting network for compressed seismic image recovery andproposed a non-uniform sampling survey recommendatio, which can be easily applied to medical and other domains for compressive sensing technique." ] }, { "id": "task668-28879398760b42f5a0f3259462c96417", "input": "Generative neural networks map a standard, possibly distribution to a complex high-dimensional distribution, which represents the real world data set. However, a determinate input distribution as well as a specific architecture of neural networks may impose limitations on capturing the diversity in the high dimensional target space. To resolve this difficulty, we propose a training framework that greedily produce a series of generative adversarial networks that incrementally capture the diversity of the target space. We show theoretically and empirically that our training algorithm converges to the theoretically optimal distribution, the projection of the real distribution onto the convex hull of the network's distribution space.", "output": [ "We propose a new method to incrementally train a mixture generative model to approximate the information projection of the real data distribution." ] }, { "id": "task668-d2b2c1264b4a4900bc66ac1b79c80abe", "input": "In this paper, I discuss some varieties of explanation that can arise\n in intelligent agents. I distinguish between process accounts, which\n address the detailed decisions made during heuristic search, and\n preference accounts, which clarify the ordering of alternatives\n independent of how they were generated. I also hypothesize \n which types of users will appreciate which types of explanation.\n In addition, I discuss three facets of multi-step decision making\n -- conceptual inference, plan generation, and plan execution --\n in which explanations can arise. I also consider alternative ways\n to present questions to agents and for them provide their answers.\n", "output": [ "This position paper analyzes different types of self explanation that can arise in planning and related systems. ", "Discusses different aspects of explanations, particularly in the context of sequential decision making. " ] }, { "id": "task668-bcb3bd1ddcde4a31bec36f505ebf5d46", "input": "We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and ERL2. Results are presented on a novel environment we call 'Krazy World' and a set of maze environments. We show E-MAML and ERL2 deliver better performance on tasks where exploration is important.", "output": [ "Modifications to MAML and RL2 that should allow for better exploration. ", "The paper proposes a trick of extending objective functions to drive exploration in meta-RL on top of two recent meta-RL algorithms" ] }, { "id": "task668-266d6d41e32d48c18a3a10df58b91aaa", "input": "Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models. We propose Mahé, a novel approach to provide Model-Agnostic Hierarchical Explanations of how powerful machine learning models, such as deep neural networks, capture these interactions as either dependent on or free of the context of data instances. Specifically, Mahé provides context-dependent explanations by a novel local interpretation algorithm that effectively captures any-order interactions, and obtains context-free explanations through generalizing context-dependent interactions to explain global behaviors. Experimental results show that Mahé obtains improved local interaction interpretations over state-of-the-art methods and successfully provides explanations of interactions that are context-free.", "output": [ "A new framework for context-dependent and context-free explanations of predictions", "The authors extend the linear local attribution method LIME for interpreting black box models, and propose a method to discern between context-dependent and context-free interactions.", "A method that can provide hierarchical explanations for a model, including both context-dependent and context-free explanations by a local interpretation algorithm." ] }, { "id": "task668-f2c6973db5614e00bba49abf23e194af", "input": "Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment. This is a critical shortcoming for applying RL to real-world problems where collecting data is expensive, and models must be tested offline before being deployed to interact with the environment -- e.g. systems that learn from human interaction. Thus, we develop a novel class of off-policy batch RL algorithms which use KL-control to penalize divergence from a pre-trained prior model of probable actions. This KL-constraint reduces extrapolation error, enabling effective offline learning, without exploration, from a fixed batch of data. We also use dropout-based uncertainty estimates to lower bound the target Q-values as a more efficient alternative to Double Q-Learning. This Way Off-Policy (WOP) algorithm is tested on both traditional RL tasks from OpenAI Gym, and on the problem of open-domain dialog generation; a challenging reinforcement learning problem with a 20,000 dimensional action space. WOP allows for the extraction of multiple different reward functions post-hoc from collected human interaction data, and can learn effectively from all of these. We test real-world generalization by deploying dialog models live to converse with humans in an open-domain setting, and demonstrate that WOP achieves significant improvements over state-of-the-art prior methods in batch deep RL.\n", "output": [ "We show that KL-control from a pre-trained prior can allow RL models to learn from a static batch of collected data, without the ability to explore online in the environment." ] }, { "id": "task668-dcf4abb3fae04a979ead8c8df9d019e3", "input": "Despite remarkable empirical success, the training dynamics of generative adversarial networks (GAN), which involves solving a minimax game using stochastic gradients, is still poorly understood. In this work, we analyze last-iterate convergence of simultaneous gradient descent (simGD) and its variants under the assumption of convex-concavity, guided by a continuous-time analysis with differential equations. First, we show that simGD, as is, converges with stochastic sub-gradients under strict convexity in the primal variable. Second, we generalize optimistic simGD to accommodate an optimism rate separate from the learning rate and show its convergence with full gradients. Finally, we present anchored simGD, a new method, and show convergence with stochastic subgradients.", "output": [ "Convergence proof of stochastic sub-gradients method and variations on convex-concave minimax problems", "An anaysis of simultaneous stochastic subgradient, simultaneous gradient with optimism, and simultaneous gradient with anchoring in the context of minmax convex concave games.", "This paper analyzes the dynamics of stochastic gradient descent when applied to convex-concave games, as well as GD with optimism and a new anchored GD algorithm that converges under weaker assumptions than SGD or SGD with optimism." ] }, { "id": "task668-e0587c800df244cca9b6fc9a82dbda4f", "input": "Neural architecture search (NAS) has achieved breakthrough success in a great number of applications in the past few years.\n It could be time to take a step back and analyze the good and bad aspects in the field of NAS. A variety of algorithms search architectures under different search space. These searched architectures are trained using different setups, e.g., hyper-parameters, data augmentation, regularization. This raises a comparability problem when comparing the performance of various NAS algorithms. NAS-Bench-101 has shown success to alleviate this problem. In this work, we propose an extension to NAS-Bench-101: NAS-Bench-201 with a different search space, results on multiple datasets, and more diagnostic information. NAS-Bench-201 has a fixed search space and provides a unified benchmark for almost any up-to-date NAS algorithms. The design of our search space is inspired by the one used in the most popular cell-based searching algorithms, where a cell is represented as a directed acyclic graph. Each edge here is associated with an operation selected from a predefined operation set. For it to be applicable for all NAS algorithms, the search space defined in NAS-Bench-201 includes all possible architectures generated by 4 nodes and 5 associated operation options, which results in 15,625 neural cell candidates in total. The training log using the same setup and the performance for each architecture candidate are provided for three datasets. This allows researchers to avoid unnecessary repetitive training for selected architecture and focus solely on the search algorithm itself. The training time saved for every architecture also largely improves the efficiency of most NAS algorithms and presents a more computational cost friendly NAS community for a broader range of researchers. We provide additional diagnostic information such as fine-grained loss and accuracy, which can give inspirations to new designs of NAS algorithms. In further support of the proposed NAS-Bench-102, we have analyzed it from many aspects and benchmarked 10 recent NAS algorithms, which verify its applicability.", "output": [ "A NAS benchmark applicable to almost any NAS algorithms." ] }, { "id": "task668-319496a0b6834c859a273f5809e53427", "input": "Key equatorial climate phenomena such as QBO and ENSO have never been adequately explained as deterministic processes. This in spite of recent research showing growing evidence of predictable behavior. This study applies the fundamental Laplace tidal equations with simplifying assumptions along the equator — i.e. no Coriolis force and a small angle approximation. The solutions to the partial differential equations are highly non-linear related to Navier-Stokes and only search approaches can be used to fit to the data.", "output": [ "Analytical Formulation of Equatorial Standing Wave Phenomena: Application to QBO and ENSO" ] }, { "id": "task668-328c339c76444c7f9442ee4798c48b9f", "input": "High performance of deep learning models typically comes at cost of considerable model size and computation time. These factors limit applicability for deployment on memory and battery constraint devices such as mobile phones or embedded systems. In this work we propose a novel pruning technique that eliminates entire filters and neurons according to their relative L1-norm as compared to the rest of the network, yielding more compression and decreased redundancy in the parameters. The resulting network is non-sparse, however, much more compact and requires no special infrastructure for its deployment. We prove the viability of our method by achieving 97.4%, 47.8% and 53% compression of LeNet-5, ResNet-56 and ResNet-110 respectively, exceeding state-of-the-art compression results reported on ResNet without losing any performance compared to the baseline. Our approach does not only exhibit good performance, but is also easy to implement on many architectures.", "output": [ "We propose a novel structured class-blind pruning technique to produce highly compressed neural networks." ] }, { "id": "task668-e6d9fd5995324466b1b747d159e83d04", "input": "Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long term dependencies. In backpropagation through time settings, these issues are tightly coupled with the large, sequential computational graph resulting from unfolding the RNN in time. We introduce the Skip RNN model which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph. This model can also be encouraged to perform fewer state updates through a budget constraint. We evaluate the proposed model on various tasks and show how it can reduce the number of required RNN updates while preserving, and sometimes even improving, the performance of the baseline RNN models. Source code is publicly available at https://imatge-upc.github.io/skiprnn-2017-telecombcn/.", "output": [ "A modification for existing RNN architectures which allows them to skip state updates while preserving the performance of the original architectures.", "Proposes the Skip RNN model which allows a recurrent network to selectively skip updating its hidden state for some inputs, leading to reduced computation at test-time.", "Proposes a novel RNN model where both the input and the state update of the recurrent cells are skipped adaptively for some time steps." ] }, { "id": "task668-0efb162821714dbbac22ed7cb01e3280", "input": "We propose a new representation, one-pixel signature, that can be used to reveal the characteristics of the convolution neural networks (CNNs). Here, each CNN classifier is associated with a signature that is created by generating, pixel-by-pixel, an adversarial value that is the result of the largest change to the class prediction. The one-pixel signature is agnostic to the design choices of CNN architectures such as type, depth, activation function, and how they were trained. It can be computed efficiently for a black-box classifier without accessing the network parameters. Classic networks such as LetNet, VGG, AlexNet, and ResNet demonstrate different characteristics in their signature images. For application, we focus on the classifier backdoor detection problem where a CNN classifier has been maliciously inserted with an unknown Trojan. We show the effectiveness of the one-pixel signature in detecting backdoored CNN. Our proposed one-pixel signature representation is general and it can be applied in problems where discriminative classifiers, particularly neural network based, are to be characterized.", "output": [ "Cnvolutional neural networks characterization for backdoored classifier detection and understanding." ] }, { "id": "task668-3ed9c08a09804e3db4f40ff3cfd21954", "input": "In this paper, we show a phenomenon, which we named ``super-convergence'', where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods. The existence of super-convergence is relevant to understanding why deep networks generalize well. One of the key elements of super-convergence is training with cyclical learning rates and a large maximum learning rate. Furthermore, we present evidence that training with large learning rates improves performance by regularizing the network. In addition, we show that super-convergence provides a greater boost in performance relative to standard training when the amount of labeled training data is limited. We also derive a simplification of the Hessian Free optimization method to compute an estimate of the optimal learning rate. The architectures to replicate this work will be made available upon publication.\n", "output": [ "Empirical proof of a new phenomenon requires new theoretical insights and is relevent to the active discussions in the literature on SGD and understanding generalization.", "The paper discusses a phenomenon where neural network training in very specific settings can profit much from a schedule including large learning rates", "The authors analyze training of residual networks using large cyclic learning rates, and demonstrate fast convergence with cyclic learning rates and evidence of large learning rates acting as regularization." ] }, { "id": "task668-0ae8e5da45154850a4ba530550d0c453", "input": "We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human experts, and an expressive search space that supports complex topologies. Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches. We also present results using random search, achieving 0.3% less top-1 accuracy on CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour.", "output": [ "In this paper we propose a hierarchical architecture representation in which doing random or evolutionary architecture search yields highly competitive results using fewer computational resources than the prior art." ] }, { "id": "task668-ab9f9025d7884fd384e5512eaf0f5755", "input": "Min-max formulations have attracted great attention in the ML community due to the rise of deep generative models and adversarial methods, and understanding the dynamics of (stochastic) gradient algorithms for solving such formulations has been a grand challenge. As a first step, we restrict to bilinear zero-sum games and give a systematic analysis of popular gradient updates, for both simultaneous and alternating versions. We provide exact conditions for their convergence and find the optimal parameter setup and convergence rates. In particular, our results offer formal evidence that alternating updates converge \"better\" than simultaneous ones.", "output": [ "We systematically analyze the convergence behaviour of popular gradient algorithms for solving bilinear games, with both simultaneous and alternating updates." ] }, { "id": "task668-cf6ab77679384e3080e0cf33302aa44a", "input": "Learning an efficient update rule from data that promotes rapid learning of new tasks from the same distribution remains an open problem in meta-learning. Typically, previous works have approached this issue either by attempting to train a neural network that directly produces updates or by attempting to learn better initialisations or scaling factors for a gradient-based update rule. Both these approaches pose challenges. On one hand, directly producing an update forgoes a useful inductive bias and can easily lead to non-converging behaviour. On the other hand, approaches that try to control a gradient-based update rule typically resort to computing gradients through the learning process to obtain their meta-gradients, leading to methods that can not scale beyond few-shot task adaptation. In this work we propose Warped Gradient Descent (WarpGrad), a method that intersects these approaches to mitigate their limitations. WarpGrad meta-learns an efficiently parameterised preconditioning matrix that facilitates gradient descent across the task distribution. Preconditioning arises by interleaving non-linear layers, referred to as warp-layers, between the layers of a task-learner. Warp-layers are meta-learned without backpropagating through the task training process in a manner similar to methods that learn to directly produce updates. WarpGrad is computationally efficient, easy to implement, and can scale to arbitrarily large meta-learning problems. We provide a geometrical interpretation of the approach and evaluate its effectiveness in a variety of settings, including few-shot, standard supervised, continual and reinforcement learning.", "output": [ "We propose a novel framework for meta-learning a gradient-based update rule that scales to beyond few-shot learning and is applicable to any form of learning, including continual learning." ] }, { "id": "task668-7d1983708a5341f6a0d8c55e1b4fbc1d", "input": "A fundamental trait of intelligence is the ability to achieve goals in the face of novel circumstances. In this work, we address one such setting which requires solving a task with a novel set of actions. Empowering machines with this ability requires generalization in the way an agent perceives its available actions along with the way it uses these actions to solve tasks. Hence, we propose a framework to enable generalization over both these aspects: understanding an action’s functionality, and using actions to solve tasks through reinforcement learning. Specifically, an agent interprets an action’s behavior using unsupervised representation learning over a collection of data samples reflecting the diverse properties of that action. We employ a reinforcement learning architecture which works over these action representations, and propose regularization metrics essential for enabling generalization in a policy. We illustrate the generalizability of the representation learning method and policy, to enable zero-shot generalization to previously unseen actions on challenging sequential decision-making environments. Our results and videos can be found at sites.google.com/view/action-generalization/", "output": [ "We address the problem of generalization of reinforcement learning to unseen action spaces." ] }, { "id": "task668-f0e87679db8145618ea0cbfbe024c55f", "input": "We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie - one from Wikipedia and the other from IMDb - written by two different authors. We asked crowdsourced workers to create questions from one version of the plot and a different set of workers to extract or synthesize corresponding answers from the other version. This unique characteristic of DuoRC where questions and answers are created from different versions of a document narrating the same underlying story, ensures by design, that there is very little lexical overlap between the questions created from one version and the segments containing the answer in the other version. Further, since the two versions have different level of plot detail, narration style, vocabulary, etc., answering questions from the second version requires deeper language understanding and incorporating background knowledge not available in the given text. Additionally, the narrative style of passages arising from movie plots (as opposed to typical descriptive passages in existing datasets) exhibits the need to perform complex reasoning over events across multiple sentences. Indeed, we observe that state-of-the-art neural RC models which have achieved near human performance on the SQuAD dataset, even when coupled with traditional NLP techniques to address the challenges presented in DuoRC exhibit very poor performance (F1 score of 37.42% on DuoRC v/s 86% on SQuAD dataset). This opens up several interesting research avenues wherein DuoRC could complement other Reading Comprehension style datasets to explore novel neural approaches for studying language understanding.", "output": [ "We propose DuoRC, a novel dataset for Reading Comprehension (RC) containing 186,089 human-generated QA pairs created from a collection of 7680 pairs of parallel movie plots and introduce a RC task of reading one version of the plot and answering questions created from the other version; thus by design, requiring complex reasoning and deeper language understanding to overcome the poor lexical overlap between the plot and the question." ] }, { "id": "task668-1c4b6aa2c6a1425fb90ba745077233fd", "input": "It is fundamental and challenging to train robust and accurate Deep Neural Networks (DNNs) when semantically abnormal examples exist. Although great progress has been made, there is still one crucial research question which is not thoroughly explored yet: What training examples should be focused and how much more should they be emphasised to achieve robust learning? In this work, we study this question and propose gradient rescaling (GR) to solve it. GR modifies the magnitude of logit vector’s gradient to emphasise on relatively easier training data points when noise becomes more severe, which functions as explicit emphasis regularisation to improve the generalisation performance of DNNs. Apart from regularisation, we connect GR to examples weighting and designing robust loss functions. We empirically demonstrate that GR is highly anomaly-robust and outperforms the state-of-the-art by a large margin, e.g., increasing 7% on CIFAR100 with 40% noisy labels. It is also significantly superior to standard regularisers in both clean and abnormal settings. Furthermore, we present comprehensive ablation studies to explore the behaviours of GR under different cases, which is informative for applying GR in real-world scenarios.", "output": [ "ROBUST DISCRIMINATIVE REPRESENTATION LEARNING VIA GRADIENT RESCALING: AN EMPHASIS REGULARISATION PERSPECTIVE" ] }, { "id": "task668-f24ad5349845404785d69597195d5d6d", "input": "Deep convolutional neural network (DCNN) based supervised learning is a widely practiced approach for large-scale image classification. However, retraining these large networks to accommodate new, previously unseen data demands high computational time and energy requirements. Also, previously seen training samples may not be available at the time of retraining. We propose an efficient training methodology and incrementally growing a DCNN to allow new classes to be learned while sharing part of the base network. Our proposed methodology is inspired by transfer learning techniques, although it does not forget previously learned classes. An updated network for learning new set of classes is formed using previously learned convolutional layers (shared from initial part of base network) with addition of few newly added convolutional kernels included in the later layers of the network. We evaluated the proposed scheme on several recognition applications. The classification accuracy achieved by our approach is comparable to the regular incremental learning approach (where networks are updated with new training samples only, without any network sharing).", "output": [ "The paper is about a new energy-efficient methodology for Incremental learning", "Proposes procedure for incremental learning as transfer learning.", "The paper presents a method to train deep convolutional neural networks in an incremental fashion, in which data are available in small batches over a period of time.", "Presents an approach to class-incremental learning using deep networks by proposing three different learning strategies in the final/best approach." ] }, { "id": "task668-5501dc9ff0bd4bc0aad0a96dca93629f", "input": "We propose that approximate Bayesian algorithms should optimize a new criterion, directly derived from the loss, to calculate their approximate posterior which we refer to as pseudo-posterior. Unlike standard variational inference which optimizes a lower bound on the log marginal likelihood, the new algorithms can be analyzed to provide loss guarantees on the predictions with the pseudo-posterior. Our criterion can be used to derive new sparse Gaussian process algorithms that have error guarantees applicable to various likelihoods.", "output": [ "This paper utilizes the analysis of Lipschitz loss on a bounded hypothesis space to derive new ERM-type algorithms with strong performance guarantees that can be applied to the non-conjugate sparse GP model." ] }, { "id": "task668-70f3533aaa5345a3b3fe94aaad50f615", "input": "This paper addresses the problem of representing a system's belief using multi-variate normal distributions (MND) where the underlying model is based on a deep neural network (DNN). The major challenge with DNNs is the computational complexity that is needed to obtain model uncertainty using MNDs. To achieve a scalable method, we propose a novel approach that expresses the parameter posterior in sparse information form. Our inference algorithm is based on a novel Laplace Approximation scheme, which involves a diagonal correction of the Kronecker-factored eigenbasis. As this makes the inversion of the information matrix intractable - an operation that is required for full Bayesian analysis, we devise a low-rank approximation of this eigenbasis and a memory-efficient sampling scheme. We provide both a theoretical analysis and an empirical evaluation on various benchmark data sets, showing the superiority of our approach over existing methods.", "output": [ "An approximate inference algorithm for deep learning" ] }, { "id": "task668-f1f2019b3af549f4b749b8d4cd0d70c2", "input": "State-of-the-art neural machine translation methods employ massive amounts of parameters. Drastically reducing computational costs of such methods without affecting performance has been up to this point unsolved. In this work, we propose a quantization strategy tailored to the Transformer architecture. We evaluate our method on the WMT14 EN-FR and WMT14 EN-DE translation tasks and achieve state-of-the-art quantization results for the Transformer, obtaining no loss in BLEU scores compared to the non-quantized baseline. We further compress the Transformer by showing that, once the model is trained, a good portion of the nodes in the encoder can be removed without causing any loss in BLEU.", "output": [ "We fully quantize the Transformer to 8-bit and improve translation quality compared to the full precision model.", "An 8-bit quantization method to quantize the machine translation model Transformer, proposing to use uniform min-max quantization during inference and bucketing weigts before quantization to reduce quantization error.", "A method for reducing the required memory space by a quantization technique, focused on reducing it for Transformer architecture." ] }, { "id": "task668-6d0a56d8ece547be9f9490ab3c0ac7c2", "input": "The ground-breaking performance obtained by deep convolutional neural networks (CNNs) for image processing tasks is inspiring research efforts attempting to extend it for 3D geometric tasks. One of the main challenge in applying CNNs to 3D shape analysis is how to define a natural convolution operator on non-euclidean surfaces. In this paper, we present a method for applying deep learning to 3D surfaces using their spherical descriptors and alt-az anisotropic convolution on 2-sphere. A cascade set of geodesic disk filters rotate on the 2-sphere and collect spherical patterns and so to extract geometric features for various 3D shape analysis tasks. We demonstrate theoretically and experimentally that our proposed method has the possibility to bridge the gap between 2D images and 3D shapes with the desired rotation equivariance/invariance, and its effectiveness is evaluated in applications of non-rigid/ rigid shape classification and shape retrieval.", "output": [ "A method for applying deep learning to 3D surfaces using their spherical descriptors and alt-az anisotropic convolution on 2-sphere.", "Presents a polar anisotropic convolution scheme on a unit sphere by replacing filter translation with filter rotation.", "This paper explores deep learning of 3D shapes using alt-az anisotropic 2-sphere convolution" ] }, { "id": "task668-dc26c5a76b124ecd9b5a9bd28947aed3", "input": "While momentum-based methods, in conjunction with the stochastic gradient descent, are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In practice, the momentum parameter is often chosen in a heuristic fashion with little theoretical guidance. In this work, we use the framework of algorithmic stability to provide an upper-bound on the generalization error for the class of strongly convex loss functions, under mild technical assumptions. Our bound decays to zero inversely with the size of the training set, and increases as the momentum parameter is increased. We also develop an upper-bound on the expected true risk, in terms of the number of training steps, the size of the training set, and the momentum parameter.", "output": [ "Stochastic gradient method with momentum generalizes." ] }, { "id": "task668-c69e8165b9544041942f819e786e6412", "input": "Building on the success of deep learning, two modern approaches to learn a probability model of the observed data are Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs). VAEs consider an explicit probability model for the data and compute a generative distribution by maximizing a variational lower-bound on the log-likelihood function. GANs, however, compute a generative model by minimizing a distance between observed and generated probability distributions without considering an explicit model for the observed data. The lack of having explicit probability models in GANs prohibits computation of sample likelihoods in their frameworks and limits their use in statistical inference problems. In this work, we show that an optimal transport GAN with the entropy regularization can be viewed as a generative model that maximizes a lower-bound on average sample likelihoods, an approach that VAEs are based on. In particular, our proof constructs an explicit probability model for GANs that can be used to compute likelihood statistics within GAN's framework. Our numerical results on several datasets demonstrate consistent trends with the proposed theory.", "output": [ "A statistical approach to compute sample likelihoods in Generative Adversarial Networks", "Show that WGAN with entropic regularization maximizes a lower bound on the likelihood of the observed data distribution.", "Authors claim it is possible to leverage the upper bound from an entropy regularized optimal transport to come up with a measure of 'sample likelihood'." ] }, { "id": "task668-ce2973fc1bde442d87057a73a967899c", "input": "Zero-Shot Learning (ZSL) is a classification task where some classes referred as unseen classes have no labeled training images. Instead, we only have side information (or description) about seen and unseen classes, often in the form of semantic or descriptive attributes. Lack of training images from a set of classes restricts the use of standard classification techniques and losses, including the popular cross-entropy loss. The key step in tackling ZSL problem is bridging visual to semantic space via learning a nonlinear embedding. A well established approach is to obtain the semantic representation of the visual information and perform classification in the semantic space. In this paper, we propose a novel architecture of casting ZSL as a fully connected neural-network with cross-entropy loss to embed visual space to semantic space. During training in order to introduce unseen visual information to the network, we utilize soft-labeling based on semantic similarities between seen and unseen classes. To the best of our knowledge, such similarity based soft-labeling is not explored for cross-modal transfer and ZSL. We evaluate the proposed model on five benchmark datasets for zero-shot learning, AwA1, AwA2, aPY, SUN and CUB datasets, and show that, despite the simplicity, our approach achieves the state-of-the-art performance in Generalized-ZSL setting on all of these datasets and outperforms the state-of-the-art for some datasets.", "output": [ "How to use cross-entropy loss for zero shot learning with soft labeling on unseen classes : a simple and effective solution that achieves state-of-the-art performance on five ZSL benchmark datasets." ] }, { "id": "task668-cd65fbfc6e024cb5894b1e871d2c82e3", "input": "Deep learning models are often sensitive to adversarial attacks, where carefully-designed input samples can cause the system to produce incorrect decisions. Here we focus on the problem of detecting attacks, rather than robust classification, since detecting that an attack occurs may be even more important than avoiding misclassification. We build on advances in explainability, where activity-map-like explanations are used to justify and validate decisions, by highlighting features that are involved with a classification decision. The key observation is that it is hard to create explanations for incorrect decisions. We propose EXAID, a novel attack-detection approach, which uses model explainability to identify images whose explanations are inconsistent with the predicted class . Specifically, we use SHAP, which uses Shapley values in the space of the input image, to identify which input features contribute to a class decision . Interestingly, this approach does not require to modify the attacked model, and it can be applied without modelling a specific attack . It can therefore be applied successfully to detect unfamiliar attacks, that were unknown at the time the detection model was designed . We evaluate EXAID on two benchmark datasets CIFAR-10 and SVHN, and against three leading attack techniques, FGSM, PGD and C&W. We find that EXAID improves over the SoTA detection methods by a large margin across a wide range of noise levels, improving detection from 70% to over 90% for small perturbations.", "output": [ "A novel adversarial detection approach, which uses explainability methods to identify images whose explanations are inconsistent with the predicted class. " ] }, { "id": "task668-42f799d4466a4c468838c166718da7f5", "input": "We study a general formulation of program synthesis called syntax-guided synthesis(SyGuS) that concerns synthesizing a program that follows a given grammar and satisfies a given logical specification. Both the logical specification and the grammar have complex structures and can vary from task to task, posing significant challenges for learning across different tasks. Furthermore, training data is often unavailable for domain specific synthesis tasks. To address these challenges, we propose a meta-learning framework that learns a transferable policy from only weak supervision. Our framework consists of three components: 1) an encoder, which embeds both the logical specification and grammar at the same time using a graph neural network; 2) a grammar adaptive policy network which enables learning a transferable policy; and 3) a reinforcement learning algorithm that jointly trains the embedding and adaptive policy. We evaluate the framework on 214 cryptographic circuit synthesis tasks. It solves 141 of them in the out-of-box solver setting, significantly outperforming a similar search-based approach but without learning, which solves only 31. The result is comparable to two state-of-the-art classical synthesis engines, which solve 129 and 153 respectively. In the meta-solver setting, the framework can efficiently adapt to unseen tasks and achieves speedup ranging from 2x up to 100x.", "output": [ "We propose a meta-learning framework that learns a transferable policy from only weak supervision to solve synthesis tasks with different logical specifications and grammars." ] }, { "id": "task668-270916e868934f0ab12a46b847f682da", "input": "Unsupervised domain adaptation aims to generalize the hypothesis trained in a source domain to an unlabeled target domain. One popular approach to this problem is to learn domain-invariant embeddings for both domains. In this work, we study, theoretically and empirically, the effect of the embedding complexity on generalization to the target domain. In particular, this complexity affects an upper bound on the target risk; this is reflected in experiments, too. Next, we specify our theoretical framework to multilayer neural networks. As a result, we develop a strategy that mitigates sensitivity to the embedding complexity, and empirically achieves performance on par with or better than the best layer-dependent complexity tradeoff.", "output": [ "We study the effect of the embedding complexity in learning domain-invariant representations and develop a strategy that mitigates sensitivity to it." ] }, { "id": "task668-e1cf3f83bd34435ea17bf3c098e0a879", "input": "This paper studies \\emph{model inversion attacks}, in which the access to a model is abused to infer information about the training data. Since its first introduction by~\\citet{fredrikson2014privacy}, such attacks have raised serious concerns given that training data usually contain sensitive information. Thus far, successful model inversion attacks have only been demonstrated on simple models, such as linear regression and logistic regression. Previous attempts to invert neural networks, even the ones with simple architectures, have failed to produce convincing results. We present a novel attack method, termed the \\emph{generative model inversion attack}, which can invert deep neural networks with high success rates. Rather than reconstructing private training data from scratch, we leverage partial public information, which can be very generic, to learn a distributional prior via generative adversarial networks (GANs) and use it to guide the inversion process. Moreover, we theoretically prove that a model's predictive power and its vulnerability to inversion attacks are indeed two sides of the same coin---highly predictive models are able to establish a strong correlation between features and labels, which coincides exactly with what an adversary exploits to mount the attacks.\n Our experiments demonstrate that the proposed attack improves identification accuracy over the existing work by about $75\\%$ for reconstructing face images from a state-of-the-art face recognition classifier. We also show that differential privacy, in its canonical form, is of little avail to protect against our attacks.", "output": [ "We develop a privacy attack that can recover the sensitive input data of a deep net from its output" ] }, { "id": "task668-a54f116506e8464db82de6f482d9a069", "input": "Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of a standard normal prior in weight space imposes only weak regularities, causing the function posterior to possibly generalize in unforeseen ways on inputs outside of the training distribution. We propose noise contrastive priors (NCPs) to obtain reliable uncertainty estimates. The key idea is to train the model to output high uncertainty for data points outside of the training distribution. NCPs do so using an input prior, which adds noise to the inputs of the current mini batch, and an output prior, which is a wide distribution given these inputs. NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training. Empirically, we show that NCPs prevent overfitting outside of the training distribution and result in uncertainty estimates that are useful for active learning. We demonstrate the scalability of our method on the flight delays data set, where we significantly improve upon previously published results.", "output": [ "We train neural networks to be uncertain on noisy inputs to avoid overconfident predictions outside of the training distribution.", "Presents an approach to obtain uncertainty estimates for neural network predictions that has good performance when quantifying predictive uncertainty at points that are outside of the training distribution.", "The paper considers the problem of uncertainty estimation of neural networks and proposes to use Bayesian approach with noice contrastive prior" ] }, { "id": "task668-a05128b977c24a91a5c98d55d4de21c4", "input": "Recent work in network quantization has substantially reduced the time and space complexity of neural network inference, enabling their deployment on embedded and mobile devices with limited computational and memory resources. However, existing quantization methods often represent all weights and activations with the same precision (bit-width). In this paper, we explore a new dimension of the design space: quantizing different layers with different bit-widths. We formulate this problem as a neural architecture search problem and propose a novel differentiable neural architecture search (DNAS) framework to efficiently explore its exponential search space with gradient-based optimization. Experiments show we surpass the state-of-the-art compression of ResNet on CIFAR-10 and ImageNet. Our quantized models with 21.1x smaller model size or 103.9x lower computational cost can still outperform baseline quantized or even full precision models.", "output": [ "A novel differentiable neural architecture search framework for mixed quantization of ConvNets.", "The authors introduce a new method for neural architecture search which selects the precision quantization of weights at each neural network layer, and use it in the context of network compression.", "The paper presents a new approach in network quantization by quantizing different layers with different bit-widths and introduces a new differentiable neural architecture search framework." ] }, { "id": "task668-3e5ffa9acb0e440e836ae7c13a07a080", "input": "We introduce an attention mechanism to improve feature extraction for deep active learning (AL) in the semi-supervised setting. The proposed attention mechanism is based on recent methods to visually explain predictions made by DNNs. We apply the proposed explanation-based attention to MNIST and SVHN classification. The conducted experiments show accuracy improvements for the original and class-imbalanced datasets with the same number of training examples and faster long-tail convergence compared to uncertainty-based methods.", "output": [ "We introduce an attention mechanism to improve feature extraction for deep active learning (AL) in the semi-supervised setting." ] }, { "id": "task668-fed4298f51b74ae183acda6bcea088a0", "input": "Search engine users nowadays heavily depend on query completion and correction to shape their queries. Typically, the completion is done by database lookup which does not understand the context and cannot generalize to prefixes not in the database . In the paper, we propose to use unsupervised deep language models to complete and correct the queries given an arbitrary prefix . We show how to address two main challenges that renders this method practical for large-scale deployment : 1) we propose a method for integrating error correction into the language model completion via a edit-distance potential and a variant of beam search that can exploit these potential functions; and 2) we show how to efficiently perform CPU-based computation to complete the queries, with error correction, in real time (generating top 10 completions within 16 ms). Experiments show that the method substantially increases hit rate over standard approaches, and is capable of handling tail queries.\n", "output": [ "realtime search query completion using character-level LSTM language models", "This paper presents methods for query completion that includes prefix correction, and some engineering details to meet particular latency requirements on a CPU.", "The authors propose an algorithm for solving the query completion problem with error correction, and adopt character-level RNN-based modeling and optimize the inference part to achieve targets in real time." ] }, { "id": "task668-c33323b268de4b489054db0cfa584489", "input": "Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results.\n Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. \n In this paper, we propose AdvGAN to generate adversarial examples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. \n For AdvGAN, once the generator is trained, it can generate adversarial perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. \n We apply AdvGAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly.\n Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.", "output": [ "We propose to generate adversarial example based on generative adversarial networks in a semi-whitebox and black-box settings.", "Describes AdvGAN, a conditional GAN plus adversarial loss, and evaluates AdvGAN on semi-white box and black box setting, reporting state-of-art results.", "This paper proposes a way of generating adversarial examples that fool classification systems and wins MadryLab's mnist challenge." ] }, { "id": "task668-da41e753a9e3490783c4a3755a1dc9c6", "input": "Predictive models that generalize well under distributional shift are often desirable and sometimes crucial to machine learning applications. One example is the estimation of treatment effects from observational data, where a subtask is to predict the effect of a treatment on subjects that are systematically different from those who received the treatment in the data. A related kind of distributional shift appears in unsupervised domain adaptation, where we are tasked with generalizing to a distribution of inputs that is different from the one in which we observe labels. We pose both of these problems as prediction under a shift in design. Popular methods for overcoming distributional shift are often heuristic or rely on assumptions that are rarely true in practice, such as having a well-specified model or knowing the policy that gave rise to the observed data. Other methods are hindered by their need for a pre-specified metric for comparing observations, or by poor asymptotic properties. In this work, we devise a bound on the generalization error under design shift, based on integral probability metrics and sample re-weighting. We combine this idea with representation learning, generalizing and tightening existing results in this space. Finally, we propose an algorithmic framework inspired by our bound and verify is effectiveness in causal effect estimation.", "output": [ "A theory and algorithmic framework for prediction under distributional shift, including causal effect estimation and domain adaptation" ] }, { "id": "task668-51c23c223bb14642952d6a9bed97a75d", "input": "An important problem that arises in reinforcement learning and Monte Carlo methods is estimating quantities defined by the stationary distribution of a Markov chain. In many real-world applications, access to the underlying transition operator is limited to a fixed set of data that has already been collected, without additional interaction with the environment being available. We show that consistent estimation remains possible in this scenario, and that effective estimation can still be achieved in important applications. Our approach is based on estimating a ratio that corrects for the discrepancy between the stationary and empirical distributions, derived from fundamental properties of the stationary distribution, and exploiting constraint reformulations based on variational divergence minimization. The resulting algorithm, GenDICE, is straightforward and effective. We prove the consistency of the method under general conditions, provide a detailed error analysis, and demonstrate strong empirical performance on benchmark tasks, including off-line PageRank and off-policy policy evaluation.", "output": [ "In this paper, we proposed a novel algorithm, GenDICE, for general stationary distribution correction estimation, which can handle both discounted and average off-policy evaluation on multiple behavior-agnostic samples." ] }, { "id": "task668-957d4ef24ba54e37b667049e3fd81dbf", "input": "Training methods for deep networks are primarily variants on stochastic gradient descent. Techniques that use (approximate) second-order information are rarely used because of the computational cost and noise associated with those approaches in deep learning contexts. However, in this paper, we show how feedforward deep networks exhibit a low-rank derivative structure. This low-rank structure makes it possible to use second-order information without needing approximations and without incurring a significantly greater computational cost than gradient descent. To demonstrate this capability, we implement Cubic Regularization (CR) on a feedforward deep network with stochastic gradient descent and two of its variants. There, we use CR to calculate learning rates on a per-iteration basis while training on the MNIST and CIFAR-10 datasets. CR proved particularly successful in escaping plateau regions of the objective function. We also found that this approach requires less problem-specific information (e.g. an optimal initial learning rate) than other first-order methods in order to perform well.", "output": [ "We show that deep learning network derivatives have a low-rank structure, and this structure allows us to use second-order derivative information to calculate learning rates adaptively and in a computationally feasible manner." ] }, { "id": "task668-31b058f773904100a5edb26a5fbf2aac", "input": "Deep neural networks (DNNs) dominate current research in machine learning. Due to massive GPU parallelization DNN training is no longer a bottleneck, and large models with many parameters and high computational effort lead common benchmark tables. In contrast, embedded devices have a very limited capability. As a result, both model size and inference time must be significantly reduced if DNNs are to achieve suitable performance on embedded devices.\n We propose a soft quantization approach to train DNNs that can be evaluated using pure fixed-point arithmetic. By exploiting the bit-shift mechanism, we derive fixed-point quantization constraints for all important components, including batch normalization and ReLU. Compared to floating-point arithmetic, fixed-point calculations significantly reduce computational effort whereas low-bit representations immediately decrease memory costs. We evaluate our approach with different architectures on common benchmark data sets and compare with recent quantization approaches. We achieve new state of the art performance using 4-bit fixed-point models with an error rate of 4.98% on CIFAR-10.", "output": [ "Soft quantization approach to learn pure fixed-point representations of deep neural networks" ] }, { "id": "task668-3573c3581985430abdf824c974011ed4", "input": "A key goal in neuroscience is to understand brain mechanisms of cognitive functions. An emerging approach is to study “brain states” dynamics using functional magnetic resonance imaging (fMRI). So far in the literature, brain states have typically been studied using 30 seconds of fMRI data or more, and it is unclear to which extent brain states can be reliably identified from very short time series. In this project, we applied graph convolutional networks (GCN) to decode brain activity over short time windows in a task fMRI dataset, i.e. associate a given window of fMRI time series with the task used. Starting with a populational brain graph with nodes defined by a parcellation of cerebral cortex and the adjacent matrix extracted from functional connectome, GCN takes a short series of fMRI volumes as input, generates high-level domain-specific graph representations, and then predicts the corresponding cognitive state. We investigated the performance of this GCN \"cognitive state annotation\" in the Human Connectome Project (HCP) database, which features 21 different experimental conditions spanning seven major cognitive domains, and high temporal resolution task fMRI data. Using a 10-second window, the 21 cognitive states were identified with an excellent average test accuracy of 89% (chance level 4.8%). As the HCP task battery was designed to selectively activate a wide range of specialized functional networks, we anticipate the GCN annotation to be applicable as a base model for other transfer learning applications, for instance, adapting to new task domains.", "output": [ "Using a 10s window of fMRI signals, our GCN model identified 21 different task conditions from HCP dataset with a test accuracy of 89%." ] }, { "id": "task668-fd827f3ac1f846d2a286e17169744968", "input": "We relate the minimax game of generative adversarial networks (GANs) to finding the saddle points of the Lagrangian function for a convex optimization problem, where the discriminator outputs and the distribution of generator outputs play the roles of primal variables and dual variables, respectively. This formulation shows the connection between the standard GAN training process and the primal-dual subgradient methods for convex optimization. The inherent connection does not only provide a theoretical convergence proof for training GANs in the function space, but also inspires a novel objective function for training. The modified objective function forces the distribution of generator outputs to be updated along the direction according to the primal-dual subgradient methods. A toy example shows that the proposed method is able to resolve mode collapse, which in this case cannot be avoided by the standard GAN or Wasserstein GAN. Experiments on both Gaussian mixture synthetic data and real-world image datasets demonstrate the performance of the proposed method on generating diverse samples.", "output": [ "We propose a primal-dual subgradient method for training GANs and this method effectively alleviates mode collapse." ] }, { "id": "task668-889259169f4a42568e4e7ec40196c55c", "input": "Operating deep neural networks on devices with limited resources requires the reduction of their memory footprints and computational requirements. In this paper we introduce a training method, called look-up table quantization (LUT-Q), which learns a dictionary and assigns each weight to one of the dictionary's values. We show that this method is very flexible and that many other techniques can be seen as special cases of LUT-Q. For example, we can constrain the dictionary trained with LUT-Q to generate networks with pruned weight matrices or restrict the dictionary to powers-of-two to avoid the need for multiplications. In order to obtain fully multiplier-less networks, we also introduce a multiplier-less version of batch normalization. Extensive experiments on image recognition and object detection tasks show that LUT-Q consistently achieves better performance than other methods with the same quantization bitwidth.", "output": [ "In this paper we introduce a training method, called look-up table quantization (LUT-Q), which learns a dictionary and assigns each weight to one of the dictionary's values" ] }, { "id": "task668-a11759bbf7a0490c88582bed408cd514", "input": "We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. (2017) to input representations of variable capacity. There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units. We perform a systematic comparison of popular choices for a self-attentional architecture. Our experiments show that models equipped with adaptive embeddings are more than twice as fast to train than the popular character input CNN while having a lower number of parameters. On the WikiText-103 benchmark we achieve 18.7 perplexity, an improvement of 10.5 perplexity compared to the previously best published result and on the Billion Word benchmark, we achieve 23.02 perplexity.", "output": [ "Variable capacity input word embeddings and SOTA on WikiText-103, Billion Word benchmarks." ] }, { "id": "task668-847cbf4320904154b4c7a4e8145a925c", "input": "The fabrication of semiconductor involves etching process to remove selected areas from wafers. However, the measurement of etched structure in micro-graph heavily relies on time-consuming manual routines. Traditional image processing usually demands on large number of annotated data and the performance is still poor. We treat this challenge as segmentation problem and use deep learning approach to detect masks of objects in etched structure of wafer. Then, we use simple image processing to carry out automatic measurement on the objects. We attempt Generative Adversarial Network (GAN) to generate more data to overcome the problem of very limited dataset. We download 10 SEM (Scanning Electron Microscope) images of 4 types from Internet, based on which we carry out our experiments. Our deep learning based method demonstrates superiority over image processing approach with mean accuracy reaching over 96% for the measurements, compared with the ground truth. To the best of our knowledge, it is the first time that deep learning has been applied in semiconductor industry for automatic measurement.", "output": [ "Using deep learning method to carry out automatic measurement of SEM images in semiconductor industry" ] }, { "id": "task668-39b00b9f87cb4a8581c9a9754bd47e6a", "input": "Existing sequence prediction methods are mostly concerned with time-independent sequences, in which the actual time span between events is irrelevant and the distance between events is simply the difference between their order positions in the sequence. While this time-independent view of sequences is applicable for data such as natural languages, e.g., dealing with words in a sentence, it is inappropriate and inefficient for many real world events that are observed and collected at unequally spaced points of time as they naturally arise, e.g., when a person goes to a grocery store or makes a phone call. The time span between events can carry important information about the sequence dependence of human behaviors. In this work, we propose a set of methods for using time in sequence prediction. Because neural sequence models such as RNN are more amenable for handling token-like input, we propose two methods for time-dependent event representation, based on the intuition on how time is tokenized in everyday life and previous work on embedding contextualization. We also introduce two methods for using next event duration as regularization for training a sequence prediction model. We discuss these methods based on recurrent neural nets. We evaluate these methods as well as baseline models on five datasets that resemble a variety of sequence prediction tasks. The experiments revealed that the proposed methods offer accuracy gain over baseline models in a range of settings.", "output": [ "Proposed methods for time-dependent event representation and regularization for sequence prediction; Evaluated these methods on five datasets that involve a range of sequence prediction tasks." ] }, { "id": "task668-20cdc79a4d284a91b46a66b4909e5dd4", "input": "Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions. However, generalization properties of GANs have not been well understood. In this paper, we analyze the generalization of GANs in practical settings. We show that discriminators trained on discrete datasets with the original GAN loss have poor generalization capability and do not approximate the theoretically optimal discriminator. We propose a zero-centered gradient penalty for improving the generalization of the discriminator by pushing it toward the optimal discriminator. The penalty guarantees the generalization and convergence of GANs. Experiments on synthetic and large scale datasets verify our theoretical analysis.\n", "output": [ "We propose a zero-centered gradient penalty for improving generalization and stability of GANs" ] }, { "id": "task668-38846b50243440929e2db1b5d1d0b010", "input": "Generative models have been successfully applied to image style transfer and domain translation. However, there is still a wide gap in the quality of results when learning such tasks on musical audio. Furthermore, most translation models only enable one-to-one or one-to-many transfer by relying on separate encoders or decoders and complex, computationally-heavy models. In this paper, we introduce the Modulated Variational auto-Encoders (MoVE) to perform musical timbre transfer. First, we define timbre transfer as applying parts of the auditory properties of a musical instrument onto another. We show that we can achieve and improve this task by conditioning existing domain translation techniques with Feature-wise Linear Modulation (FiLM). Then, by replacing the usual adversarial translation criterion by a Maximum Mean Discrepancy (MMD) objective, we alleviate the need for an auxiliary pair of discriminative networks. This allows a faster and more stable training, along with a controllable latent space encoder. By further conditioning our system on several different instruments, we can generalize to many-to-many transfer within a single variational architecture able to perform multi-domain transfers. Our models map inputs to 3-dimensional representations, successfully translating timbre from one instrument to another and supporting sound synthesis on a reduced set of control parameters. We evaluate our method in reconstruction and generation tasks while analyzing the auditory descriptor distributions across transferred domains. We show that this architecture incorporates generative controls in multi-domain transfer, yet remaining rather light, fast to train and effective on small datasets.", "output": [ "The paper uses Variational Auto-Encoding and network conditioning for Musical Timbre Transfer, we develop and generalize our architecture for many-to-many instrument transfers together with visualizations and evaluations.", "Proposes a Modulated Variational auto-Encoder to perform musical timbre transfer by replacing the usual adversarial translation criterion by a Maxiimum Mean Discrepancy", "Describes a many-to-many model for musical timbre transfer which builds on recent developments in domain and style transfer", "Proposes a hybrid VAE-based model to perform timbre transfer on recordings of musical instruments." ] }, { "id": "task668-970e85ddbabf4f588b9739c1dfde1a32", "input": "State-of-the-art results on neural machine translation often use attentional sequence-to-sequence models with some form of convolution or recursion. Vaswani et. al. (2017) propose a new architecture that avoids recurrence and convolution completely. Instead, it uses only self-attention and feed-forward layers. While the proposed architecture achieves state-of-the-art results on several machine translation tasks, it requires a large number of parameters and training iterations to converge. We propose Weighted Transformer, a Transformer with modified attention layers, that not only outperforms the baseline network in BLEU score but also converges 15-40% faster. Specifically, we replace the multi-head attention by multiple self-attention branches that the model learns to combine during the training process. Our model improves the state-of-the-art performance by 0.5 BLEU points on the WMT 2014 English-to-German translation task and by 0.4 on the English-to-French translation task.", "output": [ "Using branched attention with learned combination weights outperforms the baseline transformer for machine translation tasks." ] }, { "id": "task668-00277b6afcba456e8527106502844f11", "input": "In this paper, we propose two methods, namely Trace-norm regression (TNR) and Stable Trace-norm Analysis (StaTNA), to improve performances of recommender systems with side information. Our trace-norm regression approach extracts low-rank latent factors underlying the side information that drives user preference under different context. Furthermore, our novel recommender framework StaTNA not only captures latent low-rank common drivers for user preferences, but also considers idiosyncratic taste for individual users. We compare performances of TNR and StaTNA on the MovieLens datasets against state-of-the-art models, and demonstrate that StaTNA and TNR in general outperforms these methods.", "output": [ "Methodologies for recommender systems with side information based on trace-norm regularization" ] }, { "id": "task668-7ea2d0ff0e0c4932b46dd6a00e930dc8", "input": "In this paper, we conduct an intriguing experimental study about the physical adversarial attack on object detectors in the wild. In particular, we learn a camouflage pattern to hide vehicles from being detected by state-of-the-art convolutional neural network based detectors. Our approach alternates between two threads. In the first, we train a neural approximation function to imitate how a simulator applies a camouflage to vehicles and how a vehicle detector performs given images of the camouflaged vehicles. In the second, we minimize the approximated detection score by searching for the optimal camouflage. Experiments show that the learned camouflage can not only hide a vehicle from the image-based detectors under many test cases but also generalizes to different environments, vehicles, and object detectors.", "output": [ "We propose a method to learn physical vehicle camouflage to adversarially attack object detectors in the wild. We find our camouflage effective and transferable.", "The authors investigate the problem of learning a camouflage pattern which, when applied to a simulated vehicle, will prevent an object detector from detecting it.", "This paper targets adversarial learning for interfering car detection by learning camouflage patterns" ] }, { "id": "task668-641f2bd61da34de885c848994893e775", "input": "Given a video and a sentence, the goal of weakly-supervised video moment retrieval is to locate the video segment which is described by the sentence without having access to temporal annotations during training. Instead, a model must learn how to identify the correct segment (i.e. moment) when only being provided with video-sentence pairs. Thus, an inherent challenge is automatically inferring the latent correspondence between visual and language representations. To facilitate this alignment, we propose our Weakly-supervised Moment Alignment Network (wMAN) which exploits a multi-level co-attention mechanism to learn richer multimodal representations. The aforementioned mechanism is comprised of a Frame-By-Word interaction module as well as a novel Word-Conditioned Visual Graph (WCVG). Our approach also incorporates a novel application of positional encodings, commonly used in Transformers, to learn visual-semantic representations that contain contextual information of their relative positions in the temporal sequence through iterative message-passing. Comprehensive experiments on the DiDeMo and Charades-STA datasets demonstrate the effectiveness of our learned representations: our combined wMAN model not only outperforms the state-of-the-art weakly-supervised method by a significant margin but also does better than strongly-supervised state-of-the-art methods on some metrics.", "output": [ "Weakly-Supervised Text-Based Video Moment Retrieval" ] }, { "id": "task668-0519e8a2e07e4ffbb874b7873958b23b", "input": "Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific reasons. Though, given the lack of sample efficiency in current learning methods, reaching this goal may require substantial research efforts. We introduce the BabyAI research platform, with the goal of supporting investigations towards including humans in the loop for grounded language learning. The BabyAI platform comprises an extensible suite of 19 levels of increasing difficulty. Each level gradually leads the agent towards acquiring a combinatorially rich synthetic language, which is a proper subset of English. The platform also provides a hand-crafted bot agent, which simulates a human teacher. We report estimated amount of supervision required for training neural reinforcement and behavioral-cloning agents on some BabyAI levels. We put forward strong evidence that current deep learning methods are not yet sufficiently sample-efficient in the context of learning a language with compositional properties.", "output": [ "We present the BabyAI platform for studying data efficiency of language learning with a human in the loop", "Presents a research platform with a bot in the loop for learning to execute language instructions in which language has compositional structures", "Introduces a platform for grounded language learning that replaces any human in the loop with a heuristic teacher and uses a synthetic language mapped to a 2D grid world" ] }, { "id": "task668-411d6887d41a45f1b74265c0205a872a", "input": "Detecting anomalies is of growing importance for various industrial applications and mission-critical infrastructures, including satellite systems. Although there have been several studies in detecting anomalies based on rule-based or machine learning-based approaches for satellite systems, a tensor-based decomposition method has not been extensively explored for anomaly detection. In this work, we introduce an Integrative Tensor-based Anomaly Detection (ITAD) framework to detect anomalies in a satellite system. Because of the high risk and cost, detecting anomalies in a satellite system is crucial. We construct 3rd-order tensors with telemetry data collected from Korea Multi-Purpose Satellite-2 (KOMPSAT-2) and calculate the anomaly score using one of the component matrices obtained by applying CANDECOMP/PARAFAC decomposition to detect anomalies. Our result shows that our tensor-based approach can be effective in achieving higher accuracy and reducing false positives in detecting anomalies as compared to other existing approaches.", "output": [ "Integrative Tensor-based Anomaly Detection(ITAD) framework for a satellite system." ] }, { "id": "task668-5b20e65de6ce473b934f260ead762b60", "input": "Hierarchical planning, in particular, Hierarchical Task Networks, was proposed as a method to describe plans by decomposition of tasks to sub-tasks until primitive tasks, actions, are obtained. Plan verification assumes a complete plan as input, and the objective is finding a task that decomposes to this plan. In plan recognition, a prefix of the plan is given and the objective is finding a task that decomposes to the (shortest) plan with the given prefix. This paper describes how to verify and recognize plans using a common method known from formal grammars, by parsing.", "output": [ "The paper describes methods to verify and recognize HTN plans by parsing of attribute grammars." ] }, { "id": "task668-f8dcd56429df4fc28cf503f5058eb806", "input": "Capsule Networks have shown encouraging results on \\textit{defacto} benchmark computer vision datasets such as MNIST, CIFAR and smallNORB. Although, they are yet to be tested on tasks where (1) the entities detected inherently have more complex internal representations and (2) there are very few instances per class to learn from and (3) where point-wise classification is not suitable. Hence, this paper carries out experiments on face verification in both controlled and uncontrolled settings that together address these points. In doing so we introduce \\textit{Siamese Capsule Networks}, a new variant that can be used for pairwise learning tasks. We find that the model improves over baselines in the few-shot learning setting, suggesting that capsule networks are efficient at learning discriminative representations when given few samples. \n We find that \\textit{Siamese Capsule Networks} perform well against strong baselines on both pairwise learning datasets when trained using a contrastive loss with $\\ell_2$-normalized capsule encoded pose features, yielding best results in the few-shot learning setting where image pairs in the test set contain unseen subjects.", "output": [ "A pairwise learned capsule network that performs well on face verification tasks given limited labeled data " ] }, { "id": "task668-5fb21afa772d42c8a3bc25f291e03b03", "input": "Convolutional neural networks (CNNs) were inspired by human vision and, in some settings, achieve a performance comparable to human object recognition. This has lead to the speculation that both systems use similar mechanisms to perform recognition. In this study, we conducted a series of simulations that indicate that there is a fundamental difference between human vision and CNNs: while object recognition in humans relies on analysing shape, CNNs do not have such a shape-bias. We teased apart the type of features selected by the model by modifying the CIFAR-10 dataset so that, in addition to containing objects with shape, the images concurrently contained non-shape features, such as a noise-like mask. When trained on these modified set of images, the model did not show any bias towards selecting shapes as features. Instead it relied on whichever feature allowed it to perform the best prediction -- even when this feature was a noise-like mask or a single predictive pixel amongst 50176 pixels. We also found that regularisation methods, such as batch normalisation or Dropout, did not change this behaviour and neither did past or concurrent experience with images from other datasets.", "output": [ "This study highlights a key difference between human vision and CNNs: while object recognition in humans relies on analysing shape, CNNs do not have such a shape-bias.", "Seeks to establish via a series of well-designed experiments that CNNs trained for image classification don’t encode shape-bias like human vision.", "This paper highlights the fact that CNNs will not necessarily learn to recognize objects based on their shape and shows they will overfeat to noise based features." ] }, { "id": "task668-cf0707322dbb423e908303346453b50a", "input": "Learning in Gaussian Process models occurs through the adaptation of hyperparameters of the mean and the covariance function. The classical approach entails maximizing the marginal likelihood yielding fixed point estimates (an approach called Type II maximum likelihood or ML-II). An alternative learning procedure is to infer the posterior over hyperparameters in a hierarchical specification of GPs we call Fully Bayesian Gaussian Process Regression (GPR) . This work considers two approximations to the intractable hyperparameter posterior , 1) Hamiltonian Monte Carlo (HMC) yielding a sampling based approximation and 2) Variational Inference (VI) where the posterior over hyperparameters is approximated by a factorized Gaussian (mean-field) or a full rank Gaussian accounting for correlations between hyperparameters. We analyse the predictive performance for fully Bayesian GPR on a range of benchmark data sets.", "output": [ "Analysis of Bayesian Hyperparameter Inference in Gaussian Process Regression " ] }, { "id": "task668-f57838dd553e4b3fb35274e53fc256f8", "input": "We demonstrate how to learn efficient heuristics for automated reasoning algorithms through deep reinforcement learning. We focus on backtracking search algorithms for quantified Boolean logics, which already can solve formulas of impressive size - up to 100s of thousands of variables. The main challenge is to find a representation of these formulas that lends itself to making predictions in a scalable way. For challenging problems, the heuristic learned through our approach reduces execution time by a factor of 10 compared to the existing handwritten heuristics.", "output": [ "RL finds better heuristics for automated reasoning algorithms.", "Aims to learn a heuristic for a backtracking search algorithm utilizing Reinforcement learning and proposes a model that makes use of Graphical Neural Networks to produce literal and clauses embedding, and use them to predict the quality of each literal to decide the probability of each action.", "The paper proposes an approach to automatically learning variable selection heuristics for QBF using deep learning" ] }, { "id": "task668-f5afad1d522e41b7925a7d856e0838a9", "input": "Most research on lifelong learning applies to images or games, but not language.\n We present LAMOL, a simple yet effective method for lifelong language learning (LLL) based on language modeling.\n LAMOL replays pseudo-samples of previous tasks while requiring no extra memory or model capacity.\n Specifically, LAMOL is a language model that simultaneously learns to solve the tasks and generate training samples.\n When the model is trained for a new task, it generates pseudo-samples of previous tasks for training alongside data for the new task.\n The results show that LAMOL prevents catastrophic forgetting without any sign of intransigence and can perform five very different language tasks sequentially with only one model. \n Overall, LAMOL outperforms previous methods by a considerable margin and is only 2-3% worse than multitasking, which is usually considered the LLL upper bound.\n The source code is available at https://github.com/jojotenya/LAMOL.", "output": [ "Language modeling for lifelong language learning." ] }, { "id": "task668-42b094f8e23a4e188f223a6382bb821b", "input": "The goal of generative models is to model the underlying data distribution of a\n sample based dataset. Our intuition is that an accurate model should in principle\n also include the sample based dataset as part of its induced probability distribution.\n To investigate this, we look at fully trained generative models using the Generative\n Adversarial Networks (GAN) framework and analyze the resulting generator\n on its ability to memorize the dataset. Further, we show that the size of the initial\n latent space is paramount to allow for an accurate reconstruction of the training\n data. This gives us a link to compression theory, where Autoencoders (AE) are\n used to lower bound the reconstruction capabilities of our generative model. Here,\n we observe similar results to the perception-distortion tradeoff (Blau & Michaeli\n (2018)). Given a small latent space, the AE produces low quality and the GAN\n produces high quality outputs from a perceptual viewpoint. In contrast, the distortion\n error is smaller for the AE. By increasing the dimensionality of the latent\n space the distortion decreases for both models, but the perceptual quality only\n increases for the AE.", "output": [ "We analyze the impact of the latent space of fully trained generators by pseudo inverting them." ] }, { "id": "task668-d171c6e3fe674f8c992f5d822debd5dd", "input": "Given samples from a group of related regression tasks, a data-enriched model describes observations by a common and per-group individual parameters. In high-dimensional regime, each parameter has its own structure such as sparsity or group sparsity. In this paper, we consider the general form of data enrichment where data comes in a fixed but arbitrary number of tasks $G$ and any convex function, e.g., norm, can characterize the structure of both common and individual parameters. \t We propose an estimator for the high-dimensional data enriched model and investigate its statistical properties. We delineate the sample complexity of our estimator and provide high probability non-asymptotic bound for estimation error of all parameters under a condition weaker than the state-of-the-art. We propose an iterative estimation algorithm with a geometric convergence rate. Overall, we present a first through statistical and computational analysis of inference in the data enriched model. \n\t", "output": [ "We provide an estimator and an estimation algorithm for a class of multi-task regression problem and provide statistical and computational analysis.." ] }, { "id": "task668-634de584076f4c38b559c193956a763f", "input": "Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations. These methods assume that all the adversarial transformations are equally important, which is seldom the case in real-world applications. We advocate for cost-sensitive robustness as the criteria for measuring the classifier's performance for tasks where some adversarial transformation are more important than others. We encode the potential harm of each adversarial transformation in a cost matrix, and propose a general objective function to adapt the robust training method of Wong & Kolter (2018) to optimize for cost-sensitive robustness. Our experiments on simple MNIST and CIFAR10 models with a variety of cost matrices show that the proposed approach can produce models with substantially reduced cost-sensitive robust error, while maintaining classification accuracy.", "output": [ "A general method for training certified cost-sensitive robust classifier against adversarial perturbations", "Calculates and plugs in the costs of adversarial attack into the objective of optimization to get a model that is cost-sensitively robust against adversarial attacks. ", "Build on semnial work by Dalvi et al. and extends approach to certifiable robustness with a cost matrix that specifies for each pair of source-target classes whether the model should be robust to adversarial examples." ] }, { "id": "task668-8df2ad69979c48ce8709c132fd67a2a1", "input": "Depth is a key component of Deep Neural Networks (DNNs), however, designing depth is heuristic and requires many human efforts. We propose AutoGrow to automate depth discovery in DNNs: starting from a shallow seed architecture, AutoGrow grows new layers if the growth improves the accuracy; otherwise, stops growing and thus discovers the depth. We propose robust growing and stopping policies to generalize to different network architectures and datasets. Our experiments show that by applying the same policy to different network architectures, AutoGrow can always discover near-optimal depth on various datasets of MNIST, FashionMNIST, SVHN, CIFAR10, CIFAR100 and ImageNet. For example, in terms of accuracy-computation trade-off, AutoGrow discovers a better depth combination in ResNets than human experts. Our AutoGrow is efficient. It discovers depth within similar time of training a single DNN.", "output": [ "A method that automatically grows layers in neural networks to discover optimal depth.", "A framework to interleave training a shallower network and adding new layers which provides insights into the paradigm of 'growing networks'." ] }, { "id": "task668-f507b64cc5f3410d8228478455a04d08", "input": "Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, relation extraction, and question answering. Supervised learning from labeled hypernym sources, such as WordNet, limit the coverage of these models, which can be addressed by learning hypernyms from unlabeled text. Existing unsupervised methods either do not scale to large vocabularies or yield unacceptably poor accuracy. This paper introduces {\\it distributional inclusion vector embedding (DIVE)}, a simple-to-implement unsupervised method of hypernym discovery via per-word non-negative vector embeddings which preserve the inclusion property of word contexts. In experimental evaluations more comprehensive than any previous literature of which we are aware---evaluating on 11 datasets using multiple existing as well as newly proposed scoring functions---we find that our method provides up to double the precision of previous unsupervised methods, and the highest average performance, using a much more compact word representation, and yielding many new state-of-the-art results. In addition, the meaning of each dimension in DIVE is interpretable, which leads to a novel approach on word sense disambiguation as another promising application of DIVE.", "output": [ "We propose a novel unsupervised word embedding which preserves the inclusion property in the context distribution and achieve state-of-the-art results on unsupervised hypernymy detection" ] }, { "id": "task668-0ea105321e7644288db01b64f2611d49", "input": "Imitation learning, followed by reinforcement learning algorithms, is a promising paradigm to solve complex control tasks sample-efficiently. However, learning from demonstrations often suffers from the covariate shift problem, which results\n in cascading errors of the learned policy. We introduce a notion of conservatively extrapolated value functions, which provably lead to policies with self-correction. We design an algorithm Value Iteration with Negative Sampling (VINS) that practically learns such value functions with conservative extrapolation. We show that VINS can correct mistakes of the behavioral cloning policy on simulated robotics benchmark tasks. We also propose the algorithm of using VINS to initialize a reinforcement learning algorithm, which is shown to outperform prior works in sample efficiency.", "output": [ "We introduce a notion of conservatively-extrapolated value functions, which provably lead to policies that can self-correct to stay close to the demonstration states, and learn them with a novel negative sampling technique.", "An algorithm called value iteration with negative sampling to address the covariate shift problem in imitation learning." ] }, { "id": "task668-54dfe034e2d74f6b8569ca4d82fe2792", "input": "Many approaches to causal discovery are limited by their inability to discriminate between Markov equivalent graphs given only observational data. We formulate causal discovery as a marginal likelihood based Bayesian model selection problem. We adopt a parameterization based on the notion of the independence of causal mechanisms which renders Markov equivalent graphs distinguishable. We complement this with an empirical Bayesian approach to setting priors so that the actual underlying causal graph is assigned a higher marginal likelihood than its alternatives. Adopting a Bayesian approach also allows for straightforward modeling of unobserved confounding variables, for which we provide a variational algorithm to approximate the marginal likelihood, since this desirable feat renders the computation of the marginal likelihood intractable. We believe that the Bayesian approach to causal discovery both allows the rich methodology of Bayesian inference to be used in various difficult aspects of this problem and provides a unifying framework to causal discovery research. We demonstrate promising results in experiments conducted on real data, supporting our modeling approach and our inference methodology.", "output": [ "We cast causal structure discovery as a Bayesian model selection in a way that allows us to discriminate between Markov equivalent graphs to identify the unique causal graph." ] }, { "id": "task668-c5a84e3110ad442283bf249db217ffbf", "input": "Navigation is crucial for animal behavior and is assumed to require an internal representation of the external environment, termed a cognitive map. The precise form of this representation is often considered to be a metric representation of space. An internal representation, however, is judged by its contribution to performance on a given task, and may thus vary between different types of navigation tasks. Here we train a recurrent neural network that controls an agent performing several navigation tasks in a simple environment. To focus on internal representations, we split learning into a task-agnostic pre-training stage that modifies internal connectivity and a task-specific Q learning stage that controls the network's output. We show that pre-training shapes the attractor landscape of the networks, leading to either a continuous attractor, discrete attractors or a disordered state. These structures induce bias onto the Q-Learning phase, leading to a performance pattern across the tasks corresponding to metric and topological regularities. Our results show that, in recurrent networks, inductive bias takes the form of attractor landscapes -- which can be shaped by pre-training and analyzed using dynamical systems methods. Furthermore, we demonstrate that non-metric representations are useful for navigation tasks. ", "output": [ "Task agnostic pre-training can shape RNN's attractor landscape, and form diverse inductive bias for different navigation tasks ", "This paper studies the internal representations of recurrent neural networks trained on navigation tasks, and finds that RNNs pre-trained to use path integration contain 2D continuous attractors while RNNs pre-trained for landmark memory contain discrete attractors.", "This paper explores how pre-training recurrent networks on different navigational objectives confers different benefits for solving downstream tasks, and shows how different pretraining manifests as different dynamical structures in the networks after pre-training." ] }, { "id": "task668-ffb09cbe291c43a5a671b996d149527e", "input": "Graph Neural Networks (GNNs) have received tremendous attention recently due to their power in handling graph data for different downstream tasks across different application domains. The key of GNN is its graph convolutional filters, and recently various kinds of filters are designed. However, there still lacks in-depth analysis on (1) Whether there exists a best filter that can perform best on all graph data; (2) Which graph properties will influence the optimal choice of graph filter; (3) How to design appropriate filter adaptive to the graph data. In this paper, we focus on addressing the above three questions. We first propose a novel assessment tool to evaluate the effectiveness of graph convolutional filters for a given graph. Using the assessment tool, we find out that there is no single filter as a `silver bullet' that perform the best on all possible graphs. In addition, different graph structure properties will influence the optimal graph convolutional filter's design choice. Based on these findings, we develop Adaptive Filter Graph Neural Network (AFGNN), a simple but powerful model that can adaptively learn task-specific filter. For a given graph, it leverages graph filter assessment as regularization and learns to combine from a set of base filters. Experiments on both synthetic and real-world benchmark datasets demonstrate that our proposed model can indeed learn an appropriate filter and perform well on graph tasks.", "output": [ "Propose an assessment framework to analyze and learn graph convolutional filter" ] }, { "id": "task668-0818e2d304124c458f809f87ab2386c1", "input": "Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to recover a near noise-free image from a noisy image.\n The underlying principle is that neural networks trained on large datasets have empirically been shown to be able to generate natural images well from a low-dimensional latent representation of the image.\n Given such a generator network, or prior, a noisy image can be denoised by finding the closest image in the range of the prior.\n However, there is little theory to justify this success, let alone to predict the denoising performance as a function of the networks parameters.\n In this paper we consider the problem of denoising an image from additive Gaussian noise, assuming the image is well described by a deep neural network with ReLu activations functions, mapping a k-dimensional latent space to an n-dimensional image.\n We state and analyze a simple gradient-descent-like iterative algorithm that minimizes a non-convex loss function, and provably removes a fraction of (1 - O(k/n)) of the noise energy.\n We also demonstrate in numerical experiments that this denoising performance is, indeed, achieved by generative priors learned from data.", "output": [ "By analyzing an algorithms minimizing a non-convex loss, we show that all but a small fraction of noise can be removed from an image using a deep neural network based generative prior." ] }, { "id": "task668-dfc6780065984d69aab92978c4bc0c5a", "input": "Answering questions about data can require understanding what parts of an input X influence the response Y. Finding such an understanding can be built by testing relationships between variables through a machine learning model. For example, conditional randomization tests help determine whether a variable relates to the response given the rest of the variables. However, randomization tests require users to specify test statistics. We formalize a class of proper test statistics that are guaranteed to select a feature when it provides information about the response even when the rest of the features are known. We show that f-divergences provide a broad class of proper test statistics. In the class of f-divergences, the KL-divergence yields an easy-to-compute proper test statistic that relates to the AMI. Questions of feature importance can be asked at the level of an individual sample. We show that estimators from the same AMI test can also be used to find important features in a particular instance. We provide an example to show that perfect predictive models are insufficient for instance-wise feature selection. We evaluate our method on several simulation experiments, on a genomic dataset, a clinical dataset for hospital readmission, and on a subset of classes in ImageNet. Our method outperforms several baselines in various simulated datasets, is able to identify biologically significant genes, can select the most important predictors of a hospital readmission event, and is able to identify distinguishing features in an image-classification task.", "output": [ "We develop a simple regression-based model-agnostic feature selection method to interpret data generating processes with FDR control, and outperform several popular baselines on several simulated, medical, and image datasets.", "This paper proposes a practical improvement of the conditional randomization test and a new test statistic, proves f-divergence is one possible choice, and shows that KL-divergence cancels out some conditional distributions.", "This paper addresses the problem of finding useful features in an input that are dependent on a response variable even when conditioning on all other input variables.", "A model agnostic method to provide interpretation on the influence of input features on the response of a machine level model down to instance level, and proper test statistics for model agnostic feature selection." ] }, { "id": "task668-cdb2a40264794e55aef31c8de2313c90", "input": "Training deep neural networks requires many training samples, but in practice training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental quality- versus-quantity trade-off in the learning process. Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data? We argue that if the learner could somehow know and take the label-quality into account when learning the data representation, we could get the best of both worlds. To this end, we propose “fidelity-weighted learning” (FWL), a semi-supervised student- teacher approach for training deep neural networks using weakly-labeled data. FWL modulates the parameter updates to a student network (trained on the task we care about) on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher (who has access to the high-quality labels). Both student and teacher are learned from the data. We evaluate FWL on two tasks in information retrieval and natural language processing where we outperform state-of-the-art alternative semi-supervised methods, indicating that our approach makes better use of strong and weak labels, and leads to better task-dependent data representations.", "output": [ "We propose Fidelity-weighted Learning, a semi-supervised teacher-student approach for training neural networks using weakly-labeled data.", "This paper suggests an approach for learning with weak supervision by using a clean and a noisy dataset and assuming a teacher and student networks", "The paper attemps to train deep neural network models with few labelled training samples.", "The authors propose an approach for training deep learning models for situations where there is not enough reliable annotated data." ] }, { "id": "task668-ec914c1fd1b344bab1a48a39ea6b56a3", "input": "Routing models, a form of conditional computation where examples are routed through a subset of components in a larger network, have shown promising results in recent works. Surprisingly, routing models to date have lacked important properties, such as architectural diversity and large numbers of routing decisions. Both architectural diversity and routing depth can increase the representational power of a routing network. In this work, we address both of these deficiencies. We discuss the significance of architectural diversity in routing models, and explain the tradeoffs between capacity and optimization when increasing routing depth. In our experiments, we find that adding architectural diversity to routing models significantly improves performance, cutting the error rates of a strong baseline by 35% on an Omniglot setup. However, when scaling up routing depth, we find that modern routing techniques struggle with optimization. We conclude by discussing both the positive and negative results, and suggest directions for future research.", "output": [ "Per-example routing models benefit from architectural diversity, but still struggle to scale to a large number of routing decisions.", "Adds diversity to the type of architectural unit available for the router at each decision and scaling to deeper networks, achieving state of the art performance on Omniglot. ", "This work extends routing networks to use diverse architectures across routed modules" ] }, { "id": "task668-81ca9758e14b428dbfde383ddbf170a7", "input": " The goal of compressed sensing is to learn a structured signal $x$\n from a limited number of noisy linear measurements $y \\approx Ax$. In\n traditional compressed sensing, ``structure'' is represented by\n sparsity in some known basis. Inspired by the success of deep\n learning in modeling images, recent work starting with~\\cite{BDJP17}\n has instead considered structure to come from a generative model\n $G: \\R^k \\to \\R^n$. We present two results establishing the\n difficulty of this latter task, showing that existing bounds are\n tight.\n\n First, we provide a lower bound matching the~\\cite{BDJP17} upper\n bound for compressed sensing from $L$-Lipschitz generative models\n $G$. In particular, there exists such a function that requires\n roughly $\\Omega(k \\log L)$ linear measurements for sparse recovery\n to be possible. This holds even for the more relaxed goal of\n \\emph{nonuniform} recovery.\n\n Second, we show that generative models generalize sparsity as a\n representation of structure. In particular, we construct a\n ReLU-based neural network $G: \\R^{2k} \\to \\R^n$ with $O(1)$ layers\n and $O(kn)$ activations per layer, such that the range of $G$\n contains all $k$-sparse vectors.\n", "output": [ "Lower bound for compressed sensing w/ generative models that matches known upper bounds" ] }, { "id": "task668-d39e7c5760d0486296dc5f56b2131e0a", "input": "We propose the fusion discriminator, a single unified framework for incorporating conditional information into a generative adversarial network (GAN) for a variety of distinct structured prediction tasks, including image synthesis, semantic segmentation, and depth estimation. Much like commonly used convolutional neural network - conditional Markov random field (CNN-CRF) models, the proposed method is able to enforce higher-order consistency in the model, but without being limited to a very specific class of potentials. The method is conceptually simple and flexible, and our experimental results demonstrate improvement on several diverse structured prediction tasks.", "output": [ "We propose the fusion discriminator, a novel architecture for incorporating conditional information into the discriminator of GANs for structured prediction tasks." ] }, { "id": "task668-7146ac4a8737462794f5bcf4726edaea", "input": "Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs. One core idea of adversarial example research is to reveal neural network errors under such distribution shifts. We decompose these errors into two complementary sources: sensitivity and invariance. We show deep networks are not only too sensitive to task-irrelevant changes of their input, as is well-known from epsilon-adversarial examples, but are also too invariant to a wide range of task-relevant changes, thus making vast regions in input space vulnerable to adversarial attacks. We show such excessive invariance occurs across various tasks and architecture types. On MNIST and ImageNet one can manipulate the class-specific content of almost any image without changing the hidden activations. We identify an insufficiency of the standard cross-entropy loss as a reason for these failures. Further, we extend this objective based on an information-theoretic analysis so it encourages the model to consider all task-dependent features in its decision. This provides the first approach tailored explicitly to overcome excessive invariance and resulting vulnerabilities.", "output": [ "We show deep networks are not only too sensitive to task-irrelevant changes of their input, but also too invariant to a wide range of task-relevant changes, thus making vast regions in input space vulnerable to adversarial attacks." ] }, { "id": "task668-f4fa27f5677c4bdba45d8a71258e8664", "input": " Large deep neural networks require huge memory to run and their running speed is sometimes too slow for real applications. Therefore network size reduction with keeping accuracy is crucial for practical applications. We present a novel neural network operator, chopout, with which neural networks are trained, even in a single training process, so as to truncated sub-networks perform as well as possible. Chopout is easy to implement and integrate into most type of existing neural networks. Furthermore it enables to reduce size of networks and latent representations even after training just by truncating layers. We show its effectiveness through several experiments.", "output": [ "We present a novel simple operator, chopout, with which neural networks are trained, even in a single training process, so as to truncated sub-networks perform as well as possible." ] }, { "id": "task668-8a1ab44bceea450fb0dc1979c11fcd04", "input": "We present a novel approach for training neural abstract architectures which in- corporates (partial) supervision over the machine’s interpretable components. To cleanly capture the set of neural architectures to which our method applies, we introduce the concept of a differential neural computational machine (∂NCM) and show that several existing architectures (e.g., NTMs, NRAMs) can be instantiated as a ∂NCM and can thus benefit from any amount of additional supervision over their interpretable components. Based on our method, we performed a detailed experimental evaluation with both, the NTM and NRAM architectures, and showed that the approach leads to significantly better convergence and generalization capabilities of the learning phase than when training using only input-output examples.\n", "output": [ "We increase the amount of trace supervision possible to utilize when training fully differentiable neural machine architectures." ] }, { "id": "task668-5b9dd50a59e14073b4972c87d64d971c", "input": "Despite advances in deep learning, artificial neural networks do not learn the same way as humans do. Today, neural networks can learn multiple tasks when trained on them jointly, but cannot maintain performance on learnt tasks when tasks are presented one at a time -- this phenomenon called catastrophic forgetting is a fundamental challenge to overcome before neural networks can learn continually from incoming data. In this work, we derive inspiration from human memory to develop an architecture capable of learning continuously from sequentially incoming tasks, while averting catastrophic forgetting. Specifically, our model consists of a dual memory architecture to emulate the complementary learning systems (hippocampus and the neocortex) in the human brain and maintains a consolidated long-term memory via generative replay of past experiences. We (i) substantiate our claim that replay should be generative, (ii) show the benefits of generative replay and dual memory via experiments, and (iii) demonstrate improved performance retention even for small models with low capacity. Our architecture displays many important characteristics of the human memory and provides insights on the connection between sleep and learning in humans.", "output": [ "A dual memory architecture inspired from human brain to learn sequentially incoming tasks, while averting catastrophic forgetting." ] }, { "id": "task668-91e335a85a3240619e08b30146fec713", "input": "We propose and evaluate new techniques for compressing and speeding up dense matrix multiplications as found in the fully connected and recurrent layers of neural networks for embedded large vocabulary continuous speech recognition (LVCSR). For compression, we introduce and study a trace norm regularization technique for training low rank factored versions of matrix multiplications. Compared to standard low rank training, we show that our method leads to good accuracy versus number of parameter trade-offs and can be used to speed up training of large models. For speedup, we enable faster inference on ARM processors through new open sourced kernels optimized for small batch sizes, resulting in 3x to 7x speed ups over the widely used gemmlowp library. Beyond LVCSR, we expect our techniques and kernels to be more generally applicable to embedded neural networks with large fully connected or recurrent layers.", "output": [ "We compress and speed up speech recognition models on embedded devices through a trace norm regularization technique and optimized kernels." ] }, { "id": "task668-a122a44a7a254f19959552df551a9e5d", "input": "This work provides an automatic machine learning (AutoML) modelling architecture called Autostacker. Autostacker improves the prediction accuracy of machine learning baselines by utilizing an innovative hierarchical stacking architecture and an efficient parameter search algorithm. Neither prior domain knowledge about the data nor feature preprocessing is needed. We significantly reduce the time of AutoML with a naturally inspired algorithm - Parallel Hill Climbing (PHC). By parallelizing PHC, Autostacker can provide candidate pipelines with sufficient prediction accuracy within a short amount of time. These pipelines can be used as is or as a starting point for human experts to build on. By focusing on the modelling process, Autostacker breaks the tradition of following fixed order pipelines by exploring not only single model pipeline but also innovative combinations and structures. As we will show in the experiment section, Autostacker achieves significantly better performance both in terms of test accuracy and time cost comparing with human initial trials and recent popular AutoML system.", "output": [ "Automate machine learning system with efficient search algorithm and innovative structure to provide better model baselines." ] }, { "id": "task668-e27ecaffb0f447f0aaae9b423c0e38d4", "input": "As the area of Explainable AI (XAI), and Explainable AI Planning (XAIP), matures, the ability for agents to generate and curate explanations will likewise grow. We propose a new challenge area in the form of rebellious and deceptive explanations. We discuss how these explanations might be generated and then briefly discuss evaluation criteria.", "output": [ "Position paper proposing rebellious and deceptive explanations for agents." ] }, { "id": "task668-ae04cbb63e2a4a2b9583b64d5cee2671", "input": "Supervised learning problems---particularly those involving social data---are often subjective. That is, human readers, looking at the same data, might come to legitimate but completely different conclusions based on their personal experiences. Yet in machine learning settings feedback from multiple human annotators is often reduced to a single ``ground truth'' label, thus hiding the true, potentially rich and diverse interpretations of the data found across the social spectrum. We explore the rewards and challenges of discovering and learning representative distributions of the labeling opinions of a large human population. A major, critical cost to this approach is the number of humans needed to provide enough labels not only to obtain representative samples but also to train a machine to predict representative distributions on unlabeled data. We propose aggregating label distributions over, not just individuals, but also data items, in order to maximize the costs of humans in the loop. We test different aggregation approaches on state-of-the-art deep learning models. Our results suggest that careful label aggregation methods can greatly reduce the number of samples needed to obtain representative distributions.", "output": [ "We study the problem of learning to predict the underlying diversity of beliefs present in supervised learning domains." ] }, { "id": "task668-621784dd60c848fcbaf63501a4465072", "input": "We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics. We demonstrate that decentralized, population-based training with co-play can lead to a progression in agents' behaviors: from random, to simple ball chasing, and finally showing evidence of cooperation. Our study highlights several of the challenges encountered in large scale multi-agent training in continuous control. In particular, we demonstrate that the automatic optimization of simple shaping rewards, not themselves conducive to co-operative behavior, can lead to long-horizon team behavior. We further apply an evaluation scheme, grounded by game theoretic principals, that can assess agent performance in the absence of pre-defined evaluation tasks or human baselines.", "output": [ "We introduce a new MuJoCo soccer environment for continuous multi-agent reinforcement learning research, and show that population-based training of independent reinforcement learners can learn cooperative behaviors" ] }, { "id": "task668-baeaae4fd3884d25918fb70a64260ece", "input": "The question why deep learning algorithms generalize so well has attracted increasing\n research interest. However, most of the well-established approaches,\n such as hypothesis capacity, stability or sparseness, have not provided complete\n explanations (Zhang et al., 2016; Kawaguchi et al., 2017). In this work, we focus\n on the robustness approach (Xu & Mannor, 2012), i.e., if the error of a hypothesis\n will not change much due to perturbations of its training examples, then it\n will also generalize well. As most deep learning algorithms are stochastic (e.g.,\n Stochastic Gradient Descent, Dropout, and Bayes-by-backprop), we revisit the robustness\n arguments of Xu & Mannor, and introduce a new approach – ensemble\n robustness – that concerns the robustness of a population of hypotheses. Through\n the lens of ensemble robustness, we reveal that a stochastic learning algorithm can\n generalize well as long as its sensitiveness to adversarial perturbations is bounded\n in average over training examples. Moreover, an algorithm may be sensitive to\n some adversarial examples (Goodfellow et al., 2015) but still generalize well. To\n support our claims, we provide extensive simulations for different deep learning\n algorithms and different network architectures exhibiting a strong correlation between\n ensemble robustness and the ability to generalize.", "output": [ "Explaining the generalization of stochastic deep learning algorithms, theoretically and empirically, via ensemble robustness", "This paper presents an adaptation of the algorithmic robustness of Xu&Mannor'12 and presents learning bounds and an experimental showing correlation between empirical ensemble robustness and generalization error. ", "Proposes a study of the generalization ability of deep learning algorithms using an extension of notion of stability called ensemble robustness and gives bounds on generalization error of a randomized algorithm in terms of stability parameter and provides empirical study attempting to connect theory with practice.", "The paper studied the generalization ability of learning algorithms from the robustness viewpoint in a deep learning context" ] }, { "id": "task668-7c843bd9bbaa4cb8a55ec3331c1582e8", "input": "Conservation laws are considered to be fundamental laws of nature. It has broad application in many fields including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. Recent success of machine learning, especially deep learning, in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to explore the possibility and benefit of solving nonlinear conservation laws using deep reinforcement learning. As a proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision making process and the numerical schemes learned in such a way can easily enforce long-term accuracy. \n Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach.\n In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and how well it generalizes. Our code is released anomynously at \\url{https://github.com/qwerlanksdf/L2D}.", "output": [ "We observe that numerical PDE solvers can be regarded as Markov Desicion Processes, and propose to use Reinforcement Learning to solve 1D scalar Conservation Laws" ] }, { "id": "task668-90e0ca5cec864bf6bd0976ae9030814b", "input": "In many applications, it is desirable to extract only the relevant information from complex input data, which involves making a decision about which input features are relevant.\n The information bottleneck method formalizes this as an information-theoretic optimization problem by maintaining an optimal tradeoff between compression (throwing away irrelevant input information), and predicting the target. In many problem settings, including the reinforcement learning problems we consider in this work, we might prefer to compress only part of the input. This is typically the case when we have a standard conditioning input, such as a state observation, and a ``privileged'' input, which might correspond to the goal of a task, the output of a costly planning algorithm, or communication with another agent. In such cases, we might prefer to compress the privileged input, either to achieve better generalization (e.g., with respect to goals) or to minimize access to costly information (e.g., in the case of communication). Practical implementations of the information bottleneck based on variational inference require access to the privileged input in order to compute the bottleneck variable, so although they perform compression, this compression operation itself needs unrestricted, lossless access. In this work, we propose the variational bandwidth bottleneck, which decides for each example on the estimated value of the privileged information before seeing it, i.e., only based on the standard input, and then accordingly chooses stochastically, whether to access the privileged input or not. We formulate a tractable approximation to this framework and demonstrate in a series of reinforcement learning experiments that it can improve generalization and reduce access to computationally costly information.", "output": [ "Training agents with adaptive computation based on information bottleneck can promote generalization. " ] }, { "id": "task668-411bf04923394537b84fe137815dcb9b", "input": "Zeroth-order optimization is the process of minimizing an objective $f(x)$, given oracle access to evaluations at adaptively chosen inputs $x$. In this paper, we present two simple yet powerful GradientLess Descent (GLD) algorithms that do not rely on an underlying gradient estimate and are numerically stable. We analyze our algorithm from a novel geometric perspective and we show that for {\\it any monotone transform} of a smooth and strongly convex objective with latent dimension $k \\ge n$, we present a novel analysis that shows convergence within an $\\epsilon$-ball of the optimum in $O(kQ\\log(n)\\log(R/\\epsilon))$ evaluations, where the input dimension is $n$, $R$ is the diameter of the input space and $Q$ is the condition number. Our rates are the first of its kind to be both 1) poly-logarithmically dependent on dimensionality and 2) invariant under monotone transformations. We further leverage our geometric perspective to show that our analysis is optimal. Both monotone invariance and its ability to utilize a low latent dimensionality are key to the empirical success of our algorithms, as demonstrated on synthetic and MuJoCo benchmarks.\n", "output": [ "Gradientless Descent is a provably efficient gradient-free algorithm that is monotone-invariant and fast for high-dimensional zero-th order optimization.", "This paper proposes stable GradientLess Descent (GLD) algorithms that do not rely on gradient estimate." ] }, { "id": "task668-94661b4c0c944ee2bd7f23a4d2c1121b", "input": "Generative Adversarial Networks have made data generation possible in various use cases, but in case of complex, high-dimensional distributions it can be difficult to train them, because of convergence problems and the appearance of mode collapse.\n Sliced Wasserstein GANs and especially the application of the Max-Sliced Wasserstein distance made it possible to approximate Wasserstein distance during training in an efficient and stable way and helped ease convergence problems of these architectures.\n\n This method transforms sample assignment and distance calculation into sorting the one-dimensional projection of the samples, which results a sufficient approximation of the high-dimensional Wasserstein distance. \n\n In this paper we will demonstrate that the approximation of the Wasserstein distance by sorting the samples is not always the optimal approach and the greedy assignment of the real and fake samples can result faster convergence and better approximation of the original distribution.", "output": [ "We apply a greedy assignment on the projected samples instead of sorting to approximate Wasserstein distance" ] }, { "id": "task668-a0b790851cb2478792ce7affd44ea77e", "input": "Prepositions are among the most frequent words. Good prepositional representation is of great syntactic and semantic interest in computational linguistics. Existing methods on preposition representation either treat prepositions as content words (e.g., word2vec and GloVe) or depend heavily on external linguistic resources including syntactic parsing, training task and dataset-specific representations. In this paper we use word-triple counts (one of the words is a preposition) to capture the preposition's interaction with its head and children. Prepositional embeddings are derived via tensor decompositions on a large unlabeled corpus. We reveal a new geometry involving Hadamard products and empirically demonstrate its utility in paraphrasing of phrasal verbs. Furthermore, our prepositional embeddings are used as simple features to two challenging downstream tasks: preposition selection and prepositional attachment disambiguation. We achieve comparable to or better results than state of the art on multiple standardized datasets. ", "output": [ "This work is about tensor-based method for preposition representation training." ] }, { "id": "task668-79c0775cd0284b7c916a37e022991d41", "input": "Hyperparameter tuning is one of the most time-consuming workloads in deep learning. State-of-the-art optimizers, such as AdaGrad, RMSProp and Adam, reduce this labor by adaptively tuning an individual learning rate for each variable. Recently researchers have shown renewed interest in simpler methods like momentum SGD as they may yield better results. Motivated by this trend, we ask: can simple adaptive methods, based on SGD perform as well or better? We revisit the momentum SGD algorithm and show that hand-tuning a single learning rate and momentum makes it competitive with Adam. We then analyze its robustness to learning rate misspecification and objective curvature variation. Based on these insights, we design YellowFin, an automatic tuner for momentum and learning rate in SGD. YellowFin optionally uses a negative-feedback loop to compensate for the momentum dynamics in asynchronous settings on the fly. We empirically show YellowFin can converge in fewer iterations than Adam on ResNets and LSTMs for image recognition, language modeling and constituency parsing, with a speedup of up to $3.28$x in synchronous and up to $2.69$x in asynchronous settings.", "output": [ "YellowFin is an SGD based optimizer with both momentum and learning rate adaptivity.", "Proposes a method to automatically tuning the momentum parameter in momentum SGD methods, which achieves better results and fast convergence speed that state-of-the-art Adam algorithm." ] }, { "id": "task668-78ab652839f74ff5a10467bd9ebd77e6", "input": "We introduce a hierarchical model for efficient placement of computational graphs onto hardware devices, especially in heterogeneous environments with a mixture of CPUs, GPUs, and other computational devices. Our method learns to assign graph operations to groups and to allocate those groups to available devices. The grouping and device allocations are learned jointly. The proposed method is trained with policy gradient and requires no human intervention. Experiments with widely-used\n computer vision and natural language models show that our algorithm can find optimized, non-trivial placements for TensorFlow computational graphs with over 80,000 operations. In addition, our approach outperforms placements by human\n experts as well as a previous state-of-the-art placement method based on deep reinforcement learning. Our method achieves runtime reductions of up to 60.6% per training step when applied to models such as Neural Machine Translation.", "output": [ "We introduce a hierarchical model for efficient, end-to-end placement of computational graphs onto hardware devices.", "Proposes to jointly learn groups of operators to colocate and to place learned groups on devices to distribute operations for deep learning via reinforcement learning.", "The authors purpose a fully connect network to replace the co-location step in an auto-placement method proposed to accelerate a TensorFlow model's runtime.", "Proposes a device placement algorithm to place operations of tensorflow on devices." ] }, { "id": "task668-99a66b95518a40b29e7b3d261ba9d060", "input": "We present a new methodology that constructs a family of \\emph{positive definite kernels} from any given dissimilarity measure on structured inputs whose elements are either real-valued time series or discrete structures such as strings, histograms, and graphs. \n Our approach, which we call D2KE (from Distance to Kernel and Embedding), draws from the literature of Random Features.\n However, instead of deriving random feature maps from a user-defined kernel to approximate kernel machines, we build a kernel from a random feature map, that we specify given the distance measure. \n We further propose use of a finite number of random objects to produce a random feature embedding of each instance.\n We provide a theoretical analysis showing that D2KE enjoys better generalizability than universal Nearest-Neighbor estimates. \n On one hand, D2KE subsumes the widely-used \\emph{representative-set method} as a special case, and relates to the well-known \\emph{distance substitution kernel} in a limiting case. \n On the other hand, D2KE generalizes existing \\emph{Random Features methods} applicable only to vector input representations to complex structured inputs of variable sizes. \n We conduct classification experiments over such disparate domains as time series, strings, and histograms (for texts and images), for which our proposed framework compares favorably to existing distance-based learning methods in terms of both testing accuracy and computational time.", "output": [ "From Distance to Kernel and Embedding via Random Features For Structured Inputs" ] }, { "id": "task668-8f150768db7547859cf1c1bba17175db", "input": "This paper proposes and demonstrates a surprising pattern in the training of neural networks: there is a one to one relation between the values of any pair of losses (such as cross entropy, mean squared error, 0/1 error etc.) evaluated for a model arising at (any point of) a training run. This pattern is universal in the sense that this one to one relationship is identical across architectures (such as VGG, Resnet, Densenet etc.), algorithms (SGD and SGD with momentum) and training loss functions (cross entropy and mean squared error).", "output": [ "We identify some universal patterns (i.e., holding across architectures) in the behavior of different surrogate losses (CE, MSE, 0-1 loss) while training neural networks and present supporting empirical evidence." ] }, { "id": "task668-d4d73a9066da4bdc9aab811df699330d", "input": "Social dilemmas are situations where individuals face a temptation to increase their payoffs at a cost to total welfare. Building artificially intelligent agents that achieve good outcomes in these situations is important because many real world interactions include a tension between selfish interests and the welfare of others. We show how to modify modern reinforcement learning methods to construct agents that act in ways that are simple to understand, nice (begin by cooperating), provokable (try to avoid being exploited), and forgiving (try to return to mutual cooperation). We show both theoretically and experimentally that such agents can maintain cooperation in Markov social dilemmas. Our construction does not require training methods beyond a modification of self-play, thus if an environment is such that good strategies can be constructed in the zero-sum case (eg. Atari) then we can construct agents that solve social dilemmas in this environment.", "output": [ "How can we build artificial agents that solve social dilemmas (situations where individuals face a temptation to increase their payoffs at a cost to total welfare)?" ] }, { "id": "task668-39f77c3dbc8744bab2db7af9a9093af6", "input": "Transfer and adaptation to new unknown environmental dynamics is a key challenge for reinforcement learning (RL). An even greater challenge is performing near-optimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience rollouts for adaptation. To achieve single episode transfer in a family of environments with related dynamics, we propose a general algorithm that optimizes a probe and an inference model to rapidly estimate underlying latent variables of test dynamics, which are then immediately used as input to a universal control policy. This modular approach enables integration of state-of-the-art algorithms for variational inference or RL. Moreover, our approach does not require access to rewards at test time, allowing it to perform in settings where existing adaptive approaches cannot. In diverse experimental domains with a single episode test constraint, our method significantly outperforms existing adaptive approaches and shows favorable performance against baselines for robust transfer.", "output": [ "Single episode policy transfer in a family of environments with related dynamics, via optimized probing for rapid inference of latent variables and immediate execution of a universal policy." ] }, { "id": "task668-ec2f46153be94d88aa9895302f3fd3aa", "input": "Consistently checking the statistical significance of experimental results is the first mandatory step towards reproducible science. This paper presents a hitchhiker's guide to rigorous comparisons of reinforcement learning algorithms. After introducing the concepts of statistical testing, we review the relevant statistical tests and compare them empirically in terms of false positive rate and statistical power as a function of the sample size (number of seeds) and effect size. We further investigate the robustness of these tests to violations of the most common hypotheses (normal distributions, same distributions, equal variances). Beside simulations, we compare empirical distributions obtained by running Soft-Actor Critic and Twin-Delayed Deep Deterministic Policy Gradient on Half-Cheetah. We conclude by providing guidelines and code to perform rigorous comparisons of RL algorithm performances.", "output": [ "This paper compares statistical tests for RL comparisons (false positive, statistical power), checks robustness to assumptions using simulated distributions and empirical distributions (SAC, TD3), provides guidelines for RL students and researchers." ] }, { "id": "task668-6b9f02ab7cc540058471af23bfdb1de2", "input": "Semi-Supervised Learning (SSL) approaches have been an influential framework for the usage of unlabeled data when there is not a sufficient amount of labeled data available over the course of training. SSL methods based on Convolutional Neural Networks (CNNs) have recently provided successful results on standard benchmark tasks such as image classification. In this work, we consider the general setting of SSL problem where the labeled and unlabeled data come from the same underlying probability distribution. We propose a new approach that adopts an Optimal Transport (OT) technique serving as a metric of similarity between discrete empirical probability measures to provide pseudo-labels for the unlabeled data, which can then be used in conjunction with the initial labeled data to train the CNN model in an SSL manner. We have evaluated and compared our proposed method with state-of-the-art SSL algorithms on standard datasets to demonstrate the superiority and effectiveness of our SSL algorithm.", "output": [ "We propose a new algorithm based on the optimal transport to train a CNN in an SSL fashion." ] }, { "id": "task668-82f40561c4884aaa9945b507b3e4765c", "input": "Randomly initialized first-order optimization algorithms are the method of choice for solving many high-dimensional nonconvex problems in machine learning, yet general theoretical guarantees cannot rule out convergence to critical points of poor objective value. For some highly structured nonconvex problems however, the success of gradient descent can be understood by studying the geometry of the objective. We study one such problem -- complete orthogonal dictionary learning, and provide converge guarantees for randomly initialized gradient descent to the neighborhood of a global optimum. The resulting rates scale as low order polynomials in the dimension even though the objective possesses an exponential number of saddle points. This efficient convergence can be viewed as a consequence of negative curvature normal to the stable manifolds associated with saddle points, and we provide evidence that this feature is shared by other nonconvex problems of importance as well.", "output": [ "We provide an efficient convergence rate for gradient descent on the complete orthogonal dictionary learning objective based on a geometric analysis." ] }, { "id": "task668-29b0b19d925a49c98f12955bc6daa79e", "input": "In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks.\n Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by 80\\%.\n Secondly, we demonstrate how a novel kind of posterior approximation yields further improvements to the performance of Bayesian RNNs. We incorporate local gradient information into the approximate posterior to sharpen it around the current batch statistics. We show how this technique is not exclusive to recurrent neural networks and can be applied more widely to train Bayesian neural networks.\n We also empirically demonstrate how Bayesian RNNs are superior to traditional RNNs on a language modelling benchmark and an image captioning task, as well as showing how each of these methods improve our model over a variety of other schemes for training them. We also introduce a new benchmark for studying uncertainty for language models so future methods can be easily compared.", "output": [ " Variational Bayes scheme for Recurrent Neural Networks" ] }, { "id": "task668-cb20f78328544cbd93e4ffe8bccaf10a", "input": "Graph Neural Networks as a combination of Graph Signal Processing and Deep Convolutional Networks shows great power in pattern recognition in non-Euclidean domains. In this paper, we propose a new method to deploy two pipelines based on the duality of a graph to improve accuracy. By exploring the primal graph and its dual graph where nodes and edges can be treated as one another, we have exploited the benefits of both vertex features and edge features. As a result, we have arrived at a framework that has great potential in both semisupervised and unsupervised learning.", "output": [ "A primal dual graph neural network model for semi-supervised learning" ] }, { "id": "task668-4fb0cec311284722a4c4f914d6644381", "input": "Exploration is a key component of successful reinforcement learning, but optimal approaches are computationally intractable, so researchers have focused on hand-designing mechanisms based on exploration bonuses and intrinsic reward, some inspired by curious behavior in natural systems. In this work, we propose a strategy for encoding curiosity algorithms as programs in a domain-specific language and searching, during a meta-learning phase, for algorithms that enable RL agents to perform well in new domains. Our rich language of programs, which can combine neural networks with other building blocks including nearest-neighbor modules and can choose its own loss functions, enables the expression of highly generalizable programs that perform well in domains as disparate as grid navigation with image input, acrobot, lunar lander, ant and hopper. To make this approach feasible, we develop several pruning techniques, including learning to predict a program's success based on its syntactic properties. We demonstrate the effectiveness of the approach empirically, finding curiosity strategies that are similar to those in published literature, as well as novel strategies that are competitive with them and generalize well.", "output": [ "Meta-learning curiosity algorithms by searching through a rich space of programs yields novel mechanisms that generalize across very different reinforcement-learning domains." ] }, { "id": "task668-e488763f3f8343c18bfe02115c42ed34", "input": "Generative Adversarial Networks (GANs) can produce images of surprising complexity and realism, but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene. Capturing such complex interactions between different objects in the world, including their relative scaling, spatial layout, occlusion, or viewpoint transformation is a challenging problem. In this work, we propose to model object composition in a GAN framework as a self-consistent composition-decomposition network. Our model is conditioned on the object images from their marginal distributions and can generate a realistic image from their joint distribution. We evaluate our model through qualitative experiments and user evaluations in scenarios when either paired or unpaired examples for the individual object images and the joint scenes are given during training. Our results reveal that the learned model captures potential interactions between the two object domains given as input to output new instances of composed scene at test time in a reasonable fashion.", "output": [ "We develop a novel approach to model object compositionality in images in a GAN framework." ] }, { "id": "task668-5c9f2bbae09145cabb95bd5b6318d287", "input": "The available resolution in our visual world is extremely high, if not infinite. Existing CNNs can be applied in a fully convolutional way to images of arbitrary resolution, but as the size of the input increases, they can not capture contextual information. In addition, computational requirements scale linearly to the number of input pixels, and resources are allocated uniformly across the input, no matter how informative different image regions are. We attempt to address these problems by proposing a novel architecture that traverses an image pyramid in a top-down fashion, while it uses a hard attention mechanism to selectively process only the most informative image parts. We conduct experiments on MNIST and ImageNet datasets, and we show that our models can significantly outperform fully convolutional counterparts, when the resolution of the input is that big that the receptive field of the baselines can not adequately cover the objects of interest. Gains in performance come for less FLOPs, because of the selective processing that we follow. Furthermore, our attention mechanism makes our predictions more interpretable, and creates a trade-off between accuracy and complexity that can be tuned both during training and testing time.", "output": [ "We propose a novel architecture that traverses an image pyramid in a top-down fashion, while it visits only the most informative regions along the way." ] }, { "id": "task668-8b1296462d544cce8f739365dd0ba210", "input": "For fast and energy-efficient deployment of trained deep neural networks on resource-constrained embedded hardware, each learned weight parameter should ideally be represented and stored using a single bit. Error-rates usually increase when this requirement is imposed. Here, we report large improvements in error rates on multiple datasets, for deep convolutional neural networks deployed with 1-bit-per-weight. Using wide residual networks as our main baseline, our approach simplifies existing methods that binarize weights by applying the sign function in training; we apply scaling factors for each layer with constant unlearned values equal to the layer-specific standard deviations used for initialization. For CIFAR-10, CIFAR-100 and ImageNet, and models with 1-bit-per-weight requiring less than 10 MB of parameter memory, we achieve error rates of 3.9%, 18.5% and 26.0% / 8.5% (Top-1 / Top-5) respectively. We also considered MNIST, SVHN and ImageNet32, achieving 1-bit-per-weight test results of 0.27%, 1.9%, and 41.3% / 19.1% respectively. For CIFAR, our error rates halve previously reported values, and are within about 1% of our error-rates for the same network with full-precision weights. For networks that overfit, we also show significant improvements in error rate by not learning batch normalization scale and offset parameters. This applies to both full precision and 1-bit-per-weight networks. Using a warm-restart learning-rate schedule, we found that training for 1-bit-per-weight is just as fast as full-precision networks, with better accuracy than standard schedules, and achieved about 98%-99% of peak performance in just 62 training epochs for CIFAR-10/100. For full training code and trained models in MATLAB, Keras and PyTorch see https://github.com/McDonnell-Lab/1-bit-per-weight/ .", "output": [ "We train wide residual networks that can be immediately deployed using only a single bit for each convolutional weight, with signficantly better accuracy than past methods." ] }, { "id": "task668-f8f7c333c3d647a3bbd28d09d5494c81", "input": "Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data. In this paper, we first show that a straightforward extension of an existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data. We propose a two fold modification to a GAN algorithm to be able to generate point clouds (PC-GAN). First, we combine ideas from hierarchical Bayesian modeling and implicit generative models by learning a hierarchical and interpretable sampling process. A key component of our method is that we train a posterior inference network for the hidden variables. Second, PC-GAN defines a generic framework that can incorporate many existing GAN algorithms. We further propose a sandwiching objective, which results in a tighter Wasserstein distance estimate than the commonly used dual form in WGAN. We validate our claims on the ModelNet40 benchmark dataset and observe that PC- GAN trained by the sandwiching objective achieves better results on test data than existing methods. We also conduct studies on several tasks, including generalization on unseen point clouds, latent space interpolation, classification, and image to point clouds transformation, to demonstrate the versatility of the proposed PC-GAN algorithm.", "output": [ "We propose a GAN variant which learns to generate point clouds. Different studies have been explores, including tighter Wasserstein distance estimate, conditional generation, generalization to unseen point clouds and image to point cloud.", "This paper proposes using GAN to generate 3D point cloud and introduces a sandwiching objective, averaging the upper and lower bound of Wasserstein distance between distributions.", "This paper proposes a new generative model for unordered data, with a particular application to point clouds, which includes an inference method and a novel objective function. " ] }, { "id": "task668-2716e64cf36a49b3a562bbf96e22dc3d", "input": "We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task. We use this task to compare a series of architectures which are ubiquitous in the sequence-processing literature, in addition to a new model class---PossibleWorldNets---which computes entailment as a ``convolution over possible worlds''. Results show that convolutional networks present the wrong inductive bias for this class of problems relative to LSTM RNNs, tree-structured neural networks outperform LSTM RNNs due to their enhanced ability to exploit the syntax of logic, and PossibleWorldNets outperform all benchmarks.", "output": [ "We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task.", "The paper proposes a new model to use deep models for detecting logical entailment as a product of continuous functions over possible worlds.", "Proposes a new model designed for machine learning with predicting logical entailment." ] }, { "id": "task668-e5ae549366ef483d8e5c7875a03f19e4", "input": "For AI systems to garner widespread public acceptance, we must develop methods capable of explaining the decisions of black-box models such as neural networks. In this work, we identify two issues of current explanatory methods. First, we show that two prevalent perspectives on explanations—feature-additivity and feature-selection—lead to fundamentally different instance-wise explanations. In the literature, explainers from different perspectives are currently being directly compared, despite their distinct explanation goals. The second issue is that current post-hoc explainers have only been thoroughly validated on simple models, such as linear regression, and, when applied to real-world neural networks, explainers are commonly evaluated under the assumption that the learned models behave reasonably. However, neural networks often rely on unreasonable correlations, even when producing correct decisions. We introduce a verification framework for explanatory methods under the feature-selection perspective. Our framework is based on a non-trivial neural network architecture trained on a real-world task, and for which we are able to provide guarantees on its inner workings. We validate the efficacy of our evaluation by showing the failure modes of current explainers. We aim for this framework to provide a publicly available,1 off-the-shelf evaluation when the feature-selection perspective on explanations is needed.", "output": [ "An evaluation framework based on a real-world neural network for post-hoc explanatory methods" ] }, { "id": "task668-0f77889512804be6aee735f823316f1a", "input": "Recent work has shown that performing inference with fast, very-low-bitwidth\n (e.g., 1 to 2 bits) representations of values in models can yield surprisingly accurate\n results. However, although 2-bit approximated networks have been shown to\n be quite accurate, 1 bit approximations, which are twice as fast, have restrictively\n low accuracy. We propose a method to train models whose weights are a mixture\n of bitwidths, that allows us to more finely tune the accuracy/speed trade-off. We\n present the “middle-out” criterion for determining the bitwidth for each value, and\n show how to integrate it into training models with a desired mixture of bitwidths.\n We evaluate several architectures and binarization techniques on the ImageNet\n dataset. We show that our heterogeneous bitwidth approximation achieves superlinear\n scaling of accuracy with bitwidth. Using an average of only 1.4 bits, we are\n able to outperform state-of-the-art 2-bit architectures.", "output": [ "We introduce fractional bitwidth approximation and show it has significant advantages.", "Suggests a method for varying the degree of quantization in a neural network during the forward propagation phase", "Maintaining the accuracy of 2bits netword while using less than 2bits weights" ] }, { "id": "task668-7e27537b4f114b9b88ef0911f58e0423", "input": "Recent efforts on training light-weight binary neural networks offer promising execution/memory efficiency. This paper introduces ResBinNet, which is a composition of two interlinked methodologies aiming to address the slow convergence speed and limited accuracy of binary convolutional neural networks. The first method, called residual binarization, learns a multi-level binary representation for the features within a certain neural network layer. The second method, called temperature adjustment, gradually binarizes the weights of a particular layer. The two methods jointly learn a set of soft-binarized parameters that improve the convergence rate and accuracy of binary neural networks. We corroborate the applicability and scalability of ResBinNet by implementing a prototype hardware accelerator. The accelerator is reconfigurable in terms of the numerical precision of the binarized features, offering a trade-off between runtime and inference accuracy.\n", "output": [ "Residual Binary Neural Networks significantly improve the convergence rate and inference accuracy of the binary neural networks." ] }, { "id": "task668-4ffda110134d46c88c44db6444ce6d5d", "input": "As for knowledge-based question answering, a fundamental problem is to relax the assumption of answerable questions from simple questions to compound questions. Traditional approaches firstly detect topic entity mentioned in questions, then traverse the knowledge graph to find relations as a multi-hop path to answers, while we propose a novel approach to leverage simple-question answerers to answer compound questions. Our model consists of two parts: (i) a novel learning-to-decompose agent that learns a policy to decompose a compound question into simple questions and (ii) three independent simple-question answerers that classify the corresponding relations for each simple question. Experiments demonstrate that our model learns complex rules of compositionality as stochastic policy, which benefits simple neural networks to achieve state-of-the-art results on WebQuestions and MetaQA. We analyze the interpretable decomposition process as well as generated partitions.", "output": [ "We propose a learning-to-decompose agent that helps simple-question answerers to answer compound question over knowledge graph." ] }, { "id": "task668-8fddddbe0726477d8fadfe761036ca90", "input": "We improve the robustness of deep neural nets to adversarial attacks by using an interpolating function as the output activation. This data-dependent activation function remarkably improves both classification accuracy and stability to adversarial perturbations. Together with the total variation minimization of adversarial images and augmented training, under the strongest attack, we achieve up to 20.6%, 50.7%, and 68.7% accuracy improvement w.r.t. the fast gradient sign method, iterative fast gradient sign method, and Carlini-WagnerL2attacks, respectively. Our defense strategy is additive to many of the existing methods. We give an intuitive explanation of our defense strategy via analyzing the geometry of the feature space. For reproducibility, the code will be available on GitHub.", "output": [ "We proposal strategies for adversarial defense based on data dependent activation function, total variation minimization, and training data augmentation" ] }, { "id": "task668-089b851bc45d4d36b5a46b0f7a9e1129", "input": "In representational lifelong learning an agent aims to continually learn to solve novel tasks while updating its representation in light of previous tasks. Under the assumption that future tasks are related to previous tasks, representations should be learned in such a way that they capture the common structure across learned tasks, while allowing the learner sufficient flexibility to adapt to novel aspects of a new task. We develop a framework for lifelong learning in deep neural networks that is based on generalization bounds, developed within the PAC-Bayes framework. Learning takes place through the construction of a distribution over networks based on the tasks seen so far, and its utilization for learning a new task. Thus, prior knowledge is incorporated through setting a history-dependent prior for novel tasks. We develop a gradient-based algorithm implementing these ideas, based on minimizing an objective function motivated by generalization bounds, and demonstrate its effectiveness through numerical examples.", "output": [ "We develop a lifelong learning approach to transfer learning based on PAC-Bayes theory, whereby priors are adjusted as new tasks are encountered thereby facilitating the learning of novel tasks.", "A novel PAC-Bayesian risk bound that serves as an objective function for multi-task machine learning, and an algorithm for minimizing a simplified version of that objective function.", "Extends existing PAC-Bayes bounds to multi-task learning, to allow the prior to be adapted across different tasks." ] }, { "id": "task668-40e6d8e8f23f477e88f8ed7ce065b809", "input": "We propose a new algorithm to learn a one-hidden-layer convolutional neural network where both the convolutional weights and the outputs weights are parameters to be learned. Our algorithm works for a general class of (potentially overlapping) patches, including commonly used structures for computer vision tasks. Our algorithm draws ideas from (1) isotonic regression for learning neural networks and (2) landscape analysis of non-convex matrix factorization problems. We believe these findings may inspire further development in designing provable algorithms for learning neural networks and other complex models. While our focus is theoretical, we also present experiments that illustrate our theoretical findings.", "output": [ "We propose an algorithm for provably recovering parameters (convolutional and output weights) of a convolutional network with overlapping patches.", "This paper studies the theoretical learning of one-hidden-layer convolutional neural nets, resulting in a learning algorithm and provable guarantees using the algorithm.", "This paper gives a new algorithm for learning a two layer neural network which involves a single convolutional filter and a weight vector for different locations." ] }, { "id": "task668-7f2622a83d7647269ec6785054ce98e2", "input": "Influence diagrams provide a modeling and inference framework for sequential decision problems, representing the probabilistic knowledge by a Bayesian network and the preferences of an agent by utility functions over the random variables and decision variables.\n MDPs and POMDPS, widely used for planning under uncertainty can also be represented by influence diagrams.\n The time and space complexity of computing the maximum expected utility (MEU) and its maximizing policy is exponential in the induced width of the underlying graphical model, which is often prohibitively large due to the growth of the information set under the sequence of decisions.\n In this paper, we develop a weighted mini-bucket approach for bounding the MEU. These bounds can be used as a stand-alone approximation that can be improved as a function of a controlling i-bound parameter .\nThey can also be used as heuristic functions to guide search, especially for planning \n such as MDPs and POMDPs.\n We evaluate the scheme empirically against state-of-the-art, thus illustrating its potential.\n", "output": [ "This paper introduces an elimination based heuristic function for sequential decision making, suitable for guiding AND/OR search algorithms for solving influence diagrams.", "generalizes minibuckets inference heuristic to influence diagrams." ] }, { "id": "task668-d5413ea516c94626b5c947df9fd2dce9", "input": "Adam is shown not being able to converge to the optimal solution in certain cases. Researchers recently propose several algorithms to avoid the issue of non-convergence of Adam, but their efficiency turns out to be unsatisfactory in practice. In this paper, we provide a new insight into the non-convergence issue of Adam as well as other adaptive learning rate methods. We argue that there exists an inappropriate correlation between gradient $g_t$ and the second moment term $v_t$ in Adam ($t$ is the timestep), which results in that a large gradient is likely to have small step size while a small gradient may have a large step size. We demonstrate that such unbalanced step sizes are the fundamental cause of non-convergence of Adam, and we further prove that decorrelating $v_t$ and $g_t$ will lead to unbiased step size for each gradient, thus solving the non-convergence problem of Adam. Finally, we propose AdaShift, a novel adaptive learning rate method that decorrelates $v_t$ and $g_t$ by temporal shifting, i.e., using temporally shifted gradient $g_{t-n}$ to calculate $v_t$. The experiment results demonstrate that AdaShift is able to address the non-convergence issue of Adam, while still maintaining a competitive performance with Adam in terms of both training speed and generalization.", "output": [ "We analysis and solve the non-convergence issue of Adam." ] }, { "id": "task668-c5644f46eaae489298d7ba1500535fef", "input": "The allocation of computation resources in the backbone is a crucial issue in object detection. However, classification allocation pattern is usually adopted directly to object detector, which is proved to be sub-optimal. In order to reallocate the engaged computation resources in a more efficient way, we present CR-NAS (Computation Reallocation Neural Architecture Search) that can learn computation reallocation strategies across different feature resolution and spatial position diectly on the target detection dataset. A two-level reallocation space is proposed for both stage and spatial reallocation. A novel hierarchical search procedure is adopted to cope with the complex search space. We apply CR-NAS to multiple backbones and achieve consistent improvements. Our CR-ResNet50 and CR-MobileNetV2 outperforms the baseline by 1.9% and 1.7% COCO AP respectively without any additional computation budget. The models discovered by CR-NAS can be equiped to other powerful detection neck/head and be easily transferred to other dataset, e.g. PASCAL VOC, and other vision tasks, e.g. instance segmentation. Our CR-NAS can be used as a plugin to improve the performance of various networks, which is demanding.", "output": [ "We propose CR-NAS to reallocate engaged computation resources in different resolution and spatial position." ] }, { "id": "task668-f8f0c0de6a8c46c393adb27a4db09e40", "input": "A framework for efficient Bayesian inference in probabilistic programs is introduced by embedding a sampler inside a variational posterior approximation. Its strength lies in both ease of implementation and automatically tuning sampler parameters to speed up mixing time. Several strategies to approximate the evidence lower bound (ELBO) computation are introduced, including a rewriting of the ELBO objective. Experimental evidence is shown by performing experiments on an unconditional VAE on density estimation tasks; solving an influence diagram in a high-dimensional space with a conditional variational autoencoder (cVAE) as a deep Bayes classifier; and state-space models for time-series data.", "output": [ "We embed SG-MCMC samplers inside a variational approximation" ] }, { "id": "task668-3f5d98f7917e4691a15dc9e1aab125db", "input": "Deep artificial neural networks can achieve an extremely small difference between training and test accuracies on identically distributed training and test sets, which is a standard measure of generalization. However, the training and test sets may not be sufficiently representative of the empirical sample set, which consists of real-world input samples. When samples are drawn from an underrepresented or unrepresented subset during inference, the gap between the training and inference accuracies can be significant. To address this problem, we first reformulate a classification algorithm as a procedure for searching for a source code that maps input features to classes. We then derive a necessary and sufficient condition for generalization using a universal cognitive similarity metric, namely information distance, based on Kolmogorov complexity. Using this condition, we formulate an optimization problem to learn a more general classification function. To achieve this end, we extend the input features by concatenating encodings of them, and then train the classifier on the extended features. As an illustration of this idea, we focus on image classification, where we use channel codes on the input features as a systematic way to improve the degree to which the training and test sets are representative of the empirical sample set. To showcase our theoretical findings, considering that corrupted or perturbed input features belong to the empirical sample set, but typically not to the training and test sets, we demonstrate through extensive systematic experiments that, as a result of learning a more general classification function, a model trained on encoded input features is significantly more robust to common corruptions, e.g., Gaussian and shot noise, as well as adversarial perturbations, e.g., those found via projected gradient descent, than the model trained on uncoded input features.", "output": [ "We present a theoretical and experimental framework for defining, understanding, and achieving generalization, and as a result robustness, in deep learning by drawing on algorithmic information theory and coding theory." ] }, { "id": "task668-e5f18b9f119c4db1b567d95ed1563f87", "input": "Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the efficiency of learning on a novel task. This approach encounters difficulty when transfer is not mutually beneficial, for instance, when tasks are sufficiently dissimilar or change over time. Here, we use the connection between gradient-based meta-learning and hierarchical Bayes to propose a mixture of hierarchical Bayesian models over the parameters of an arbitrary function approximator such as a neural network. Generalizing the model-agnostic meta-learning (MAML) algorithm, we present a stochastic expectation maximization procedure to jointly estimate parameter initializations for gradient descent as well as a latent assignment of tasks to initializations. This approach better captures the diversity of training tasks as opposed to consolidating inductive biases into a single set of hyperparameters. Our experiments demonstrate better generalization on the standard miniImageNet benchmark for 1-shot classification. We further derive a novel and scalable non-parametric variant of our method that captures the evolution of a task distribution over time as demonstrated on a set of few-shot regression tasks.", "output": [ "We use the connection between gradient-based meta-learning and hierarchical Bayes to learn a mixture of meta-learners that is appropriate for a heterogeneous and evolving task distribution." ] }, { "id": "task668-3e1eb555e6464e53845269c3c010794e", "input": "We present Merged-Averaged Classifiers via Hashing (MACH) for $K$-classification with large $K$. Compared to traditional one-vs-all classifiers that require $O(Kd)$ memory and inference cost, MACH only need $O(d\\log{K})$ memory while only requiring $O(K\\log{K} + d\\log{K})$ operation for inference. MACH is the first generic $K$-classification algorithm, with provably theoretical guarantees, which requires $O(\\log{K})$ memory without any assumption on the relationship between classes. MACH uses universal hashing to reduce classification with a large number of classes to few independent classification task with very small (constant) number of classes. We provide theoretical quantification of accuracy-memory tradeoff by showing the first connection between extreme classification and heavy hitters. With MACH we can train ODP dataset with 100,000 classes and 400,000 features on a single Titan X GPU (12GB), with the classification accuracy of 19.28\\%, which is the best-reported accuracy on this dataset. Before this work, the best performing baseline is a one-vs-all classifier that requires 40 billion parameters (320 GB model size) and achieves 9\\% accuracy. In contrast, MACH can achieve 9\\% accuracy with 480x reduction in the model size (of mere 0.6GB). With MACH, we also demonstrate complete training of fine-grained imagenet dataset (compressed size 104GB), with 21,000 classes, on a single GPU.", "output": [ "How to Training 100,000 classes on a single GPU", "Proposes an efficient hashing method MACH for softmax approximation in the context of large output space, which saves both memory and computation.", "A method for classification scheme for problems involving a large number of classes in a multi-class setting demonstrated on ODP and Imagenet-21K datasets", "The paper presents a hashing based scheme for reducing memory and computation time for K-way classification when K is large" ] }, { "id": "task668-6b79a7fd742441c6a365c5991f97855d", "input": "Recent pretrained sentence encoders achieve state of the art results on language understanding tasks, but does this mean they have implicit knowledge of syntactic structures? We introduce a grammatically annotated development set for the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018), which we use to investigate the grammatical knowledge of three pretrained encoders, including the popular OpenAI Transformer (Radford et al., 2018) and BERT (Devlin et al., 2018). We fine-tune these encoders to do acceptability classification over CoLA and compare the models’ performance on the annotated analysis set. Some phenomena, e.g. modification by adjuncts, are easy to learn for all models, while others, e.g. long-distance movement, are learned effectively only by models with strong overall performance, and others still, e.g. morphological agreement, are hardly learned by any model.", "output": [ "We investigate the implicit syntactic knowledge of sentence embeddings using a new analysis set of grammatically annotated sentences with acceptability judgments." ] }, { "id": "task668-10cf4788bd554b3c9d3ac1d4cf0cb868", "input": "We present a large-scale empirical study of catastrophic forgetting (CF) in modern Deep Neural Network (DNN) models that perform sequential (or: incremental) learning.\n A new experimental protocol is proposed that takes into account typical constraints encountered in application scenarios.\n As the investigation is empirical, we evaluate CF behavior on the hitherto largest number of visual classification datasets, from each of which we construct a representative number of Sequential Learning Tasks (SLTs) in close alignment to previous works on CF.\n Our results clearly indicate that there is no model that avoids CF for all investigated datasets and SLTs under application conditions. We conclude with a discussion of potential solutions and workarounds to CF, notably for the EWC and IMM models.", "output": [ "We check DNN models for catastrophic forgetting using a new evaluation scheme that reflects typical application conditions, with surprising results." ] }, { "id": "task668-8d6eb84b61f242f39e169d5eb3631c58", "input": "Disentangling factors of variation has always been a challenging problem in representation learning. Existing algorithms suffer from many limitations, such as unpredictable disentangling factors, bad quality of generated images from encodings, lack of identity information, etc. In this paper, we proposed a supervised algorithm called DNA-GAN trying to disentangle different attributes of images. The latent representations of images are DNA-like, in which each individual piece represents an independent factor of variation. By annihilating the recessive piece and swapping a certain piece of two latent representations, we obtain another two different representations which could be decoded into images. In order to obtain realistic images and also disentangled representations, we introduced the discriminator for adversarial training. Experiments on Multi-PIE and CelebA datasets demonstrate the effectiveness of our method and the advantage of overcoming limitations existing in other methods.", "output": [ "We proposed a supervised algorithm, DNA-GAN, to disentangle multiple attributes of images.", "This paper investigates the problem of attribute-conditioned image generation using generative adversarial networks, and proposes to generate images from attribute and latent code as high-level representation.", "This paper proposed a new method to disentangle different attributes of images using a novel DNA structure GAN" ] }, { "id": "task668-2987b05e0bcb4e54af00dc5eeebcd02d", "input": "Partial differential equations (PDEs) play a prominent role in many disciplines such as applied mathematics, physics, chemistry, material science, computer science, etc. PDEs are commonly derived based on physical laws or empirical observations. However, the governing equations for many complex systems in modern applications are still not fully known. With the rapid development of sensors, computational power, and data storage in the past decade, huge quantities of data can be easily collected and efficiently stored. Such vast quantity of data offers new opportunities for data-driven discovery of hidden physical laws. Inspired by the latest development of neural network designs in deep learning, we propose a new feed-forward deep network, called PDE-Net, to fulfill two objectives at the same time: to accurately predict dynamics of complex systems and to uncover the underlying hidden PDE models. The basic idea of the proposed PDE-Net is to learn differential operators by learning convolution kernels (filters), and apply neural networks or other machine learning methods to approximate the unknown nonlinear responses. Comparing with existing approaches, which either assume the form of the nonlinear response is known or fix certain finite difference approximations of differential operators, our approach has the most flexibility by learning both differential operators and the nonlinear responses. A special feature of the proposed PDE-Net is that all filters are properly constrained, which enables us to easily identify the governing PDE models while still maintaining the expressive and predictive power of the network. These constrains are carefully designed by fully exploiting the relation between the orders of differential operators and the orders of sum rules of filters (an important concept originated from wavelet theory). We also discuss relations of the PDE-Net with some existing networks in computer vision such as Network-In-Network (NIN) and Residual Neural Network (ResNet). Numerical experiments show that the PDE-Net has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.", "output": [ "This paper proposes a new feed-forward network, call PDE-Net, to learn PDEs from data. ", "The paper expores the use of deep learning machinery for the purpose of identifying dynamical systems specified by PDEs.", "The paper proposes a neural network based algorithm for learning from data that arises from dynamical systems with governing equations that can be written as partial differential equations.", "This paper addresses complex dynamical systems modelling through nonparametric Partial Differential Equations using neural architectures, with the most important idea of the papier (PDE-net) to learn both differential operators and the function that governs the PDE." ] }, { "id": "task668-3fa7caf0397a478aa0f11350ff8452e5", "input": "Intrinsic rewards in reinforcement learning provide a powerful algorithmic capability for agents to learn how to interact with their environment in a task-generic way. However, increased incentives for motivation can come at the cost of increased fragility to stochasticity. We introduce a method for computing an intrinsic reward for curiosity using metrics derived from sampling a latent variable model used to estimate dynamics. Ultimately, an estimate of the conditional probability of observed states is used as our intrinsic reward for curiosity. In our experiments, a video game agent uses our model to autonomously learn how to play Atari games using our curiosity reward in combination with extrinsic rewards from the game to achieve improved performance on games with sparse extrinsic rewards. When stochasticity is introduced in the environment, our method still demonstrates improved performance over the baseline.", "output": [ "We introduce a method for computing an intrinsic reward for curiosity using metrics derived from sampling a latent variable model used to estimate dynamics." ] }, { "id": "task668-8f8414342c554890b1236e266e233c50", "input": " With the proliferation of models for natural language processing (NLP) tasks, it is even harder to understand the differences between models and their relative merits. Simply looking at differences between holistic metrics such as accuracy, BLEU, or F1 do not tell us \\emph{why} or \\emph{how} a particular method is better and how dataset biases influence the choices of model design.\n In this paper, we present a general methodology for {\\emph{interpretable}} evaluation of NLP systems and choose the task of named entity recognition (NER) as a case study, which is a core task of identifying people, places, or organizations in text. The proposed evaluation method enables us to interpret the \\textit{model biases}, \\textit{dataset biases}, and how the \\emph{differences in the datasets} affect the design of the models, identifying the strengths and weaknesses of current approaches. By making our {analysis} tool available, we make it easy for future researchers to run similar analyses and drive the progress in this area.", "output": [ "We propose a generalized evaluation methodology to interpret model biases, dataset biases, and their correlation." ] }, { "id": "task668-a91778a3e8694076bc28c18869462e8d", "input": "We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES). Existing algorithms for MAML are based on policy gradients, and incur significant difficulties when attempting to estimate second derivatives using backpropagation on stochastic policies. We show how ES can be applied to MAML to obtain an algorithm which avoids the problem of estimating second derivatives, and is also conceptually simple and easy to implement. Moreover, ES-MAML can handle new types of nonsmooth adaptation operators, and other techniques for improving performance and estimation of ES methods become applicable. We show empirically that ES-MAML is competitive with existing methods and often yields better adaptation with fewer queries.", "output": [ "We provide a new framework for MAML in the ES/blackbox setting, and show that it allows deterministic and linear policies, better exploration, and non-differentiable adaptation operators." ] }, { "id": "task668-8e51bfd0c1d841bcb5e21339af6474f0", "input": "Graph neural networks have shown promising results on representing and analyzing diverse graph-structured data such as social, citation, and protein interaction networks. Existing approaches commonly suffer from the oversmoothing issue, regardless of whether policies are edge-based or node-based for neighborhood aggregation. Most methods also focus on transductive scenarios for fixed graphs, leading to poor generalization performance for unseen graphs. To address these issues, we propose a new graph neural network model that considers both edge-based neighborhood relationships and node-based entity features, i.e. Graph Entities with Step Mixture via random walk (GESM). GESM employs a mixture of various steps through random walk to alleviate the oversmoothing problem and attention to use node information explicitly. These two mechanisms allow for a weighted neighborhood aggregation which considers the properties of entities and relations. With intensive experiments, we show that the proposed GESM achieves state-of-the-art or comparable performances on four benchmark graph datasets comprising transductive and inductive learning tasks. Furthermore, we empirically demonstrate the significance of considering global information. The source code will be publicly available in the near future.", "output": [ "Simple and effective graph neural network with mixture of random walk steps and attention" ] }, { "id": "task668-769c6bd674b347fbba295422bc4f731f", "input": "Designing accurate and efficient convolutional neural architectures for vast amount of hardware is challenging because hardware designs are complex and diverse. This paper addresses the hardware diversity challenge in Neural Architecture Search (NAS). Unlike previous approaches that apply search algorithms on a small, human-designed search space without considering hardware diversity, we propose HURRICANE that explores the automatic hardware-aware search over a much larger search space and a multistep search scheme in coordinate ascent framework, to generate tailored models for different types of hardware. Extensive experiments on ImageNet show that our algorithm consistently achieves a much lower inference latency with a similar or better accuracy than state-of-the-art NAS methods on three types of hardware. Remarkably, HURRICANE achieves a 76.63% top-1 accuracy on ImageNet with a inference latency of only 16.5 ms for DSP, which is a 3.4% higher accuracy and a 6.35x inference speedup than FBNet-iPhoneX. For VPU, HURRICANE achieves a 0.53% higher top-1 accuracy than Proxyless-mobile with a 1.49x speedup. Even for well-studied mobile CPU, HURRICANE achieves a 1.63% higher top-1 accuracy than FBNet-iPhoneX with a comparable inference latency. HURRICANE also reduces the training time by 54.7% on average compared to SinglePath-Oneshot.", "output": [ "We propose HURRICANE to address the challenge of hardware diversity in one-shot neural architecture search" ] }, { "id": "task668-8b039fe9bff3434b848f562e0bd1bbd8", "input": "Which generative model is the most suitable for Continual Learning? This paper aims at evaluating and comparing generative models on disjoint sequential image generation tasks. We investigate how several models learn and forget, considering various strategies: rehearsal, regularization, generative replay and fine-tuning. We used two quantitative metrics to estimate the generation quality and memory ability. We experiment with sequential tasks on three commonly used benchmarks for Continual Learning (MNIST, Fashion MNIST and CIFAR10). We found that among all models, the original GAN performs best and among Continual Learning strategies, generative replay outperforms all other methods. Even if we found satisfactory combinations on MNIST and Fashion MNIST, training generative models sequentially on CIFAR10 is particularly instable, and remains a challenge.", "output": [ "A comparative study of generative models on Continual Learning scenarios." ] }, { "id": "task668-0982ac54de73463aaeda2f0d2c06617e", "input": "Small spacecraft now have precise attitude control systems available commercially, allowing them to slew in 3 degrees of freedom, and capture images within short notice. When combined with appropriate software, this agility can significantly increase response rate, revisit time and coverage. In prior work, we have demonstrated an algorithmic framework that combines orbital mechanics, attitude control and scheduling optimization to plan the time-varying, full-body orientation of agile, small spacecraft in a constellation. The proposed schedule optimization would run at the ground station autonomously, and the resultant schedules uplinked to the spacecraft for execution. The algorithm is generalizable over small steerable spacecraft, control capability, sensor specs, imaging requirements, and regions of interest. In this article, we modify the algorithm to run onboard small spacecraft, such that the constellation can make time-sensitive decisions to slew and capture images autonomously, without ground control. We have developed a communication module based on Delay/Disruption Tolerant Networking (DTN) for onboard data management and routing among the satellites, which will work in conjunction with the other modules to optimize the schedule of agile communication and steering. We then apply this preliminary framework on representative constellations to simulate targeted measurements of episodic precipitation events and subsequent urban floods. The command and control efficiency of our agile algorithm is compared to non-agile (11.3x improvement) and non-DTN (21% improvement) constellations.", "output": [ "We propose an algorithmic framework to schedule constellations of small spacecraft with 3-DOF re-orientation capabilities, networked with inter-sat links.", "This paper proposes a communication module to optimize the schedule of communication for the problem of spacecraft constellations, and compares the algorithm in distributed and centralized settings." ] }, { "id": "task668-44b298d362774280a2503079d1bef431", "input": "We present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to object concepts with a segmentation-based network dissection method. Then, we examine the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. Finally, we examine the contextual relationship between these units and their surrounding by inserting the discovered object concepts into new images. We show several practical applications enabled by our framework, from comparing internal representations across different layers and models, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in the scene.", "output": [ "GAN representations are examined in detail, and sets of representation units are found that control the generation of semantic concepts in the output." ] }, { "id": "task668-4745f6a5a00c4360a9104090493ee179", "input": "This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks. The framework can successfully train both deep discriminative models and deep generative models in complex continual learning settings where existing tasks evolve over time and entirely new tasks emerge. Experimental results show that VCL outperforms state-of-the-art continual learning methods on a variety of tasks, avoiding catastrophic forgetting in a fully automatic way.", "output": [ "This paper develops a principled method for continual learning in deep models." ] }, { "id": "task668-69424b4cf4ab43079d70967aa572f1a0", "input": "Reinforcement learning typically requires carefully designed reward functions in order to learn the desired behavior. We present a novel reward estimation method that is based on a finite sample of optimal state trajectories from expert demon- strations and can be used for guiding an agent to mimic the expert behavior. The optimal state trajectories are used to learn a generative or predictive model of the “good” states distribution. The reward signal is computed by a function of the difference between the actual next state acquired by the agent and the predicted next state given by the learned generative or predictive model. With this inferred reward function, we perform standard reinforcement learning in the inner loop to guide the agent to learn the given task. Experimental evaluations across a range of tasks demonstrate that the proposed method produces superior performance compared to standard reinforcement learning with both complete or sparse hand engineered rewards. Furthermore, we show that our method successfully enables an agent to learn good actions directly from expert player video of games such as the Super Mario Bros and Flappy Bird.", "output": [ "Reward Estimation from Game Videos" ] }, { "id": "task668-da82862112e543e8b28f31da331c0842", "input": "Recent advances in deep learning techniques has shown the usefulness of the deep neural networks in extracting features required to perform the task at hand.\n However, these features learnt are in particular helpful only for the initial task. This is due to the fact that the features learnt are very task specific and does not capture the most general and task agnostic features of the input.\n In fact the way humans are seen to learn is by disentangling features which task agnostic. This indicates that leaning task agnostic features by disentangling only the most informative features from the input data.\n Recently Variational Auto-Encoders (VAEs) have shown to be the de-facto models to capture the latent variables in a generative sense.\n As these latent features can be represented as continuous and/or discrete variables, this indicates us to use VAE with a mixture of continuous and discrete variables for the latent space.\n We achieve this by performing our experiments using a modified version of joint-vae to learn the disentangled features.\n", "output": [ "Mixture Model for Neural Disentanglement" ] }, { "id": "task668-14b764aee24a45249a7d1bbc616f9365", "input": "Successful recurrent models such as long short-term memories (LSTMs) and gated recurrent units (GRUs) use \\emph{ad hoc} gating mechanisms. Empirically these models have been found to improve the learning of medium to long term temporal dependencies and to help with vanishing gradient issues.\n\t\n We prove that learnable gates in a recurrent model formally provide \\emph{quasi-invariance to general time transformations} in the input data. We recover part of the LSTM architecture from a simple axiomatic approach.\n\t\n This result leads to a new way of initializing gate biases in LSTMs and GRUs. Experimentally, this new \\emph{chrono initialization} is shown to greatly improve learning of long term dependencies, with minimal implementation effort.\n\n", "output": [ "Proves that gating mechanisms provide invariance to time transformations. Introduces and tests a new initialization for LSTMs from this insight.", "Paper links recurrent network deisgn and its effect on how the network reacts to time transformations, and uses this to develop a simple bias initialization scheme." ] }, { "id": "task668-8833bef0ce6f4d88b3ced09dd218541f", "input": "The idea that neural networks may exhibit a bias towards simplicity has a long history. Simplicity bias provides a way to quantify this intuition. It predicts, for a broad class of input-output maps which can describe many systems in science and engineering, that simple outputs are exponentially more likely to occur upon uniform random sampling of inputs than complex outputs are. This simplicity bias behaviour has been observed for systems ranging from the RNA sequence to secondary structure map, to systems of coupled differential equations, to models of plant growth. Deep neural networks can be viewed as a mapping from the space of parameters (the weights) to the space of functions (how inputs get transformed to outputs by the network). We show that this parameter-function map obeys the necessary conditions for simplicity bias, and numerically show that it is hugely biased towards functions with low descriptional complexity. We also demonstrate a Zipf like power-law probability-rank relation. A bias towards simplicity may help explain why neural nets generalize so well.", "output": [ "A very strong bias towards simple outpouts is observed in many simple input-ouput maps. The parameter-function map of deep networks is found to be biased in the same way." ] }, { "id": "task668-15f82077bcdd4927a618366b259ddcbc", "input": "A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the observations generated by the Markov Decision Process (MDP). We provide a general framework for analyzing this scenario, which we use to design multiple synthetic benchmarks from only modifying the observation space of an MDP. When an agent overfits to different observation spaces even if the underlying MDP dynamics is fixed, we term this observational overfitting. Our experiments expose intriguing properties especially with regards to implicit regularization, and also corroborate results from previous works in RL generalization and supervised learning (SL).", "output": [ "We isolate one factor of RL generalization by analyzing the case when the agent only overfits to the observations. We show that architectural implicit regularizations occur in this regime." ] }, { "id": "task668-33795af1137342ed92c62f1f0e09f6a3", "input": "The variational autoencoder (VAE) has found success in modelling the manifold of natural images on certain datasets, allowing meaningful images to be generated while interpolating or extrapolating in the latent code space, but it is unclear whether similar capabilities are feasible for text considering its discrete nature. In this work, we investigate the reason why unsupervised learning of controllable representations fails for text. We find that traditional sequence VAEs can learn disentangled representations through their latent codes to some extent, but they often fail to properly decode when the latent factor is being manipulated, because the manipulated codes often land in holes or vacant regions in the aggregated posterior latent space, which the decoding network is not trained to process. Both as a validation of the explanation and as a fix to the problem, we propose to constrain the posterior mean to a learned probability simplex, and performs manipulation within this simplex. Our proposed method mitigates the latent vacancy problem and achieves the first success in unsupervised learning of controllable representations for text. Empirically, our method significantly outperforms unsupervised baselines and is competitive with strong supervised approaches on text style transfer. Furthermore, when switching the latent factor (e.g., topic) during a long sentence generation, our proposed framework can often complete the sentence in a seemingly natural way -- a capability that has never been attempted by previous methods.", "output": [ "why previous VAEs on text cannot learn controllable latent representation as on images, as well as a fix to enable the first success towards controlled text generation without supervision" ] }, { "id": "task668-29df9af37c154a6797a60f52acdc715c", "input": "To deal simultaneously with both, the attributed network embedding and clustering, we propose a new model. It exploits both content and structure information, capitalising on their simultaneous use. The proposed model relies on the approximation of the relaxed continuous embedding solution by the true discrete clustering one. Thereby, we show that incorporating an embedding representation provides simpler and more interpretable solutions. Experiment results demonstrate that the proposed algorithm performs better, in terms of clustering and embedding, than the state-of-art algorithms, including deep learning methods devoted to similar tasks for attributed network datasets with different proprieties.", "output": [ "This paper propose a novel matrix decomposition framework for simultaneous attributed network data embedding and clustering.", "This paper proposes an algorithm to perform jointly attribute network embedding and clustering together." ] }, { "id": "task668-2bd42145cfe64431aff82a722b105077", "input": "Up until very recently, inspired by a mass of researches on adversarial examples for computer vision, there has been a growing interest in designing adversarial attacks for Natural Language Processing (NLP) tasks, followed by very few works of adversarial defenses for NLP. To our knowledge, there exists no defense method against the successful synonym substitution based attacks that aim to satisfy all the lexical, grammatical, semantic constraints and thus are hard to perceived by humans. We contribute to fill this gap and propose a novel adversarial defense method called Synonym Encoding Method (SEM), which inserts an encoder before the input layer of the model and then trains the model to eliminate adversarial perturbations. Extensive experiments demonstrate that SEM can efficiently defend current best synonym substitution based adversarial attacks with little decay on the accuracy for benign examples. To better evaluate SEM, we also design a strong attack method called Improved Genetic Algorithm (IGA) that adopts the genetic metaheuristic for synonym substitution based attacks. Compared with existing genetic based adversarial attack, IGA can achieve higher attack success rate while maintaining the transferability of the adversarial examples.", "output": [ "The first text adversarial defense method in word level, and the improved generic based attack method against synonyms substitution based attacks." ] }, { "id": "task668-3d93e4b8a6164708955ceaa202208f49", "input": "We view molecule optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph. A key challenge is therefore to model diverse translation outputs. Our primary contributions include a junction tree encoder-decoder for learning diverse graph translations along with a novel adversarial training method for aligning distributions of molecules. Diverse output distributions in our model are explicitly realized by low-dimensional latent vectors that modulate the translation process. We evaluate our model on multiple molecule optimization tasks and show that our model outperforms previous state-of-the-art baselines by a significant margin. \n", "output": [ "We introduce a graph-to-graph encoder-decoder framework for learning diverse graph translations.", "Proposes a graph-to-graph translation model for molecule optimization inspired by matched molecular pair analysis.", "Extension of JT-VAE into the graph to graph translation scenario by adding the latent variable to capture multi-modality and an adversarial regularization in the latent space", "Proposes a quite complex system, involving many different choices and components, for obtaining chemical compouds with improved properties starting from a given corpora." ] }, { "id": "task668-8cd0c2ce004e48cf80aea6116dc1b5dc", "input": "Learning good representations of users and items is crucially important to recommendation with implicit feedback. Matrix factorization is the basic idea to derive the representations of users and items by decomposing the given interaction matrix. However, existing matrix factorization based approaches share the limitation in that the interaction between user embedding and item embedding is only weakly enforced by fitting the given individual rating value, which may lose potentially useful information. In this paper, we propose a novel Augmented Generalized Matrix Factorization (AGMF) approach that is able to incorporate the historical interaction information of users and items for learning effective representations of users and items. Despite the simplicity of our proposed approach, extensive experiments on four public implicit feedback datasets demonstrate that our approach outperforms state-of-the-art counterparts. Furthermore, the ablation study demonstrates that by using multi-hot encoding to enrich user embedding and item embedding for Generalized Matrix Factorization, better performance, faster convergence, and lower training loss can be achieved.", "output": [ "A simple extension of generalized matrix factorization can outperform state-of-the-art approaches for recommendation.", "The work presents a matrix factorization framework for enforcing the effect of historical data when learning user preferences in collaborative filtering settings." ] }, { "id": "task668-075460f691ec49da92726a4bea6a316e", "input": "In the visual system, neurons respond to a patch of the input known as their classical receptive field (RF), and can be modulated by stimuli in the surround. These interactions are often mediated by lateral connections, giving rise to extra-classical RFs. We use supervised learning via backpropagation to learn feedforward connections, combined with an unsupervised learning rule to learn lateral connections between units within a convolutional neural network. These connections allow each unit to integrate information from its surround, generating extra-classical receptive fields for the units in our new proposed model (CNNEx). We demonstrate that these connections make the network more robust and achieve better performance on noisy versions of the MNIST and CIFAR-10 datasets. Although the image statistics of MNIST and CIFAR-10 differ greatly, the same unsupervised learning rule generalized to both datasets. Our framework can potentially be applied to networks trained on other tasks, with the learned lateral connections aiding the computations implemented by feedforward connections when the input is unreliable.", "output": [ "CNNs with biologically-inspired lateral connections learned in an unsupervised manner are more robust to noisy inputs. " ] }, { "id": "task668-dde7cb7c6f8c497caaa41c72172d67a7", "input": "This paper proposes ASAL, a new pool based active learning method that generates high entropy samples. Instead of directly annotating the synthetic samples, ASAL searches similar samples from the pool and includes them for training. Hence, the quality of new samples is high and annotations are reliable. ASAL is particularly suitable for large data sets because it achieves a better run-time complexity (sub-linear) for sample selection than traditional uncertainty sampling (linear). We present a comprehensive set of experiments on two data sets and show that ASAL outperforms similar methods and clearly exceeds the established baseline (random sampling). In the discussion section we analyze in which situations ASAL performs best and why it is sometimes hard to outperform random sample selection. To the best of our knowledge this is the first adversarial active learning technique that is applied for multiple class problems using deep convolutional classifiers and demonstrates superior performance than random sample selection.", "output": [ "ASAL is a pool based active learning method that generates high entropy samples and retrieves matching samples from the pool in sub-linear time." ] }, { "id": "task668-3068c16cf2524824b5956e593dcfb850", "input": "Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task. Bayesian hierarchical modeling provides a theoretical framework for formalizing meta-learning as inference for a set of parameters that are shared across tasks. Here, we reformulate the model-agnostic meta-learning algorithm (MAML) of Finn et al. (2017) as a method for probabilistic inference in a hierarchical Bayesian model. In contrast to prior methods for meta-learning via hierarchical Bayes, MAML is naturally applicable to complex function approximators through its use of a scalable gradient descent procedure for posterior inference. Furthermore, the identification of MAML as hierarchical Bayes provides a way to understand the algorithm’s operation as a meta-learning procedure, as well as an opportunity to make use of computational strategies for efficient inference. We use this opportunity to propose an improvement to the MAML algorithm that makes use of techniques from approximate inference and curvature estimation.", "output": [ "A specific gradient-based meta-learning algorithm, MAML, is equivalent to an inference procedure in a hierarchical Bayesian model. We use this connection to improve MAML via methods from approximate inference and curvature estimation." ] }, { "id": "task668-c2deb513b74645bd8e20136b25b925ac", "input": "We explore ways of incorporating bilingual dictionaries to enable semi-supervised\n neural machine translation. Conventional back-translation methods have shown\n success in leveraging target side monolingual data. However, since the quality of\n back-translation models is tied to the size of the available parallel corpora, this\n could adversely impact the synthetically generated sentences in a low resource\n setting. We propose a simple data augmentation technique to address both this\n shortcoming. We incorporate widely available bilingual dictionaries that yield\n word-by-word translations to generate synthetic sentences. This automatically\n expands the vocabulary of the model while maintaining high quality content. Our\n method shows an appreciable improvement in performance over strong baselines.", "output": [ "We use bilingual dictionaries for data augmentation for neural machine translation", "This paper investigates using bilingual dictionaries to create synthetic sources for target-side monolingual data to improve over NMT models trained with small amounts of parallel data." ] }, { "id": "task668-ccf61b074a264e1e8d55ff2fb97099c1", "input": "The goal of this paper is to demonstrate a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA). We employ MERA as a replacement for linear layers in a neural network and test this implementation on the CIFAR-10 dataset. The proposed method outperforms factorization using tensor trains, providing greater compression for the same level of accuracy and greater accuracy for the same level of compression. We demonstrate MERA-layers with 3900 times fewer parameters and a reduction in accuracy of less than 1% compared to the equivalent fully connected layers.\n", "output": [ "We replace the fully connected layers of a neural network with the multi-scale entanglement renormalization ansatz, a type of quantum operation which describes long range correlations. ", "In the paper the authors suggest to use MERA tensorization technique for compressing neural networks.", "A new parameterization of linear maps for neural network use, using a hierarchical factorization of the linear map that reduces the number of parameters while still allowing for relatively complex interactions to be modelled.", "Studies compressing feed forward layers using low rank tensor decompositions and explore a tree like decomposition" ] }, { "id": "task668-ec1859f8574c428ab47ad5de5bec8ac6", "input": "Stability is a fundamental property of dynamical systems, yet to this date it has had little bearing on the practice of recurrent neural networks. In this work, we conduct a thorough investigation of stable recurrent models. Theoretically, we prove stable recurrent neural networks are well approximated by feed-forward networks for the purpose of both inference and training by gradient descent. Empirically, we demonstrate stable recurrent models often perform as well as their unstable counterparts on benchmark sequence tasks. Taken together, these findings shed light on the effective power of recurrent networks and suggest much of sequence learning happens, or can be made to happen, in the stable regime. Moreover, our results help to explain why in many cases practitioners succeed in replacing recurrent models by feed-forward models.\n", "output": [ "Stable recurrent models can be approximated by feed-forward networks and empirically perform as well as unstable models on benchmark tasks.", "Studies the stability of RNNs and investigation of spectral normalization to sequential predictions." ] }, { "id": "task668-03f210ec2d67499b83a3c791334e9fd5", "input": "We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links. The success of such a task heavily relies on the ability of modeling and inferring the patterns of (or between) the relations. In this paper, we present a new approach for knowledge graph embedding called RotatE, which is able to model and infer various relation patterns including: symmetry/antisymmetry, inversion, and composition. Specifically, the RotatE model defines each relation as a rotation from the source entity to the target entity in the complex vector space. In addition, we propose a novel self-adversarial negative sampling technique for efficiently and effectively training the RotatE model. Experimental results on multiple benchmark knowledge graphs show that the proposed RotatE model is not only scalable, but also able to infer and model various relation patterns and significantly outperform existing state-of-the-art models for link prediction.", "output": [ "A new state-of-the-art approach for knowledge graph embedding.", "Presents a neural link prediction scoring function that can infer symmetry, anti-symmetry, inversion and composition patterns of relations in a knowledge base.", "This paper proposes an approach to knowledge graph embedding by modeling relations as rotations in the complex vector space.", "Proposes a method for graph embedding to be used for link prediction" ] }, { "id": "task668-efd6f7fb9ab741b69e3120055c1de73e", "input": "An important type of question that arises in Explainable Planning is a contrastive question, of the form \"Why action A instead of action B?\". These kinds of questions can be answered with a contrastive explanation that compares properties of the original plan containing A against the contrastive plan containing B. An effective explanation of this type serves to highlight the differences between the decisions that have been made by the planner and what the user would expect, as well as to provide further insight into the model and the planning process. Producing this kind of explanation requires the generation of the contrastive plan. This paper introduces domain-independent compilations of user questions into constraints. These constraints are added to the planning model, so that a solution to the new model represents the contrastive plan. We introduce a formal description of the compilation from user question to constraints in a temporal and numeric PDDL2.1 planning setting.", "output": [ "This paper introduces domain-independent compilations of user questions into constraints for contrastive explanations." ] }, { "id": "task668-08370fd74a4f4eb9b733035e18509ecd", "input": "Neural Processes (NPs) (Garnelo et al., 2018) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data efficiently with linear complexity in the number of context input-output pairs, and can learn a wide family of conditional distributions; they learn predictive distributions conditioned on context sets of arbitrary size. Nonetheless, we show that NPs suffer a fundamental drawback of underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. We address this issue by incorporating attention into NPs, allowing each input location to attend to the relevant context points for the prediction. We show that this greatly improves the accuracy of predictions, results in noticeably faster training, and expands the range of functions that can be modelled.", "output": [ "A model for regression that learns conditional distributions of a stochastic process, by incorporating attention into Neural Processes.", "Proposes to resolve the issue of underfitting in the neural process method by adding an attention mechanism to the deterministic path.", "An extension to the framework of Neural Processes that adds an attention-based conditioning mechanism, allowing the model to better capture dependencies in the conditioning set.", "The authors extend neural processes by incorporating self-attention for enriching the features of the context points and cross-attention for producing a query-specific representation. They resolve the underfitting problem of NPs and show ANPs to converge better and faster than NPs." ] }, { "id": "task668-c4e0c158e8494ee2b9caaee8db7d309e", "input": "Training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental quality-versus-quantity trade-off in the learning process. Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data? We argue that if the learner could somehow know and take the label-quality into account, we could get the best of both worlds. To this end, we introduce “fidelity-weighted learning” (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data. FWL modulates the parameter updates to a student network, trained on the task we care about on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher, who has access to limited samples with high-quality labels.", "output": [ "We propose Fidelity-weighted Learning, a semi-supervised teacher-student approach for training neural networks using weakly-labeled data." ] }, { "id": "task668-7e7ace2fe2c34a04b527d4f8a96189e3", "input": "Real-life control tasks involve matters of various substances---rigid or soft bodies, liquid, gas---each with distinct physical behaviors. This poses challenges to traditional rigid-body physics engines. Particle-based simulators have been developed to model the dynamics of these complex scenes; however, relying on approximation techniques, their simulation often deviates from real-world physics, especially in the long term. In this paper, we propose to learn a particle-based simulator for complex control tasks. Combining learning with particle-based systems brings in two major benefits: first, the learned simulator, just like other particle-based systems, acts widely on objects of different materials; second, the particle-based representation poses strong inductive bias for learning: particles of the same type have the same dynamics within. This enables the model to quickly adapt to new environments of unknown dynamics within a few observations. We demonstrate robots achieving complex manipulation tasks using the learned simulator, such as manipulating fluids and deformable foam, with experiments both in simulation and in the real world. Our study helps lay the foundation for robot learning of dynamic scenes with particle-based representations.", "output": [ "Learning particle dynamics with dynamic interaction graphs for simulating and control rigid bodies, deformable objects, and fluids. " ] }, { "id": "task668-61621c06d009465dbabb4502cd35ec9a", "input": "We investigate the combination of actor-critic reinforcement learning algorithms with uniform large-scale experience replay and propose solutions for two challenges: (a) efficient actor-critic learning with experience replay (b) stability of very off-policy learning. We employ those insights to accelerate hyper-parameter sweeps in which all participating agents run concurrently and share their experience via a common replay module.\n\n To this end we analyze the bias-variance tradeoffs in V-trace, a form of importance sampling for actor-critic methods. Based on our analysis, we then argue for mixing experience sampled from replay with on-policy experience, and propose a new trust region scheme that scales effectively to data distributions where V-trace becomes unstable.\n\n We provide extensive empirical validation of the proposed solution. We further show the benefits of this setup by demonstrating state-of-the-art data efficiency on Atari among agents trained up until 200M environment frames.", "output": [ "We investigate and propose solutions for two challenges in reinforcement learning: (a) efficient actor-critic learning with experience replay (b) stability of very off-policy learning." ] }, { "id": "task668-1d2bb92c1143467aaa0b3e475e61e106", "input": "We formulate stochastic gradient descent (SGD) as a novel factorised Bayesian filtering problem, in which each parameter is inferred separately, conditioned on the corresopnding backpropagated gradient. Inference in this setting naturally gives rise to BRMSprop and BAdam: Bayesian variants of RMSprop and Adam. Remarkably, the Bayesian approach recovers many features of state-of-the-art adaptive SGD methods, including amongst others root-mean-square normalization, Nesterov acceleration and AdamW. As such, the Bayesian approach provides one explanation for the empirical effectiveness of state-of-the-art adaptive SGD algorithms. Empirically comparing BRMSprop and BAdam with naive RMSprop and Adam on MNIST, we find that Bayesian methods have the potential to considerably reduce test loss and classification error.", "output": [ "We formulated SGD as a Bayesian filtering problem, and show that this gives rise to RMSprop, Adam, AdamW, NAG and other features of state-of-the-art adaptive methods", "The paper analyzes stochastic gradient descent through Bayesian filtering as a framework for analyzing adaptive methods.", "The authors attempt to unify existing adaptive gradient methods under the Bayesian filtering framework with the dynamical prior" ] }, { "id": "task668-d1dbc01be57a4aee8375c7c149c341fa", "input": "Dynamic ridesharing services (DRS) play a major role in improving the efficiency of urban transportation. User satisfaction in dynamic ridesharing is determined by multiple factors such as travel time, cost, and social compatibility with co-passengers. Existing DRS optimize profit by maximizing the operational value for service providers or minimize travel time for users but they neglect the social experience of riders, which significantly influences the total value of the service to users. We propose DROPS, a dynamic ridesharing framework that factors the riders' social preferences in the matching process so as to improve the quality of the trips formed. Scheduling trips for users is a multi-objective optimization that aims to maximize the operational value for the service provider, while simultaneously maximizing the value of the trip for the users. The user value is estimated based on compatibility between co-passengers and the ride time. We then present a real-time matching algorithm for trip formation. Finally, we evaluate our approach empirically using real-world taxi trips data, and a population model including social preferences based on user surveys. The results demonstrate improvement in riders' social compatibility, without significantly affecting the vehicle miles for the service provider and travel time for users.", "output": [ "We propose a novel dynamic ridesharing framework to form trips that optimizes both operational value for the service provider and user value to the passengers by factoring the users' social preferences into the decision-making process." ] }, { "id": "task668-f01e569aec504f789ad07e9a9a31c63d", "input": " Recent advances in deep learning have shown promising results in many low-level vision tasks. However, solving the single-image-based view synthesis is still an open problem. In particular, the generation of new images at parallel camera views given a single input image is of great interest, as it enables 3D visualization of the 2D input scenery. We propose a novel network architecture to perform stereoscopic view synthesis at arbitrary camera positions along the X-axis, or Deep 3D Pan, with \"t-shaped\" adaptive kernels equipped with globally and locally adaptive dilations. Our proposed network architecture, the monster-net, is devised with a novel t-shaped adaptive kernel with globally and locally adaptive dilation, which can efficiently incorporate global camera shift into and handle local 3D geometries of the target image's pixels for the synthesis of naturally looking 3D panned views when a 2-D input image is given. Extensive experiments were performed on the KITTI, CityScapes and our VXXLXX_STEREO indoors dataset to prove the efficacy of our method. Our monster-net significantly outperforms the state-of-the-art method, SOTA, by a large margin in all metrics of RMSE, PSNR, and SSIM. Our proposed monster-net is capable of reconstructing more reliable image structures in synthesized images with coherent geometry. Moreover, the disparity information that can be extracted from the \"t-shaped\" kernel is much more reliable than that of the SOTA for the unsupervised monocular depth estimation task, confirming the effectiveness of our method.\n", "output": [ "Novel architecture for stereoscopic view synthesis at arbitrary camera shifts utilizing adaptive t-shaped kernels with adaptive dilations." ] }, { "id": "task668-57a0b194ffce494b8c9486a03c8c90aa", "input": "We propose a new Integral Probability Metric (IPM) between distributions: the Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions for functions (critic) restricted to a Sobolev ball defined with respect to a dominant measure mu. We show that the Sobolev IPM compares two distributions in high dimensions based on weighted conditional Cumulative Distribution Functions (CDF) of each coordinate on a leave one out basis. The Dominant measure mu plays a crucial role as it defines the support on which conditional CDFs are compared. Sobolev IPM can be seen as an extension of the one dimensional Von-Mises Cramer statistics to high dimensional distributions. We show how Sobolev IPM can be used to train Generative Adversarial Networks (GANs). We then exploit the intrinsic conditioning implied by Sobolev IPM in text generation. Finally we show that a variant of Sobolev GAN achieves competitive results in semi-supervised learning on CIFAR-10, thanks to the smoothness enforced on the critic by Sobolev GAN which relates to Laplacian regularization.", "output": [ "We define a new Integral Probability Metric (Sobolev IPM) and show how it can be used for training GANs for text generation and semi-supervised learning.", "Suggests a novel regularization scheme for GANs based on a Sobolev norm, measuring deviations between L2 norms of derivatives.", "The authors provide another type of GAN using the typical setup of a GAN but with a different function class, and produce a recipe for training GANs with that sort of function class.", "The paper proposes a different gradient penalty for GAN critics that forces the expected squared norm of the gradient to be equal to 1" ] }, { "id": "task668-cf3b18feb367428d8d71a4be5d077d4d", "input": "In many real-world settings, a learning model must perform few-shot classification: learn to classify examples from unseen classes using only a few labeled examples per class.\n Additionally, to be safely deployed, it should have the ability to detect out-of-distribution inputs: examples that do not belong to any of the classes.\n While both few-shot classification and out-of-distribution detection are popular topics,\ntheir combination has not been studied. In this work, we propose tasks for out-of-distribution detection in the few-shot setting and establish benchmark datasets, based on four popular few-shot classification datasets. Then, we propose two new methods for this task and investigate their performance.\n In sum, we establish baseline out-of-distribution detection results using standard metrics on new benchmark datasets and show improved results with our proposed methods.", "output": [ "We quantitatively study out-of-distribution detection in few-shot setting, establish baseline results with ProtoNet, MAML, ABML, and improved upon them.", "The paper proposes two new confidence scores which are more suitable for out-of-distribution detection of few-shot classification and shows that a distance metric-based approach improves performance." ] }, { "id": "task668-2a315fe95e534a53aff0420d1479fa9b", "input": "Adam-typed optimizers, as a class of adaptive moment estimation methods with the exponential moving average scheme, have been successfully used in many applications of deep learning. Such methods are appealing for capability on large-scale sparse datasets. On top of that, they are computationally efficient and insensitive to the hyper-parameter settings. In this paper, we present a new framework for adapting Adam-typed methods, namely AdamT. Instead of applying a simple exponential weighted average, AdamT also includes the trend information when updating the parameters with the adaptive step size and gradients. The newly added term is expected to efficiently capture the non-horizontal moving patterns on the cost surface, and thus converge more rapidly. We show empirically the importance of the trend component, where AdamT outperforms the conventional Adam method constantly in both convex and non-convex settings.", "output": [ "We present a new framework for adapting Adam-typed methods, namely AdamT, to include the trend information when updating the parameters with the adaptive step size and gradients.", "A new type of Adam variant that uses Holt's linear method to compute the smoothed first order and second order momentum instead of using exponential weighted average." ] }, { "id": "task668-902447a9e6e94aaea44a976f6e7f1e0e", "input": "It is well-known that neural networks are universal approximators, but that deeper networks tend in practice to be more powerful than shallower ones. We shed light on this by proving that the total number of neurons m required to approximate natural classes of multivariate polynomials of n variables grows only linearly with n for deep neural networks, but grows exponentially when merely a single hidden layer is allowed. We also provide evidence that when the number of hidden layers is increased from 1 to k, the neuron requirement grows exponentially not with n but with n^{1/k}, suggesting that the minimum number of layers required for practical expressibility grows only logarithmically with n.", "output": [ "We prove that deep neural networks are exponentially more efficient than shallow ones at approximating sparse multivariate polynomials." ] }, { "id": "task668-a57d020bab8c4d5baba03aa66f7ddcff", "input": "Highly regularized LSTMs achieve impressive results on several benchmark datasets in language modeling. We propose a new regularization method based on decoding the last token in the context using the predicted distribution of the next token. This biases the model towards retaining more contextual information, in turn improving its ability to predict the next token. With negligible overhead in the number of parameters and training time, our Past Decode Regularization (PDR) method achieves a word level perplexity of 55.6 on the Penn Treebank and 63.5 on the WikiText-2 datasets using a single softmax. We also show gains by using PDR in combination with a mixture-of-softmaxes, achieving a word level perplexity of 53.8 and 60.5 on these datasets. In addition, our method achieves 1.169 bits-per-character on the Penn Treebank Character dataset for character level language modeling. These results constitute a new state-of-the-art in their respective settings.", "output": [ "Decoding the last token in the context using the predicted next token distribution acts as a regularizer and improves language modeling.", "The authors introduce the idea of past decoding for the purpose of regularization for improved perplexity on Penn Treebank", "Proposes an additional loss term to use when training an LSTM LM and shows that by adding this loss term they can achieve SOTA perplexity on a number of LM benchmarks.", "Suggests a new regularization technique which can be added on top of those used in AWD-LSTM of Merity et al. (2017) with little overhead." ] }, { "id": "task668-4c55f941a9b84dbe9d389c4c92a00023", "input": "Continual learning is the problem of sequentially learning new tasks or knowledge while protecting previously acquired knowledge. However, catastrophic forgetting poses a grand challenge for neural networks performing such learning process. Thus, neural networks that are deployed in the real world often struggle in scenarios where the data distribution is non-stationary (concept drift), imbalanced, or not always fully available, i.e., rare edge cases. We propose a Differentiable Hebbian Consolidation model which is composed of a Differentiable Hebbian Plasticity (DHP) Softmax layer that adds a rapid learning plastic component (compressed episodic memory) to the fixed (slow changing) parameters of the softmax output layer; enabling learned representations to be retained for a longer timescale. We demonstrate the flexibility of our method by integrating well-known task-specific synaptic consolidation methods to penalize changes in the slow weights that are important for each target task. We evaluate our approach on the Permuted MNIST, Split MNIST and Vision Datasets Mixture benchmarks, and introduce an imbalanced variant of Permuted MNIST --- a dataset that combines the challenges of class imbalance and concept drift. Our proposed model requires no additional hyperparameters and outperforms comparable baselines by reducing forgetting.", "output": [ "Hebbian plastic weights can behave as a compressed episodic memory storage in neural networks and with the combination of task-specific synaptic consolidation can improve the ability to alleviate catastrophic forgetting in continual learning." ] }, { "id": "task668-7246d4a897dc4a9bb1b7f9342698d6cf", "input": "This paper introduces an information theoretic co-training objective for unsupervised learning. We consider the problem of predicting the future . Rather than predict future sensations (image pixels or sound waves) we predict ``hypotheses'' to be confirmed by future sensations . More formally, we assume a population distribution on pairs $(x,y)$ where we can think of $x$ as a past sensation and $y$ as a future sensation . We train both a predictor model $P_\\Phi(z|x)$ and a confirmation model $P_\\Psi(z|y)$ where we view $z$ as hypotheses (when predicted) or facts (when confirmed ). For a population distribution on pairs $(x,y)$ we focus on the problem of measuring the mutual information between $x$ and $ y$. By the data processing inequality this mutual information is at least as large as the mutual information between $x$ and $z$ under the distribution on triples $(x,z,y)$ defined by the confirmation model $P_\\Psi(z|y)$. The information theoretic training objective for $P_\\Phi(z|x)$ and $P_\\Psi(z|y)$ can be viewed as a form of co-training where we want the prediction from $x$ to match the confirmation from $y$. We give experiments on applications to learning phonetics on the TIMIT dataset.", "output": [ "Presents an information theoretic training objective for co-training and demonstrates its power in unsupervised learning of phonetics." ] }, { "id": "task668-62591c73f7994d0f87e9d49d75bfa641", "input": "In this preliminary work, we study the generalization properties of infinite ensembles of infinitely-wide neural networks. Amazingly, this model family admits tractable calculations for many information-theoretic quantities. We report analytical and empirical investigations in the search for signals that correlate with generalization.", "output": [ "Infinite ensembles of infinitely wide neural networks are an interesting model family from an information theoretic perspective." ] }, { "id": "task668-e898e19f96d047f487c337b7026a5acd", "input": "We study the problem of alleviating the instability issue in the GAN training procedure via new architecture design. The discrepancy between the minimax and maximin objective values could serve as a proxy for the difficulties that the alternating gradient descent encounters in the optimization of GANs. In this work, we give new results on the benefits of multi-generator architecture of GANs. We show that the minimax gap shrinks to \\epsilon as the number of generators increases with rate O(1/\\epsilon). This improves over the best-known result of O(1/\\epsilon^2). At the core of our techniques is a novel application of Shapley-Folkman lemma to the generic minimax problem, where in the literature the technique was only known to work when the objective function is restricted to the Lagrangian function of a constraint optimization problem. Our proposed Stackelberg GAN performs well experimentally in both synthetic and real-world datasets, improving Frechet Inception Distance by 14.61% over the previous multi-generator GANs on the benchmark datasets.", "output": [ "We study the problem of alleviating the instability issue in the GAN training procedure via new architecture design, with theoretical guarantees." ] }, { "id": "task668-1dc686c45c004a228d9a2621a91c1fec", "input": "Exploration is a fundamental aspect of Reinforcement Learning, typically implemented using stochastic action-selection. Exploration, however, can be more efficient if directed toward gaining new world knowledge. Visit-counters have been proven useful both in practice and in theory for directed exploration. However, a major limitation of counters is their locality. While there are a few model-based solutions to this shortcoming, a model-free approach is still missing.\n We propose $E$-values, a generalization of counters that can be used to evaluate the propagating exploratory value over state-action trajectories. We compare our approach to commonly used RL techniques, and show that using $E$-values improves learning and performance over traditional counters. We also show how our method can be implemented with function approximation to efficiently learn continuous MDPs. We demonstrate this by showing that our approach surpasses state of the art performance in the Freeway Atari 2600 game.", "output": [ "We propose a generalization of visit-counters that evaluate the propagating exploratory value over trajectories, enabling efficient exploration for model-free RL" ] }, { "id": "task668-45b2a2dc3e6147dfaa4a95347f2247d8", "input": "The machine learning and computer vision community is witnessing an unprecedented rate of new tasks being proposed and addressed, thanks to the power of deep convolutional networks to find complex mappings from X to Y. The advent of each task often accompanies the release of a large-scale human-labeled dataset, for supervised training of the deep network. However, it is expensive and time-consuming to manually label sufficient amount of training data. Therefore, it is important to develop algorithms that can leverage off-the-shelf labeled dataset to learn useful knowledge for the target task. While previous works mostly focus on transfer learning from a single source, we study multi-source transfer across domains and tasks (MS-DTT), in a semi-supervised setting. We propose GradMix, a model-agnostic method applicable to any model trained with gradient-based learning rule. GradMix transfers knowledge via gradient descent, by weighting and mixing the gradients from all sources during training. Our method follows a meta-learning objective, by assigning layer-wise weights to the source gradients, such that the combined gradient follows the direction that can minimize the loss for a small set of samples from the target dataset. In addition, we propose to adaptively adjust the learning rate for each mini-batch based on its importance to the target task, and a pseudo-labeling method to leverage the unlabeled samples in the target domain. We perform experiments on two MS-DTT tasks: digit recognition and action recognition, and demonstrate the advantageous performance of the proposed method against multiple baselines.", "output": [ "We propose a gradient-based method to transfer knowledge from multiple sources across different domains and tasks.", "This paper proposes to combine the gradients of source domains to help the learning in the target domain. " ] }, { "id": "task668-934d4dbf52404d58b5d62ef5aced5f0e", "input": "In recent years several adversarial attacks and defenses have been proposed. Often seemingly robust models turn out to be non-robust when more sophisticated attacks are used. One way out of this dilemma are provable robustness guarantees. While provably robust models for specific $l_p$-perturbation models have been developed, we show that they do not come with any guarantee against other $l_q$-perturbations. We propose a new regularization scheme, MMR-Universal, for ReLU networks which enforces robustness wrt $l_1$- \\textit{and} $l_\\infty$-perturbations and show how that leads to the first provably robust models wrt any $l_p$-norm for $p\\geq 1$.", "output": [ "We introduce a method to train models with provable robustness wrt all the $l_p$-norms for $p\\geq 1$ simultaneously." ] }, { "id": "task668-f6a93a8456e84a93ad3510055e0a336d", "input": "In this paper, we tackle the problem of detecting samples that are not drawn from the training distribution, i.e., out-of-distribution (OOD) samples, in classification. Many previous studies have attempted to solve this problem by regarding samples with low classification confidence as OOD examples using deep neural networks (DNNs). However, on difficult datasets or models with low classification ability, these methods incorrectly regard in-distribution samples close to the decision boundary as OOD samples. This problem arises because their approaches use only the features close to the output layer and disregard the uncertainty of the features. Therefore, we propose a method that extracts the uncertainties of features in each layer of DNNs using a reparameterization trick and combines them. In experiments, our method outperforms the existing methods by a large margin, achieving state-of-the-art detection performance on several datasets and classification models. For example, our method increases the AUROC score of prior work (83.8%) to 99.8% in DenseNet on the CIFAR-100 and Tiny-ImageNet datasets.", "output": [ "We propose a method that extracts the uncertainties of features in each layer of DNNs and combines them for detecting OOD samples when solving classification tasks." ] }, { "id": "task668-c20fda2a09b2459691608b7b738115a5", "input": "As machine learning methods see greater adoption and implementation in high stakes applications such\nas medical image diagnosis, the need for model interpretability and explanation has become more\ncritical. Classical approaches that assess feature importance (eg saliency maps) do not explain how and why a particular region of an image is relevant to the prediction. We propose\na method that explains the outcome of a classification black-box by gradually exaggerating\nthe semantic effect of a given class. Given a query input to a classifier, our method produces a\nprogressive set of plausible variations of that query, which gradually change the posterior probability\nfrom its original class to its negation. These counter-factually generated samples preserve features\nunrelated to the classification decision, such that a user can employ our method as a ``tuning knob'' to traverse a data manifold while crossing the decision boundary. Our method is model agnostic and only requires the output value and gradient of the predictor with respect to its input.", "output": [ "A method to explain a classifier, by generating visual perturbation of an image by exaggerating or diminishing the semantic features that the classifier associates with a target label.", "A model that when given a query input to a black-box, aims to explain the outcome by providing plausible and progressive variations to the query that can result in a change to the output.", "A method for explaining the output of black box classification of images, that generates gradual perturbation of outputs in response to gradually perturbed input queries." ] }, { "id": "task668-591455bed18740a9824a1a63c7e96b87", "input": "Methods that calculate dense vector representations for features in unstructured data—such as words in a document—have proven to be very successful for knowledge representation. We study how to estimate dense representations when multiple feature types exist within a dataset for supervised learning where explicit labels are available, as well as for unsupervised learning where there are no labels. Feat2Vec calculates embeddings for data with multiple feature types enforcing that all different feature types exist in a common space. In the supervised case, we show that our method has advantages over recently proposed methods; such as enabling higher prediction accuracy, and providing a way to avoid the cold-start\n problem. In the unsupervised case, our experiments suggest that Feat2Vec significantly outperforms existing algorithms that do not leverage the structure of the data. We believe that we are the first to propose a method for learning unsuper vised embeddings that leverage the structure of multiple feature types.", "output": [ "Learn dense vector representations of arbitrary types of features in labeled and unlabeled datasets" ] }, { "id": "task668-fe31d019b63d447aab9b5879d8fbec6f", "input": "For computer vision applications, prior works have shown the efficacy of reducing numeric precision of model parameters (network weights) in deep neural networks. Activation maps, however, occupy a large memory footprint during both the training and inference step when using mini-batches of inputs. One way to reduce this large memory footprint is to reduce the precision of activations. However, past works have shown that reducing the precision of activations hurts model accuracy. We study schemes to train networks from scratch using reduced-precision activations without hurting accuracy. We reduce the precision of activation maps (along with model parameters) and increase the number of filter maps in a layer, and find that this scheme matches or surpasses the accuracy of the baseline full-precision network. As a result, one can significantly improve the execution efficiency (e.g. reduce dynamic memory footprint, memory band- width and computational energy) and speed up the training and inference process with appropriate hardware support. We call our scheme WRPN -- wide reduced-precision networks. We report results and show that WRPN scheme is better than previously reported accuracies on ILSVRC-12 dataset while being computationally less expensive compared to previously reported reduced-precision networks.", "output": [ "Lowering precision (to 4-bits, 2-bits and even binary) and widening the filter banks gives as accurate network as those obtained with FP32 weights and activations." ] }, { "id": "task668-13f1772ef3b94d44a7ec09b7b4b29f93", "input": "Autonomous vehicles are becoming more common in city transportation. Companies will begin to find a need to teach these vehicles smart city fleet coordination. Currently, simulation based modeling along with hand coded rules dictate the decision making of these autonomous vehicles. We believe that complex intelligent behavior can be learned by these agents through Reinforcement Learning. In this paper, we discuss our work for solving this system by adapting the Deep Q-Learning (DQN) model to the multi-agent setting. Our approach applies deep reinforcement learning by combining convolutional neural networks with DQN to teach agents to fulfill customer demand in an environment that is partially observ-able to them. We also demonstrate how to utilize transfer learning to teach agents to balance multiple objectives such as navigating to a charging station when its en-ergy level is low. The two evaluations presented show that our solution has shown hat we are successfully able to teach agents cooperation policies while balancing multiple objectives.", "output": [ "Utilized Deep Reinforcement Learning to teach agents ride-sharing fleet style coordination." ] }, { "id": "task668-793b6d8067344741a08632a51630c584", "input": "Determining the number of latent dimensions is a ubiquitous problem in machine\n learning. In this study, we introduce a novel method that relies on SVD to discover\n the number of latent dimensions. The general principle behind the method is to\n compare the curve of singular values of the SVD decomposition of a data set with\n the randomized data set curve. The inferred number of latent dimensions corresponds\n to the crossing point of the two curves. To evaluate our methodology, we\n compare it with competing methods such as Kaisers eigenvalue-greater-than-one\n rule (K1), Parallel Analysis (PA), Velicers MAP test (Minimum Average Partial).\n We also compare our method with the Silhouette Width (SW) technique which is\n used in different clustering methods to determine the optimal number of clusters.\n The result on synthetic data shows that the Parallel Analysis and our method have\n similar results and more accurate than the other methods, and that our methods is\n slightly better result than the Parallel Analysis method for the sparse data sets.", "output": [ "In this study, we introduce a novel method that relies on SVD to discover the number of latent dimensions." ] }, { "id": "task668-33d5723c22c9434eac3d63a46990f9f0", "input": "A common way to speed up training of large convolutional networks is to add computational units. Training is then performed using data-parallel synchronous Stochastic Gradient Descent (SGD) with a mini-batch divided between computational units. With an increase in the number of nodes, the batch size grows. However, training with a large batch often results in lower model accuracy. We argue that the current recipe for large batch training (linear learning rate scaling with warm-up) is not general enough and training may diverge. To overcome these optimization difficulties, we propose a new training algorithm based on Layer-wise Adaptive Rate Scaling (LARS). Using LARS, we scaled AlexNet and ResNet-50 to a batch size of 16K.", "output": [ "A new large batch training algorithm based on Layer-wise Adaptive Rate Scaling (LARS); using LARS, we scaled AlexNet and ResNet-50 to a batch of 16K." ] }, { "id": "task668-05673053828f4546ad9c52ff9521974b", "input": "In most practical settings and theoretical analyses, one assumes that a model can be trained until convergence. However, the growing complexity of machine learning datasets and models may violate such assumptions. Indeed, current approaches for hyper-parameter tuning and neural architecture search tend to be limited by practical resource constraints. Therefore, we introduce a formal setting for studying training under the non-asymptotic, resource-constrained regime, i.e., budgeted training. We analyze the following problem: \"given a dataset, algorithm, and fixed resource budget, what is the best achievable performance?\" We focus on the number of optimization iterations as the representative resource. Under such a setting, we show that it is critical to adjust the learning rate schedule according to the given budget. Among budget-aware learning schedules, we find simple linear decay to be both robust and high-performing. We support our claim through extensive experiments with state-of-the-art models on ImageNet (image classification), Kinetics (video classification), MS COCO (object detection and instance segmentation), and Cityscapes (semantic segmentation). We also analyze our results and find that the key to a good schedule is budgeted convergence, a phenomenon whereby the gradient vanishes at the end of each allowed budget. We also revisit existing approaches for fast convergence and show that budget-aware learning schedules readily outperform such approaches under (the practical but under-explored) budgeted training setting.", "output": [ "Introduce a formal setting for budgeted training and propose a budget-aware linear learning rate schedule", "This work presents a technique for tuning the learning rate for Neural Network training when under a fixed number of epochs.", "This paper analyzed which learning rate schedule should be used when the number of iteration is limited using an introduced concept of BAS (Budget-Aware Schedule)." ] }, { "id": "task668-ef2a2af1531043a18edb809c14c7efcb", "input": "This paper proposes Metagross (Meta Gated Recursive Controller), a new neural sequence modeling unit. Our proposed unit is characterized by recursive parameterization of its gating functions, i.e., gating mechanisms of Metagross are controlled by instances of itself, which are repeatedly called in a recursive fashion. This can be interpreted as a form of meta-gating and recursively parameterizing a recurrent model. We postulate that our proposed inductive bias provides modeling benefits pertaining to learning with inherently hierarchically-structured sequence data (e.g., language, logical or music tasks). To this end, we conduct extensive experiments on recursive logic tasks (sorting, tree traversal, logical inference), sequential pixel-by-pixel classification, semantic parsing, code generation, machine translation and polyphonic music modeling, demonstrating the widespread utility of the proposed approach, i.e., achieving state-of-the-art (or close) performance on all tasks.", "output": [ "Recursive Parameterization of Recurrent Models improve performance " ] }, { "id": "task668-75fadbc4fab44b62a73f71782368bdcb", "input": "The ability of a classifier to recognize unknown inputs is important for many classification-based systems. We discuss the problem of simultaneous classification and novelty detection, i.e. determining whether an input is from the known set of classes and from which specific class, or from an unknown domain and does not belong to any of the known classes. We propose a method based on the Generative Adversarial Networks (GAN) framework. We show that a multi-class discriminator trained with a generator that generates samples from a mixture of nominal and novel data distributions is the optimal novelty detector. We approximate that generator with a mixture generator trained with the Feature Matching loss and empirically show that the proposed method outperforms conventional methods for novelty detection. Our findings demonstrate a simple, yet powerful new application of the GAN framework for the task of novelty detection.", "output": [ "We propose to solve a problem of simultaneous classification and novelty detection within the GAN framework.", "Proposes a GAN to unify classification and novelty detection.", "The paper presents a method for novelty detection based on a multi-class GAN which is trained to output images generated from a mixture of the nominal and novel distributions.", "The paper proposes a GAN for novelty detection using a mixture generator with feature matching loss" ] }, { "id": "task668-f6327a6aac4f4949978ceea4edf26c9e", "input": "Comparing the inferences of diverse candidate models is an essential part of model checking and escaping local optima. To enable efficient comparison, we introduce an amortized variational inference framework that can perform fast and reliable posterior estimation across models of the same architecture. Our Any Parameter Encoder (APE) extends the encoder neural network common in amortized inference to take both a data feature vector and a model parameter vector as input. APE thus reduces posterior inference across unseen data and models to a single forward pass. In experiments comparing candidate topic models for synthetic data and product reviews, our Any Parameter Encoder yields comparable posteriors to more expensive methods in far less time, especially when the encoder architecture is designed in model-aware fashion.", "output": [ "We develop VAEs where the encoder takes a model parameter vector as input, so we can do rapid inference for many models" ] }, { "id": "task668-1c04ab2a54c449ea80e960b8528956e9", "input": "The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks. However, despite the proliferation of such methods there is currently no study of their robustness to adversarial attacks. We provide the first adversarial vulnerability analysis on the widely used family of methods based on random walks. We derive efficient adversarial perturbations that poison the network structure and have a negative effect on both the quality of the embeddings and the downstream tasks. We further show that our attacks are transferable since they generalize to many models, and are successful even when the attacker is restricted.", "output": [ "Adversarial attacks on unsupervised node embeddings based on eigenvalue perturbation theory." ] }, { "id": "task668-b30a29396e344551a7919fe3dd332039", "input": "An algorithm is introduced for learning a predictive state representation with off-policy temporal difference (TD) learning that is then used to learn to steer a vehicle with reinforcement learning. There are three components being learned simultaneously: (1) the off-policy predictions as a compact representation of state, (2) the behavior policy distribution for estimating the off-policy predictions, and (3) the deterministic policy gradient for learning to act. A behavior policy discriminator is learned and used for estimating the important sampling ratios needed to learn the predictive representation off-policy with general value functions (GVFs). A linear deterministic policy gradient method is used to train the agent with only the predictive representations while the predictions are being learned. All three components are combined, demonstrated and evaluated on the problem of steering the vehicle from images in the TORCS racing simulator environment.\n Steering from only images is a challenging problem where evaluation is completed on a held-out set of tracks that were never seen during training in order to measure the generalization of the predictions and controller. Experiments show the proposed method is able to steer smoothly and navigate many but not all of the tracks available in TORCS with performance that exceeds DDPG using only images as input and approaches the performance of an ideal non-vision based kinematics model.", "output": [ "An algorithm to learn a predictive state representation with general value functions and off-policy learning is applied to the problem of vision-based steering in autonomous driving." ] }, { "id": "task668-faaf1d504e164a758a11951d88da189f", "input": "Classical models describe primary visual cortex (V1) as a filter bank of orientation-selective linear-nonlinear (LN) or energy models, but these models fail to predict neural responses to natural stimuli accurately. Recent work shows that convolutional neural networks (CNNs) can be trained to predict V1 activity more accurately, but it remains unclear which features are extracted by V1 neurons beyond orientation selectivity and phase invariance. Here we work towards systematically studying V1 computations by categorizing neurons into groups that perform similar computations. We present a framework for identifying common features independent of individual neurons' orientation selectivity by using a rotation-equivariant convolutional neural network, which automatically extracts every feature at multiple different orientations. We fit this rotation-equivariant CNN to responses of a population of 6000 neurons to natural images recorded in mouse primary visual cortex using two-photon imaging. We show that our rotation-equivariant network outperforms a regular CNN with the same number of feature maps and reveals a number of common features, which are shared by many V1 neurons and are pooled sparsely to predict neural activity. Our findings are a first step towards a powerful new tool to study the nonlinear functional organization of visual cortex.", "output": [ "A rotation-equivariant CNN model of V1 that outperforms previous models and suggest functional groupings of V1 neurons." ] }, { "id": "task668-741a1734a4e84113a25f6cd37ac066d9", "input": "Intelligent agents can learn to represent the action spaces of other agents simply by observing them act. Such representations help agents quickly learn to predict the effects of their own actions on the environment and to plan complex action sequences. In this work, we address the problem of learning an agent’s action space purely from visual observation. We use stochastic video prediction to learn a latent variable that captures the scene's dynamics while being minimally sensitive to the scene's static content. We introduce a loss term that encourages the network to capture the composability of visual sequences and show that it leads to representations that disentangle the structure of actions. We call the full model with composable action representations Composable Learned Action Space Predictor (CLASP). We show the applicability of our method to synthetic settings and its potential to capture action spaces in complex, realistic visual settings. When used in a semi-supervised setting, our learned representations perform comparably to existing fully supervised methods on tasks such as action-conditioned video prediction and planning in the learned action space, while requiring orders of magnitude fewer action labels. Project website: https://daniilidis-group.github.io/learned_action_spaces", "output": [ "We learn a representation of an agent's action space from pure visual observations. We use a recurrent latent variable approach with a novel composability loss.", "Proposes a compositional latent-variable model to learn models that predict what will happen next in scenarios where action-labels are not available in abundance.", "A variational IB based approach to learn action representations directly from videos of actions being taken, achieving better efficiency of subsequent learning methods while requiring lesser amount of action label videos.", "This paper proposes an approach to video prediction which autonomously finds an action space encoding differences between subsequent frames" ] }, { "id": "task668-bdb7ebd4c00c4e319b1712689a4666ef", "input": "In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions. However, jointly modeling future observations can be computationally expensive or even intractable if the observations are high-dimensional (e.g. images). For this reason, previous works have considered partial models, which model only part of the observation. In this paper, we show that partial models can be causally incorrect: they are confounded by the observations they don't model, and can therefore lead to incorrect planning. To address this, we introduce a general family of partial models that are provably causally correct, but avoid the need to fully model future observations.", "output": [ "Causally correct partial models do not have to generate the whole observation to remain causally correct in stochastic environments." ] }, { "id": "task668-e8816d3df990472d811567c20aee4743", "input": "Self-attention-based Transformer has demonstrated the state-of-the-art performances in a number of natural language processing tasks. Self attention is able to model long-term dependencies, but it may suffer from the extraction of irrelevant information in the context. To tackle the problem, we propose a novel model called Sparse Transformer. Sparse Transformer is able to improve the concentration of attention on the global context through an explicit selection of the most relevant segments. Extensive experimental results on a series of natural language processing tasks, including neural machine translation, image captioning, and language modeling, all demonstrate the advantages of Sparse Transformer in model performance. \n Sparse Transformer reaches the state-of-the-art performances in the IWSLT 2015 English-to-Vietnamese translation and IWSLT 2014 German-to-English translation. In addition, we conduct qualitative analysis to account for Sparse Transformer's superior performance.", "output": [ "This work propose Sparse Transformer to improve the concentration of attention on the global context through an explicit selection of the most relevant segments for sequence to sequence learning. " ] }, { "id": "task668-f0aae510b5c6446d86c28a7bbda9042a", "input": "This paper introduces the task of semantic instance completion: from an incomplete RGB-D scan of a scene, we aim to detect the individual object instances comprising the scene and infer their complete object geometry. This enables a semantically meaningful decomposition of a scanned scene into individual, complete 3D objects, including hidden and unobserved object parts. This will open up new possibilities for interactions with object in a scene, for instance for virtual or robotic agents. To address this task, we propose 3D-SIC, a new data-driven approach that jointly detects object instances and predicts their completed geometry. The core idea of 3D-SIC is a novel end-to-end 3D neural network architecture that leverages joint color and geometry feature learning. The fully-convolutional nature of our 3D network enables efficient inference of semantic instance completion for 3D scans at scale of large indoor environments in a single forward pass. In a series evaluation, we evaluate on both real and synthetic scan benchmark data, where we outperform state-of-the-art approaches by over 15 in mAP@0.5 on ScanNet, and over 18 in mAP@0.5 on SUNCG.", "output": [ "From an incomplete RGB-D scan of a scene, we aim to detect the individual object instances comprising the scene and infer their complete object geometry.", "Proposes an end-to-end 3D CNN structure which combines color features and 3D features to predict the missing 3D structure of a scene from RGB-D scans.", "The authors propose a novel end-to-end 3D convolutional network which predicts 3D semantic instance completion as object bounding boxes, class labels and complete object geometry." ] }, { "id": "task668-110f1d80b9b14fe28173f9d05b1174d6", "input": "This paper explores the scenarios under which\n an attacker can claim that ‘Noise and access to\n the softmax layer of the model is all you need’\n to steal the weights of a convolutional neural network\n whose architecture is already known. We\n were able to achieve 96% test accuracy using\n the stolen MNIST model and 82% accuracy using\n stolen KMNIST model learned using only\n i.i.d. Bernoulli noise inputs. We posit that this\n theft-susceptibility of the weights is indicative\n of the complexity of the dataset and propose a\n new metric that captures the same. The goal of\n this dissemination is to not just showcase how far\n knowing the architecture can take you in terms of\n model stealing, but to also draw attention to this\n rather idiosyncratic weight learnability aspects of\n CNNs spurred by i.i.d. noise input. We also disseminate\n some initial results obtained with using\n the Ising probability distribution in lieu of the i.i.d.\n Bernoulli distribution", "output": [ "Input only noise , glean the softmax outputs, steal the weights" ] }, { "id": "task668-eeea9f26f923463e8b6b55b7258671ba", "input": "The dependency of the generalization error of neural networks on model and dataset size is of critical importance both in practice and for understanding the theory of neural networks. Nevertheless, the functional form of this dependency remains elusive. In this work, we present a functional form which approximates well the generalization error in practice. Capitalizing on the successful concept of model scaling (e.g., width, depth), we are able to simultaneously construct such a form and specify the exact models which can attain it across model/data scales. Our construction follows insights obtained from observations conducted over a range of model/data scales, in various model types and datasets, in vision and language tasks. We show that the form both fits the observations well across scales, and provides accurate predictions from small- to large-scale models and data.", "output": [ "We predict the generalization error and specify the model which attains it across model/data scales." ] }, { "id": "task668-67d66bd6c7fa41269ed3dbc8a917bd02", "input": "The goal of imitation learning (IL) is to enable a learner to imitate expert behavior given expert demonstrations. Recently, generative adversarial imitation learning (GAIL) has shown significant progress on IL for complex continuous tasks. However, GAIL and its extensions require a large number of environment interactions during training. In real-world environments, the more an IL method requires the learner to interact with the environment for better imitation, the more training time it requires, and the more damage it causes to the environments and the learner itself. We believe that IL algorithms could be more applicable to real-world problems if the number of interactions could be reduced. \n In this paper, we propose a model-free IL algorithm for continuous control. Our algorithm is made up mainly three changes to the existing adversarial imitation learning (AIL) methods – (a) adopting off-policy actor-critic (Off-PAC) algorithm to optimize the learner policy, (b) estimating the state-action value using off-policy samples without learning reward functions, and (c) representing the stochastic policy function so that its outputs are bounded. Experimental results show that our algorithm achieves competitive results with GAIL while significantly reducing the environment interactions.", "output": [ "In this paper, we proposed a model-free, off-policy IL algorithm for continuous control. Experimental results showed that our algorithm achieves competitive results with GAIL while significantly reducing the environment interactions." ] }, { "id": "task668-782a47a2a7884ca79fda2fc98c365def", "input": "Voice Conversion (VC) is a task of converting perceived speaker identity from a source speaker to a particular target speaker. Earlier approaches in the literature primarily find a mapping between the given source-target speaker-pairs. Developing mapping techniques for many-to-many VC using non-parallel data, including zero-shot learning remains less explored areas in VC. Most of the many-to-many VC architectures require training data from all the target speakers for whom we want to convert the voices. In this paper, we propose a novel style transfer architecture, which can also be extended to generate voices even for target speakers whose data were not used in the training (i.e., case of zero-shot learning). In particular, propose Adaptive Generative Adversarial Network (AdaGAN), new architectural training procedure help in learning normalized speaker-independent latent representation, which will be used to generate speech with different speaking styles in the context of VC. We compare our results with the state-of-the-art StarGAN-VC architecture. In particular, the AdaGAN achieves 31.73%, and 10.37% relative improvement compared to the StarGAN in MOS tests for speech quality and speaker similarity, respectively. The key strength of the proposed architectures is that it yields these results with less computational complexity. AdaGAN is 88.6% less complex than StarGAN-VC in terms of FLoating Operation Per Second (FLOPS), and 85.46% less complex in terms of trainable parameters. ", "output": [ "Novel adaptive instance normalization based GAN framework for non parallel many-to-many and zero-shot VC. " ] }, { "id": "task668-8130f72c94a94983a40ee4d7e0d24095", "input": "The universal approximation theorem, in one of its most general versions, says that if we consider only continuous activation functions σ, then a standard feedforward neural network with one hidden layer is able to approximate any continuous multivariate function f to any given approximation threshold ε, if and only if σ is non-polynomial. In this paper, we give a direct algebraic proof of the theorem. Furthermore we shall explicitly quantify the number of hidden units required for approximation. Specifically, if X in R^n is compact, then a neural network with n input units, m output units, and a single hidden layer with {n+d choose d} hidden units (independent of m and ε), can uniformly approximate any polynomial function f:X -> R^m whose total degree is at most d for each of its m coordinate functions. In the general case that f is any continuous function, we show there exists some N in O(ε^{-n}) (independent of m), such that N hidden units would suffice to approximate f. We also show that this uniform approximation property (UAP) still holds even under seemingly strong conditions imposed on the weights. We highlight several consequences: (i) For any δ > 0, the UAP still holds if we restrict all non-bias weights w in the last layer to satisfy |w| < δ. (ii) There exists some λ>0 (depending only on f and σ), such that the UAP still holds if we restrict all non-bias weights w in the first layer to satisfy |w|>λ. (iii) If the non-bias weights in the first layer are *fixed* and randomly chosen from a suitable range, then the UAP holds with probability 1.", "output": [ "A quantitative refinement of the universal approximation theorem via an algebraic approach.", "The authors derive the universal approximation property proofs algebraically and assert that the results are general to other kinds of neural networks and similar learners.", "A new proof of Leshno's version of the universal approximation property for neural networks, and new insights into the universal approximation property." ] }, { "id": "task668-83e41ca41de64065b2740fd3d1bd36dc", "input": "Federated learning distributes model training among a multitude of agents, who, guided by privacy concerns, perform training using their local data but share only model parameter updates, for iterative aggregation at the server. In this work, we explore the threat of model poisoning attacks on federated learning initiated by a single, non-colluding malicious agent where the adversarial objective is to cause the model to misclassify a set of chosen inputs with high confidence. We explore a number of strategies to carry out this attack, starting with simple boosting of the malicious agent's update to overcome the effects of other agents' updates. To increase attack stealth, we propose an alternating minimization strategy, which alternately optimizes for the training loss and the adversarial objective. We follow up by using parameter estimation for the benign agents' updates to improve on attack success. Finally, we use a suite of interpretability techniques to generate visual explanations of model decisions for both benign and malicious models and show that the explanations are nearly visually indistinguishable. Our results indicate that even a highly constrained adversary can carry out model poisoning attacks while simultaneously maintaining stealth, thus highlighting the vulnerability of the federated learning setting and the need to develop effective defense strategies.", "output": [ "Effective model poisoning attacks on federated learning able to cause high-confidence targeted misclassification of desired inputs" ] }, { "id": "task668-1f3b928974ec4ff2a62031dbf8afd02e", "input": "Deep Convolution Neural Networks (CNNs), rooted by the pioneer work of \\cite{Hinton1986,LeCun1985,Alex2012}, and summarized in \\cite{LeCunBengioHinton2015}, have been shown to be very useful in a variety of fields. The state-of-the art CNN machines such as image rest net \\cite{He_2016_CVPR} are described by real value inputs and kernel convolutions followed by the local and non-linear rectified linear outputs. Understanding the role of these layers, the accuracy and limitations of them, as well as making them more efficient (fewer parameters) are all ongoing research questions. \n \n Inspired in quantum theory, we propose the use of complex value kernel functions, followed by the local non-linear absolute (modulus) operator square. We argue that an advantage of quantum inspired complex kernels is robustness to realistic unpredictable scenarios (such as clutter noise, data deformations). We study a concrete problem of shape detection and show that when multiple overlapping shapes are deformed and/or clutter noise is added, a convolution layer with quantum inspired complex kernels outperforms the statistical/classical kernel counterpart and a \"Bayesian shape estimator\" . The superior performance is due to the quantum phenomena of interference, not present in classical CNNs. ", "output": [ "A quantum inspired kernel for convolution network, exhibiting interference phenomena, can be very useful (and compared it with real value counterpart)." ] }, { "id": "task668-884f0a96967b4f2da106b9a8ee66c2a4", "input": "Presence of bias and confounding effects is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in the recent years. Such challenges range from spurious associations of confounding variables in medical studies to the bias of race in gender or face recognition systems. One solution is to enhance datasets and organize them such that they do not reflect biases, which is a cumbersome and intensive task. The alternative is to make use of available data and build models considering these biases. Traditional statistical methods apply straightforward techniques such as residualization or stratification to precomputed features to account for confounding variables. However, these techniques are not in general applicable to end-to-end deep learning methods. In this paper, we propose a method based on the adversarial training strategy to learn discriminative features unbiased and invariant to the confounder(s). This is enabled by incorporating a new adversarial loss function that encourages a vanished correlation between the bias and learned features. We apply our method to a synthetic, a medical diagnosis, and a gender classification (Gender Shades) dataset. Our results show that the learned features by our method not only result in superior prediction performance but also are uncorrelated with the bias or confounder variables. The code is available at http://blinded_for_review/.", "output": [ "We propose a method based on the adversarial training strategy to learn discriminative features unbiased and invariant to the confounder(s) by incorporating a loss function that encourages a vanished correlation between the bias and learned features." ] }, { "id": "task668-370ecb1b372145d1b88177103b1db7fc", "input": "Intelligent creatures can explore their environments and learn useful skills without supervision.\n In this paper, we propose ``Diversity is All You Need''(DIAYN), a method for learning useful skills without a reward function. Our proposed method learns skills by maximizing an information theoretic objective using a maximum entropy policy. On a variety of simulated robotic tasks, we show that this simple objective results in the unsupervised emergence of diverse skills, such as walking and jumping. In a number of reinforcement learning benchmark environments, our method is able to learn a skill that solves the benchmark task despite never receiving the true task reward. We show how pretrained skills can provide a good parameter initialization for downstream tasks, and can be composed hierarchically to solve complex, sparse reward tasks. Our results suggest that unsupervised discovery of skills can serve as an effective pretraining mechanism for overcoming challenges of exploration and data efficiency in reinforcement learning.", "output": [ "We propose an algorithm for learning useful skills without a reward function, and show how these skills can be used to solve downstream tasks." ] }, { "id": "task668-d889ccf16e284214bc194f17e881e708", "input": "Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-net and searching for an optimal architecture. In this paper, we present a novel approach, namely Partially-Connected DARTS, by sampling a small part of super-net to reduce the redundancy in exploring the network space, thereby performing a more efficient search without comprising the performance. In particular, we perform operation search in a subset of channels while bypassing the held out part in a shortcut. This strategy may suffer from an undesired inconsistency on selecting the edges of super-net caused by sampling different channels. We solve it by introducing edge normalization, which adds a new set of edge-level hyper-parameters to reduce uncertainty in search. Thanks to the reduced memory cost, PC-DARTS can be trained with a larger batch size and, consequently, enjoy both faster speed and higher training stability. Experiment results demonstrate the effectiveness of the proposed method. Specifically, we achieve an error rate of 2.57% on CIFAR10 within merely 0.1 GPU-days for architecture search, and a state-of-the-art top-1 error rate of 24.2% on ImageNet (under the mobile setting) within 3.8 GPU-days for search. Our code has been made available at https://www.dropbox.com/sh/on9lg3rpx1r6dkf/AABG5mt0sMHjnEJyoRnLEYW4a?dl=0 .", "output": [ "Allowing partial channel connection in super-networks to regularize and accelerate differentiable architecture search", "An extension of the neural architecture search method DARTS that addresses its shortcoming of immense memory cost by using a random subset of channels and a method to normalize edges.", "This paper proposes to improve DARTS in terms of training efficiency, from large memory and computing overheads, and proposes a partially-connected DARTS with partial channel connection and edge normalization." ] }, { "id": "task668-690cbc94b4114b87b5ee276a3074fcea", "input": "It has been shown that using geometric spaces with non-zero curvature instead of plain Euclidean spaces with zero curvature improves performance on a range of Machine Learning tasks for learning representations. Recent work has leveraged these geometries to learn latent variable models like Variational Autoencoders (VAEs) in spherical and hyperbolic spaces with constant curvature. While these approaches work well on particular kinds of data that they were designed for e.g.~tree-like data for a hyperbolic VAE, there exists no generic approach unifying all three models. We develop a Mixed-curvature Variational Autoencoder, an efficient way to train a VAE whose latent space is a product of constant curvature Riemannian manifolds, where the per-component curvature can be learned. This generalizes the Euclidean VAE to curved latent spaces, as the model essentially reduces to the Euclidean VAE if curvatures of all latent space components go to 0.", "output": [ "Variational Autoencoders with latent spaces modeled as products of constant curvature Riemannian manifolds improve on image reconstruction over single-manifold variants.", "This paper introduces a general formulation of the notion of a VAE with a latent space composed by a curved manifold.", "This paper is about developing VAEs in non-Euclidean spaces." ] }, { "id": "task668-a747ef9b9506428c96c97c847157738f", "input": "Visual grounding of language is an active research field aiming at enriching text-based representations with visual information. In this paper, we propose a new way to leverage visual knowledge for sentence representations. Our approach transfers the structure of a visual representation space to the textual space by using two complementary sources of information: (1) the cluster information: the implicit knowledge that two sentences associated with the same visual content describe the same underlying reality and (2) the perceptual information contained within the structure of the visual space. We use a joint approach to encourage beneficial interactions during training between textual, perceptual, and cluster information. We demonstrate the quality of the learned representations on semantic relatedness, classification, and cross-modal retrieval tasks.", "output": [ "We propose a joint model to incorporate visual knowledge in sentence representations", "The paper proposes a method to use videos paired with captions to improve sentence embeddings", "This submission proposes a model for sentence learning sentence representations that are grounded, based on associated video data.", "Proposes a method for improving text-based sentence embeddings through a joint multimodal framework." ] }, { "id": "task668-2410c56c6ce44023a78bb4ae0015fefd", "input": "Deep learning (DL) has in recent years been widely used in natural\n language processing (NLP) applications due to its superior\n performance. However, while natural languages are rich in\n grammatical structure, DL has not been able to explicitly\n represent and enforce such structures. This paper proposes a new\n architecture to bridge this gap by exploiting tensor product\n representations (TPR), a structured neural-symbolic framework\n developed in cognitive science over the past 20 years, with the\n aim of integrating DL with explicit language structures and rules.\n We call it the Tensor Product Generation Network\n (TPGN), and apply it to image captioning. The key\n ideas of TPGN are: 1) unsupervised learning of\nrole-unbinding vectors of words via a TPR-based deep neural\nnetwork, and 2) integration of TPR with typical DL architectures\n including Long Short-Term Memory (LSTM) models. The novelty of our\n approach lies in its ability to generate a sentence and extract\n partial grammatical structure of the sentence by using\n role-unbinding vectors, which are obtained in an unsupervised\n manner. Experimental results demonstrate the effectiveness of the\n proposed approach.", "output": [ "This paper is intended to develop a tensor product representation approach for deep-learning-based natural language processinig applications." ] }, { "id": "task668-7d2e7da7c0e94843b97183bcc2620fc5", "input": "For the challenging semantic image segmentation task the best performing models\n have traditionally combined the structured modelling capabilities of Conditional\n Random Fields (CRFs) with the feature extraction power of CNNs. In more recent\n works however, CRF post-processing has fallen out of favour. We argue that this\n is mainly due to the slow training and inference speeds of CRFs, as well as the\n difficulty of learning the internal CRF parameters. To overcome both issues we\n propose to add the assumption of conditional independence to the framework of\n fully-connected CRFs. This allows us to reformulate the inference in terms of\n convolutions, which can be implemented highly efficiently on GPUs.Doing so\n speeds up inference and training by two orders of magnitude. All parameters of\n the convolutional CRFs can easily be optimized using backpropagation. Towards\n the goal of facilitating further CRF research we have made our implementations\n publicly available.", "output": [ "We propose Convolutional CRFs a fast, powerful and trainable alternative to Fully Connected CRFs.", "The authors replace the large filtering step in the permutohedral lattice with a spatially varying convolutional kernel and show that inference is more efficient and training is easier. ", "Proposes to perform message passing on a truncated Gaussian kernel CRF using a defined kernel and parallelized message passing on GPU." ] }, { "id": "task668-ac4acf5f1ef140418bb0187604f3f04a", "input": "This paper proposes a new approach for step size adaptation in gradient methods. The proposed method called step size optimization (SSO) formulates the step size adaptation as an optimization problem which minimizes the loss function with respect to the step size for the given model parameters and gradients. Then, the step size is optimized based on alternating direction method of multipliers (ADMM). SSO does not require the second-order information or any probabilistic models for adapting the step size, so it is efficient and easy to implement. Furthermore, we also introduce stochastic SSO for stochastic learning environments. In the experiments, we integrated SSO to vanilla SGD and Adam, and they outperformed state-of-the-art adaptive gradient methods including RMSProp, Adam, L4-Adam, and AdaBound on extensive benchmark datasets.", "output": [ "We propose an efficient and effective step size adaptation method for the gradient methods.", "A new step size adaptation in first-order gradient methods that establishes a new optimization problem with the first-order expansion of the loss function and regularization, where step size is treated as variable." ] }, { "id": "task668-de8a9fbdeea1457f8b61090092e233ea", "input": "Neural networks offer high-accuracy solutions to a range of problems, but are computationally costly to run in production systems. We propose a technique called Deep Learning Approximation to take an already-trained neural network model and build a faster (and almost equally accurate) network by manipulating the network structure and coefficients without requiring re-training or access to the training data. Speedup is achieved by applying a sequential series of independent optimizations that reduce the floating-point operations (FLOPs) required to perform a forward pass. An optimal lossy approximation is chosen for each layer by weighing the relative accuracy loss and FLOP reduction. On PASCAL VOC 2007 with the YOLO network, we show an end-to-end 2x speedup in a network forward pass with a $5$\\% drop in mAP that can be re-gained by finetuning, enabling this network (and others like it) to be deployed in compute-constrained systems.", "output": [ "Decompose weights to use fewer FLOPs with SVD" ] }, { "id": "task668-ed912520d09e4d82a60e83f0f3e80820", "input": "Temporal difference (TD) learning is a popular algorithm for policy evaluation in reinforcement learning, but the vanilla TD can substantially suffer from the inherent optimization variance. A variance reduced TD (VRTD) algorithm was proposed by Korda and La (2015), which applies the variance reduction technique directly to the online TD learning with Markovian samples. In this work, we first point out the technical errors in the analysis of VRTD in Korda and La (2015), and then provide a mathematically solid analysis of the non-asymptotic convergence of VRTD and its variance reduction performance. We show that VRTD is guaranteed to converge to a neighborhood of the fixed-point solution of TD at a linear convergence rate. Furthermore, the variance error (for both i.i.d. and Markovian sampling) and the bias error (for Markovian sampling) of VRTD are significantly reduced by the batch size of variance reduction in comparison to those of vanilla TD.", "output": [ "This paper provides a rigorous study of the variance reduced TD learning and characterizes its advantage over vanilla TD learning" ] }, { "id": "task668-e698cee3e91b4e77a6cab173a8aff0f7", "input": "State-of-the-art machine learning methods exhibit limited compositional generalization. At the same time, there is a lack of realistic benchmarks that comprehensively measure this ability, which makes it challenging to find and evaluate improvements. We introduce a novel method to systematically construct such benchmarks by maximizing compound divergence while guaranteeing a small atom divergence between train and test sets, and we quantitatively compare this method to other approaches for creating compositional generalization benchmarks. We present a large and realistic natural language question answering dataset that is constructed according to this method, and we use it to analyze the compositional generalization ability of three machine learning architectures. We find that they fail to generalize compositionally and that there is a surprisingly strong negative correlation between compound divergence and accuracy. We also demonstrate how our method can be used to create new compositionality benchmarks on top of the existing SCAN dataset, which confirms these findings.\n", "output": [ "Benchmark and method to measure compositional generalization by maximizing divergence of compound frequency at small divergence of atom frequency." ] }, { "id": "task668-35c0a0f7eba34d3788d634975f8ca158", "input": "Capsule Networks have shown encouraging results on defacto benchmark computer vision datasets such as MNIST, CIFAR and smallNORB. Although, they are yet to be tested on tasks where (1) the entities detected inherently have more complex internal representations and (2) there are very few instances per class to learn from and (3) where point-wise classification is not suitable. Hence, this paper carries out experiments on face verification in both controlled and uncontrolled settings that together address these points. In doing so we introduce Siamese Capsule Networks, a new variant that can be used for pairwise learning tasks. The model is trained using contrastive loss with l2-normalized capsule encoded pose features. We find that Siamese Capsule Networks perform well against strong baselines on both pairwise learning datasets, yielding best results in the few-shot learning setting where image pairs in the test set contain unseen subjects.", "output": [ "A variant of capsule networks that can be used for pairwise learning tasks. Results shows that Siamese Capsule Networks work well in the few shot learning setting." ] }, { "id": "task668-6202c3f4e43b46d394ff8ad2717dac3d", "input": "We study the control of symmetric linear dynamical systems with unknown dynamics and a hidden state. Using a recent spectral filtering technique for concisely representing such systems in a linear basis, we formulate optimal control in this setting as a convex program. This approach eliminates the need to solve the non-convex problem of explicit identification of the system and its latent state, and allows for provable optimality guarantees for the control signal. We give the first efficient algorithm for finding the optimal control signal with an arbitrary time horizon T, with sample complexity (number of training rollouts) polynomial only in log(T) and other relevant parameters.", "output": [ "Using a novel representation of symmetric linear dynamical systems with a latent state, we formulate optimal control as a convex program, giving the first polynomial-time algorithm that solves optimal control with sample complexity only polylogarithmic in the time horizon." ] }, { "id": "task668-eb1b5676442942609654b51cd41e0dda", "input": "Modern neural networks are over-parametrized. In particular, each rectified linear hidden unit can be modified by a multiplicative factor by adjusting input and out- put weights, without changing the rest of the network. Inspired by the Sinkhorn-Knopp algorithm, we introduce a fast iterative method for minimizing the l2 norm of the weights, equivalently the weight decay regularizer. It provably converges to a unique solution. Interleaving our algorithm with SGD during training improves the test accuracy. For small batches, our approach offers an alternative to batch- and group- normalization on CIFAR-10 and ImageNet with a ResNet-18.", "output": [ "Fast iterative algorithm to balance the energy of a network while staying in the same functional equivalence class" ] }, { "id": "task668-346d58cc9fb84d9db77bd3f4df43c0a2", "input": "This paper introduces CloudLSTM, a new branch of recurrent neural models tailored to forecasting over data streams generated by geospatial point-cloud sources. We design a Dynamic Point-cloud Convolution (D-Conv) operator as the core component of CloudLSTMs, which performs convolution directly over point-clouds and extracts local spatial features from sets of neighboring points that surround different elements of the input. This operator maintains the permutation invariance of sequence-to-sequence learning frameworks, while representing neighboring correlations at each time step -- an important aspect in spatiotemporal predictive learning. The D-Conv operator resolves the grid-structural data requirements of existing spatiotemporal forecasting models and can be easily plugged into traditional LSTM architectures with sequence-to-sequence learning and attention mechanisms.\n We apply our proposed architecture to two representative, practical use cases that involve point-cloud streams, i.e. mobile service traffic forecasting and air quality indicator forecasting. Our results, obtained with real-world datasets collected in diverse scenarios for each use case, show that CloudLSTM delivers accurate long-term predictions, outperforming a variety of neural network models.", "output": [ "This paper introduces CloudLSTM, a new branch of recurrent neural models tailored to forecasting over data streams generated by geospatial point-cloud sources." ] }, { "id": "task668-5aa48c19558d442aa970a3ffd551dcce", "input": "Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust and efficient measures of uncertainty are crucial. While it is possible to train regression networks to output the parameters of a probability distribution by maximizing a Gaussian likelihood function, the resulting model remains oblivious to the underlying confidence of its predictions. In this paper, we propose a novel method for training deterministic NNs to not only estimate the desired target but also the associated evidence in support of that target. We accomplish this by placing evidential priors over our original Gaussian likelihood function and training our NN to infer the hyperparameters of our evidential distribution. We impose priors during training such that the model is penalized when its predicted evidence is not aligned with the correct output. Thus the model estimates not only the probabilistic mean and variance of our target but also the underlying uncertainty associated with each of those parameters. We observe that our evidential regression method learns well-calibrated measures of uncertainty on various benchmarks, scales to complex computer vision tasks, and is robust to adversarial input perturbations.\n", "output": [ "Fast, calibrated uncertainty estimation for neural networks without sampling", "This paper proposes a novel approach to estimate the confidence of predictions in a regression setting, opening the door to online applications with fully integrated uncertainty estimates.", "This paper proposed deep evidential regression, a method for training neural networks to not only estimate the output but also the associated evidence in support of that output." ] }, { "id": "task668-211036e7755142e08d4472547bc583b8", "input": "We propose a novel way of reducing the number of parameters in the storage-hungry fully connected layers of a neural network by using pre-defined sparsity, where the majority of connections are absent prior to starting training. Our results indicate that convolutional neural networks can operate without any loss of accuracy at less than 0.5% classification layer connection density, or less than 5% overall network connection density. We also investigate the effects of pre-defining the sparsity of networks with only fully connected layers. Based on our sparsifying technique, we introduce the `scatter' metric to characterize the quality of a particular connection pattern. As proof of concept, we show results on CIFAR, MNIST and a new dataset on classifying Morse code symbols, which highlights some interesting trends and limits of sparse connection patterns.", "output": [ "Neural networks can be pre-defined to have sparse connectivity without performance degradation.", "This paper examines sparse connection patterns in upper layers of convolutional image classification networks, and introduces heuristics for distributing connections among windows/groups and a measure called scatter to construct connectivity masks.", "Proposal to reduce the number of parameters learned by a deep network by setting up sparse connection weights in classiication layers, and introduction of a concept of \"scatter.\"" ] }, { "id": "task668-d7387502e2fa4822bf94f43a48c6789e", "input": "Good representations facilitate transfer learning and few-shot learning. Motivated by theories of language and communication that explain why communities with large number of speakers have, on average, simpler languages with more regularity, we cast the representation learning problem in terms of learning to communicate. Our starting point sees traditional autoencoders as a single encoder with a fixed decoder partner that must learn to communicate. Generalizing from there, we introduce community-based autoencoders in which multiple encoders and decoders collectively learn representations by being randomly paired up on successive training iterations. Our experiments show that increasing community sizes reduce idiosyncrasies in the learned codes, resulting in more invariant representations with increased reusability and structure.", "output": [ "Motivated by theories of language and communication, we introduce community-based autoencoders, in which multiple encoders and decoders collectively learn structured and reusable representations.", "The authors tackle the problem of representation learning, aim to build reusable and structured represenation, argue co-adaptation between encoder and decoder in traditional AE yields poor representation, and introduce community based auto-encoders.", "The paper presents a community based autoencoder framework to address co-adaptation of encoders and decoders and aims at constructing better representations." ] }, { "id": "task668-a0cfa0d4450947d9bcfc227483b8b24b", "input": "We propose a novel architecture for k-shot classification on the Omniglot dataset. Building on prototypical networks, we extend their architecture to what we call Gaussian prototypical networks. Prototypical networks learn a map between images and embedding vectors, and use their clustering for classification. In our model, a part of the encoder output is interpreted as a confidence region estimate about the embedding point, and expressed as a Gaussian covariance matrix. Our network then constructs a direction and class dependent distance metric on the embedding space, using uncertainties of individual data points as weights. We show that Gaussian prototypical networks are a preferred architecture over vanilla prototypical networks with an equivalent number of parameters. We report results consistent with state-of-the-art performance in 1-shot and 5-shot classification both in 5-way and 20-way regime on the Omniglot dataset. We explore artificially down-sampling a fraction of images in the training set, which improves our performance. Our experiments therefore lead us to hypothesize that Gaussian prototypical networks might perform better in less homogeneous, noisier datasets, which are commonplace in real world applications.", "output": [ "A novel architecture for few-shot classification capable of dealing with uncertainty." ] }, { "id": "task668-11f9a282533a4c4687f88fe1888b221d", "input": "Asynchronous distributed methods are a popular way to reduce the communication and synchronization costs of large-scale optimization. Yet, for all their success, little is known about their convergence guarantees in the challenging case of general non-smooth, non-convex objectives, beyond cases where closed-form proximal operator solutions are available.\n This is all the more surprising since these objectives are the ones appearing in the training of deep neural networks.\n\n In this paper, we introduce the first convergence analysis covering asynchronous methods in the case of general non-smooth, non-convex objectives. Our analysis applies to stochastic sub-gradient descent methods both with and without block variable partitioning, and both with and without momentum. It is phrased in the context of a general probabilistic model of asynchronous scheduling accurately adapted to modern hardware properties. We validate our analysis experimentally in the context of training deep neural network architectures. We show their overall successful asymptotic convergence as well as exploring how momentum, synchronization, and partitioning all affect performance.", "output": [ "Asymptotic convergence for stochastic subgradien method with momentum under general parallel asynchronous computation for general nonconvex nonsmooth optimization" ] }, { "id": "task668-0fa4209bad9a47b9a5ad1998e72a9129", "input": "Clustering algorithms have wide applications and play an important role in data analysis fields including time series data analysis. The performance of a clustering algorithm depends on the features extracted from the data. However, in time series analysis, there has been a problem that the conventional methods based on the signal shape are unstable for phase shift, amplitude and signal length variations.\n In this paper, we propose a new clustering algorithm focused on the dynamical system aspect of the signal using recurrent neural network and variational Bayes method. Our experiments show that our proposed algorithm has a robustness against above variations and boost the classification performance.", "output": [ "Novel time series data clustring algorithm based on dynamical system features." ] }, { "id": "task668-5e2dfa84f47548d2966a807b91fa1779", "input": "We introduce Neural Markov Logic Networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic. Like Markov Logic Networks (MLNs), NMLNs are an exponential-family model for modelling distributions over possible worlds, but unlike MLNs, they do not rely on explicitly specified first-order logic rules. Instead, NMLNs learn an implicit representation of such rules as a neural network that acts as a potential function on fragments of the relational structure. Interestingly, any MLN can be represented as an NMLN. Similarly to recently proposed Neural theorem provers (NTPs) (Rocktaschel at al. 2017), NMLNs can exploit embeddings of constants but, unlike NTPs, NMLNs work well also in their absence. This is extremely important for predicting in settings other than the transductive one. We showcase the potential of NMLNs on knowledge-base completion tasks and on generation of molecular (graph) data.", "output": [ " We introduce a statistical relational learning system that borrows ideas from Markov logic but learns an implicit representation of rules as a neural network.", "The paper provides an extension to Markov Logic Networks by removing their dependency on pre-defined first-order logic rules to model more domains in knowledge-base completion tasks." ] }, { "id": "task668-4aa8215fcf204a95bb5a489f90648659", "input": "Informed and robust decision making in the face of uncertainty is critical for robots that perform physical tasks alongside people. We formulate this as a Bayesian Reinforcement Learning problem over latent Markov Decision Processes (MDPs). While Bayes-optimality is theoretically the gold standard, existing algorithms do not scale well to continuous state and action spaces. We propose a scalable solution that builds on the following insight: in the absence of uncertainty, each latent MDP is easier to solve. We split the challenge into two simpler components. First, we obtain an ensemble of clairvoyant experts and fuse their advice to compute a baseline policy. Second, we train a Bayesian residual policy to improve upon the ensemble's recommendation and learn to reduce uncertainty. Our algorithm, Bayesian Residual Policy Optimization (BRPO), imports the scalability of policy gradient methods as well as the initialization from prior models. BRPO significantly improves the ensemble of experts and drastically outperforms existing adaptive RL methods.", "output": [ "We propose a scalable Bayesian Reinforcement Learning algorithm that learns a Bayesian correction over an ensemble of clairvoyant experts to solve problems with complex latent rewards and dynamics.", "This paper considers Bayesian Reinforcement Learning problem over latent Markov Decision Processes (MDPs) by making decisions with experts.", "In this paper, the authors motivate and propose a learning algorithm, called Bayesian Residual Policy Optimization (BRPO), for Bayesian reinforcement learning problems." ] }, { "id": "task668-3765ebbd4d8f425ca87c452acdcd921e", "input": "We provide a theoretical algorithm for checking local optimality and escaping saddles at nondifferentiable points of empirical risks of two-layer ReLU networks. Our algorithm receives any parameter value and returns: local minimum, second-order stationary point, or a strict descent direction. The presence of M data points on the nondifferentiability of the ReLU divides the parameter space into at most 2^M regions, which makes analysis difficult. By exploiting polyhedral geometry, we reduce the total computation down to one convex quadratic program (QP) for each hidden node, O(M) (in)equality tests, and one (or a few) nonconvex QP. For the last QP, we show that our specific problem can be solved efficiently, in spite of nonconvexity. In the benign case, we solve one equality constrained QP, and we prove that projected gradient descent solves it exponentially fast. In the bad case, we have to solve a few more inequality constrained QPs, but we prove that the time complexity is exponential only in the number of inequality constraints. Our experiments show that either benign case or bad case with very few inequality constraints occurs, implying that our algorithm is efficient in most cases.", "output": [ "A theoretical algorithm for testing local optimality and extracting descent directions at nondifferentiable points of empirical risks of one-hidden-layer ReLU networks.", "Proposes an algorithm to check whether a given point is a generalized second-order stationary point.", "A theoretical algorithm, involving solving convex and non-convex quadratic programs, for checking local optimality and escaping saddles when training two-layer ReLU networks.", "Author proposes a method to check if a point is a stationary point or not and then classify stationary points as either local min or second-order stationary" ] }, { "id": "task668-df4e243da077424da6e16f66c02813e8", "input": "The performance of deep neural networks is often attributed to their automated, task-related feature construction. It remains an open question, though, why this leads to solutions with good generalization, even in cases where the number of parameters is larger than the number of samples. Back in the 90s, Hochreiter and Schmidhuber observed that flatness of the loss surface around a local minimum correlates with low generalization error. For several flatness measures, this correlation has been empirically validated. However, it has recently been shown that existing measures of flatness cannot theoretically be related to generalization: if a network uses ReLU activations, the network function can be reparameterized without changing its output in such a way that flatness is changed almost arbitrarily. This paper proposes a natural modification of existing flatness measures that results in invariance to reparameterization. The proposed measures imply a robustness of the network to changes in the input and the hidden layers. Connecting this feature robustness to generalization leads to a generalized definition of the representativeness of data. With this, the generalization error of a model trained on representative data can be bounded by its feature robustness which depends on our novel flatness measure.", "output": [ "We introduce a novel measure of flatness at local minima of the loss surface of deep neural networks which is invariant with respect to layer-wise reparameterizations and we connect flatness to feature robustness and generalization.", "The authors propose a notion of feature robustness which is invariant with respect to rescaling the weight and discuss the notion's relationship to generalization.", "This paper defines a notion of feature-robustness and combines it with epsilon representativeness of a function to describe a connection between flatness of minima and generalization in deep neural networks." ] }, { "id": "task668-28d192b090414ee2921fca76cc8ebb68", "input": "The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning. In this work, we discuss the required elements of a robotic system that can continually and autonomously improve with data collected in the real world, and propose a particular instantiation of such a system. Subsequently, we investigate a number of challenges of learning without instrumentation -- including the lack of episodic resets, state estimation, and hand-engineered rewards -- and propose simple, scalable solutions to these challenges. We demonstrate the efficacy of our proposed system on dexterous robotic manipulation tasks in simulation and the real world, and also provide an insightful analysis and ablation study of the challenges associated with this learning paradigm.", "output": [ "System to learn robotic tasks in the real world with reinforcement learning without instrumentation" ] }, { "id": "task668-2556c05c1f8545be91149bc78ed283f8", "input": "In this paper, we propose an arbitrarily-conditioned data imputation framework built upon variational autoencoders and normalizing flows. The proposed model is capable of mapping any partial data to a multi-modal latent variational distribution. Sampling from such a distribution leads to stochastic imputation. Preliminary evaluation on MNIST dataset shows promising stochastic imputation conditioned on partial images as input.", "output": [ "We propose an arbitrarily-conditioned data imputation framework built upon variational autoencoders and normalizing flows" ] }, { "id": "task668-0d762e65aa5440188fc75304aa47e234", "input": "Automatic question generation from paragraphs is an important and challenging problem, particularly due to the long context from paragraphs. In this paper, we propose and study two hierarchical models for the task of question generation from paragraphs. Specifically, we propose (a) a novel hierarchical BiLSTM model with selective attention and (b) a novel hierarchical Transformer architecture, both of which learn hierarchical representations of paragraphs. \n We model a paragraph in terms of its constituent sentences, and a sentence in terms of its constituent words. While the introduction of the attention mechanism benefits the hierarchical BiLSTM model, the hierarchical Transformer, with its inherent attention and positional encoding mechanisms also performs better than flat transformer model.\n We conducted empirical evaluation on the widely used SQuAD and MS MARCO datasets using standard metrics. \n The results demonstrate the overall effectiveness of the hierarchical models over their flat counterparts. \n Qualitatively, our hierarchical models are able to generate fluent and relevant questions.\n", "output": [ "Automatic question generation from paragraph using hierarchical models" ] }, { "id": "task668-f48d62ea39024a618237cbbe390ee241", "input": "Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, but need to overcome the challenge of maintaining high accuracy as precision decreases. Here, we present a method for training such networks, Learned Step Size Quantization, that achieves the highest accuracy to date on the ImageNet dataset when using models, from a variety of architectures, with weights and activations quantized to 2-, 3- or 4-bits of precision, and that can train 3-bit models that reach full precision baseline accuracy. Our approach builds upon existing methods for learning weights in quantized networks by improving how the quantizer itself is configured. Specifically, we introduce a novel means to estimate and scale the task loss gradient at each weight and activation layer's quantizer step size, such that it can be learned in conjunction with other network parameters. This approach works using different levels of precision as needed for a given system and requires only a simple modification of existing training code.", "output": [ "A method for learning quantization configuration for low precision networks that achieves state of the art performance for quantized networks." ] }, { "id": "task668-c84388cb1f2549e3bad57dfa691b9d08", "input": "Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step. In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results. Next, we introduce dynamic convolutions which are simpler and more efficient than self-attention. We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements. The number of operations required by this approach scales linearly in the input length, whereas self-attention is quadratic. Experiments on large-scale machine translation, language modeling and abstractive summarization show that dynamic convolutions improve over strong self-attention models. On the WMT'14 English-German test set dynamic convolutions achieve a new state of the art of 29.7 BLEU.", "output": [ "Dynamic lightweight convolutions are competitive to self-attention on language tasks." ] }, { "id": "task668-5d5f940d1a3b4d84be7ba8d5e06b34b4", "input": "The International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS) plays a pivotal role in fostering the development of new Knowledge Engineering (KE) tools, and in emphasising the importance of principled approaches for all the different KE aspects that are needed for the successful long-term use of planning in real-world applications. \n In this paper, as an exercise in synthesis and for the sake of stimulating thoughts and discussion, we review the format of previous ICKEPS, to suggest alternative formats for future competitions, ideally to motivate someone to step up and organise the next ones.", "output": [ "Ideas for future ICKEPS" ] }, { "id": "task668-f7a21a80c47249458d05cb24eb1e5c6f", "input": "High-dimensional time series are common in many domains. Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations. However, most representation learning algorithms for time series data are difficult to interpret. This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time.\n To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling. This framework allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance. We introduce a new way to overcome the non-differentiability in discrete representation learning and present a gradient-based version of the traditional self-organizing map algorithm that is more performant than the original. Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the representation space.\n This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty.\n We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set. Our learned representations compare favorably with competitor methods and facilitate downstream tasks on the real world data.", "output": [ "We present a method to learn interpretable representations on time series using ideas from variational autoencoders, self-organizing maps and probabilistic models." ] }, { "id": "task668-5460eb5e6e564c0baa12b18c41126c16", "input": "We propose a metric-learning framework for computing distance-preserving maps that generate low-dimensional embeddings for a certain class of manifolds. We employ Siamese networks to solve the problem of least squares multidimensional scaling for generating mappings that preserve geodesic distances on the manifold. In contrast to previous parametric manifold learning methods we show a substantial reduction in training effort enabled by the computation of geodesic distances in a farthest point sampling strategy. Additionally, the use of a network to model the distance-preserving map reduces the complexity of the multidimensional scaling problem and leads to an improved non-local generalization of the manifold compared to analogous non-parametric counterparts. We demonstrate our claims on point-cloud data and on image manifolds and show a numerical analysis of our technique to facilitate a greater understanding of the representational power of neural networks in modeling manifold data.", "output": [ "Parametric Manifold Learning with Neural Networks in a Geometric Framework " ] }, { "id": "task668-e9029686c44e448b80660742221894b8", "input": "Learning a better representation with neural networks is a challenging problem, which has been tackled from different perspectives in the past few years. In this work, we focus on learning a representation that would be useful in a clustering task. We introduce two novel loss components that substantially improve the quality of produced clusters, are simple to apply to arbitrary models and cost functions, and do not require a complicated training procedure. We perform an extensive set of experiments, supervised and unsupervised, and evaluate the proposed loss components on two most common types of models, Recurrent Neural Networks and Convolutional Neural Networks, showing that the approach we propose consistently improves the quality of KMeans clustering in terms of mutual information scores and outperforms previously proposed methods.", "output": [ "A novel loss component that forces the network to learn a representation that is well-suited for clustering during training for a classification task.", "This paper proposes two regularization terms based on a compound hinge loss over the KL divergence between two softmax-normalized input arguments to encourage learning disentangled representations", "Proposal for two regularizers intended to make the representations learned in the penultimate layer of a classifier more conforming to inherent structure in the data." ] }, { "id": "task668-3d6d9bd0c88e46a6aceda23e22aa5248", "input": "Su-Boyd-Candes (2014) made a connection between Nesterov's method and an ordinary differential equation (ODE). We show if a Hessian damping term is added to the ODE from Su-Boyd-Candes (2014), then Nesterov's method arises as a straightforward discretization of the modified ODE. Analogously, in the strongly convex case, a Hessian damping term is added to Polyak's ODE, which is then discretized to yield Nesterov's method for strongly convex functions. Despite the Hessian term, both second order ODEs can be represented as first order systems.\n\n Established Liapunov analysis is used to recover the accelerated rates of convergence in both continuous and discrete time. Moreover, the Liapunov analysis can be extended to the case of stochastic gradients which allows the full gradient case to be considered as a special case of the stochastic case. The result is a unified approach to convex acceleration in both continuous and discrete time and in both the stochastic and full gradient cases. \n", "output": [ "We derive Nesterov's method arises as a straightforward discretization of an ODE different from the one in Su-Boyd-Candes and prove acceleration the stochastic case" ] }, { "id": "task668-50997b50b9e745e79c5cf17f80f901aa", "input": "Training recurrent neural networks (RNNs) on long sequences using backpropagation through time (BPTT) remains a fundamental challenge. \n It has been shown that adding a local unsupervised loss term into the optimization objective makes the training of RNNs on long sequences more effective. \n While the importance of an unsupervised task can in principle be controlled by a coefficient in the objective function, the gradients with respect to the unsupervised loss term still influence all the hidden state dimensions, which might cause important information about the supervised task to be degraded or erased. \n Compared to existing semi-supervised sequence learning methods, this paper focuses upon a traditionally overlooked mechanism -- an architecture with explicitly designed private and shared hidden units designed to mitigate the detrimental influence of the auxiliary unsupervised loss over the main supervised task.\n We achieve this by dividing RNN hidden space into a private space for the supervised task and a shared space for both the supervised and unsupervised tasks. We present extensive experiments with the proposed framework on several long sequence modeling benchmark datasets. Results indicate that the proposed framework can yield performance gains in RNN models where long term dependencies are notoriously challenging to deal with.", "output": [ "This paper focuses upon a traditionally overlooked mechanism -- an architecture with explicitly designed private and shared hidden units designed to mitigate the detrimental influence of the auxiliary unsupervised loss over the main supervised task." ] }, { "id": "task668-d5ee1b31304b47d8b6321adab20c1fed", "input": "Dialogue research tends to distinguish between chit-chat and goal-oriented tasks. While the former is arguably more naturalistic and has a wider use of language, the latter has clearer metrics and a more straightforward learning signal. Humans effortlessly combine the two, and engage in chit-chat for example with the goal of exchanging information or eliciting a specific response. Here, we bridge the divide between these two domains in the setting of a rich multi-player text-based fantasy environment where agents and humans engage in both actions and dialogue. Specifically, we train a goal-oriented model with reinforcement learning via self-play against an imitation-learned chit-chat model with two new approaches: the policy either learns to pick a topic or learns to pick an utterance given the top-k utterances. We show that both models outperform a strong inverse model baseline and can converse naturally with their dialogue partner in order to achieve goals.", "output": [ "Agents interact (speak, act) and can achieve goals in a rich world with diverse language, bridging the gap between chit-chat and goal-oriented dialogue.", "This paper studies a multiagent dialog task in which the learning agent aims to generate natural language actions that elicit a particular action from the other agent, and shows RL-agents can achieve higher task completion levels than imitation learning baselines.", "This paper explores the goal-oriented dialogue setting with reinforcement learning in a Fantasy Text Adventure Game and observes that the RL approaches outperform supervised learning models." ] }, { "id": "task668-36e7edfa498744e994b891aab3b3d974", "input": "Neural Network (NN) has achieved state-of-the-art performances in many tasks within image, speech, and text domains. Such great success is mainly due to special structure design to fit the particular data patterns, such as CNN capturing spatial locality and RNN modeling sequential dependency. Essentially, these specific NNs achieve good performance by leveraging the prior knowledge over corresponding domain data. Nevertheless, there are many applications with all kinds of tabular data in other domains. Since there are no shared patterns among these diverse tabular data, it is hard to design specific structures to fit them all. Without careful architecture design based on domain knowledge, it is quite challenging for NN to reach satisfactory performance in these tabular data domains. To fill the gap of NN in tabular data learning, we propose a universal neural network solution, called TabNN, to derive effective NN architectures for tabular data in all kinds of tasks automatically. Specifically, the design of TabNN follows two principles: \\emph{to explicitly leverages expressive feature combinations} and \\emph{to reduce model complexity}. Since GBDT has empirically proven its strength in modeling tabular data, we use GBDT to power the implementation of TabNN. Comprehensive experimental analysis on a variety of tabular datasets demonstrate that TabNN can achieve much better performance than many baseline solutions.", "output": [ "We propose a universal neural network solution to derive effective NN architectures for tabular data automatically.", "A new Neural Network training procedure, designed for tabular data, that seeks to leverage feature clusters extracted from GBDTs.", "Proposal for a hybrid machine learning algorithm using Gradient Boosted Decision Trees and Deep Neural Networks, with intended research direction on tabular data." ] }, { "id": "task668-2e2b8713b2f04a51bef0a6e7d275e4ae", "input": "Building upon the recent success of deep reinforcement learning methods, we investigate the possibility of on-policy reinforcement learning improvement by reusing the data from several consecutive policies. On-policy methods bring many benefits, such as ability to evaluate each resulting policy. However, they usually discard all the information about the policies which existed before. In this work, we propose adaptation of the replay buffer concept, borrowed from the off-policy learning setting, to the on-policy algorithms. To achieve this, the proposed algorithm generalises the Q-, value and advantage functions for data from multiple policies. The method uses trust region optimisation, while avoiding some of the common problems of the algorithms such as TRPO or ACKTR: it uses hyperparameters to replace the trust region selection heuristics, as well as the trainable covariance matrix instead of the fixed one. In many cases, the method not only improves the results comparing to the state-of-the-art trust region on-policy learning algorithms such as ACKTR and TRPO, but also with respect to their off-policy counterpart DDPG. ", "output": [ "We investigate the theoretical and practical evidence of on-policy reinforcement learning improvement by reusing the data from several consecutive policies." ] }, { "id": "task668-673f1d56fcdd43a7b0084d532cf70965", "input": "Granger causality is a widely-used criterion for analyzing interactions in large-scale networks. As most physical interactions are inherently nonlinear, we consider the problem of inferring the existence of pairwise Granger causality between nonlinearly interacting stochastic processes from their time series measurements. Our proposed approach relies on modeling the embedded nonlinearities in the measurements using a component-wise time series prediction model based on Statistical Recurrent Units (SRUs). We make a case that the network topology of Granger causal relations is directly inferrable from a structured sparse estimate of the internal parameters of the SRU networks trained to predict the processes’ time series measurements. We propose a variant of SRU, called economy-SRU, which, by design has considerably fewer trainable parameters, and therefore less prone to overfitting. The economy-SRU computes a low-dimensional sketch of its high-dimensional hidden state in the form of random projections to generate the feedback for its recurrent processing. Additionally, the internal weight parameters of the economy-SRU are strategically regularized in a group-wise manner to facilitate the proposed network in extracting meaningful predictive features that are highly time-localized to mimic real-world causal events. Extensive experiments are carried out to demonstrate that the proposed economy-SRU based time series prediction model outperforms the MLP, LSTM and attention-gated CNN-based time series models considered previously for inferring Granger causality.", "output": [ "A new recurrent neural network architecture for detecting pairwise Granger causality between nonlinearly interacting time series. " ] }, { "id": "task668-d46faec9bae241239f8b80b29f1872a7", "input": "We frame Question Answering (QA) as a Reinforcement Learning task, an approach that we call Active Question Answering. \n\n We propose an agent that sits between the user and a black box QA system and learns to reformulate questions to elicit the best possible answers. The agent probes the system with, potentially many, natural language reformulations of an initial question and aggregates the returned evidence to yield the best answer. \n\n The reformulation system is trained end-to-end to maximize answer quality using policy gradient. We evaluate on SearchQA, a dataset of complex questions extracted from Jeopardy!. The agent outperforms a state-of-the-art base model, playing the role of the environment, and other benchmarks.\n\nWe also analyze the language that the agent has learned while interacting with the question answering system. We find that successful question reformulations look quite different from natural language paraphrases. The agent is able to discover non-trivial reformulation strategies that resemble classic information retrieval techniques such as term re-weighting (tf-idf) and stemming.", "output": [ "We propose an agent that sits between the user and a black box question-answering system and which learns to reformulate questions to elicit the best possible answers", "This paper proposes active question answering via a reinforcement learning approach that learns to rephrase questions in a way to provide the best possible answers.", "Clearly describes how the researchers designed and actively trained two models for question reformulation and answer selection during question answering episodes" ] }, { "id": "task668-4900c5e8f8d5417caed5e53ce1b9410b", "input": "Common-sense or background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, the requisite background knowledge is indirectly acquired from static corpora. We develop a new reading architecture for the dynamic integration of explicit background knowledge in NLU models. A new task-agnostic reading module provides refined word representations to a task-specific NLU architecture by processing background knowledge in the form of free-text statements, together with the task-specific inputs. Strong performance on the tasks of document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of our approach. Analysis shows that our models learn to exploit knowledge selectively and in a semantically appropriate way.", "output": [ "In this paper we present a task-agnostic reading architecture for the dynamic integration of explicit background knowledge in neural NLU models. " ] }, { "id": "task668-eee710670b614455b8d3044222f9be63", "input": "In representation learning (RL), how to make the learned representations easy to interpret and less overfitted to training data are two important but challenging issues. To address these problems, we study a new type of regularization approach that encourages the supports of weight vectors in RL models to have small overlap, by simultaneously promoting near-orthogonality among vectors and sparsity of each vector. We apply the proposed regularizer to two models: neural networks (NNs) and sparse coding (SC), and develop an efficient ADMM-based algorithm for regularized SC. Experiments on various datasets demonstrate that weight vectors learned under our regularizer are more interpretable and have better generalization performance.", "output": [ "We propose a new type of regularization approach that encourages non-overlapness in representation learning, for the sake of improving interpretability and reducing overfitting.", "The paper introduces a matrix regularizer to simultaneously induce both sparsity and approximate orthogonality.", "The paper studies a regularization method to promote sparsity and reduce the overlap among the supports of the weight vectors in the learned representations to enhance interpretability and avoid overfitting", "The paper proposed a new regularization approach that simultaneously encourages the weight vectors (W) to be sparse and orthogonal to each other." ] }, { "id": "task668-6ecef10c1da54c509b3a7cd75a8b323b", "input": "This paper presents a storage-efficient learning model titled Recursive Binary Neural Networks for embedded and mobile devices having a limited amount of on-chip data storage such as hundreds of kilo-Bytes. The main idea of the proposed model is to recursively recycle data storage of weights (parameters) during training. This enables a device with a given storage constraint to train and instantiate a neural network classifier with a larger number of weights on a chip, achieving better classification accuracy. Such efficient use of on-chip storage reduces off-chip storage accesses, improving energy-efficiency and speed of training. We verified the proposed training model with deep and convolutional neural network classifiers on the MNIST and voice activity detection benchmarks. For the deep neural network, our model achieves data storage requirement of as low as 2 bits/weight, whereas the conventional binary neural network learning models require data storage of 8 to 32 bits/weight. With the same amount of data storage, our model can train a bigger network having more weights, achieving 1% less test error than the conventional binary neural network learning model. To achieve the similar classification error, the conventional binary neural network model requires 4× more data storage for weights than our proposed model. For the convolution neural network classifier, the proposed model achieves 2.4% less test error for the same on-chip storage or 6× storage savings to achieve the similar accuracy.\n", "output": [ "We propose a learning model enabling DNN to learn with only 2 bit/weight, which is especially useful for on-device learning", "Proposes a method to discretisize a NN incrementally to improve memory and performance." ] }, { "id": "task668-b75b0758f8e94abd89b9d5b51aa55c8c", "input": "As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed on clusters to perform model fitting in parallel. Alistarh et al. (2017) describe two variants of data-parallel SGD that quantize and encode gradients to lessen communication costs. For the first variant, QSGD, they provide strong theoretical guarantees. For the second variant, which we call QSGDinf, they demonstrate impressive empirical gains for distributed training of large neural networks. Building on their work, we propose an alternative scheme for quantizing gradients and show that it yields stronger theoretical guarantees than exist for QSGD while matching the empirical performance of QSGDinf.", "output": [ "NUQSGD closes the gap between the theoretical guarantees of QSGD and the empirical performance of QSGDinf." ] }, { "id": "task668-d8472422974d4f269dedcedb4bc0cbb8", "input": "Recovering 3D geometry shape, albedo and lighting from a single image has wide applications in many areas, which is also a typical ill-posed problem. In order to eliminate the ambiguity, face prior knowledge like linear 3D morphable models (3DMM) learned from limited scan data are often adopted to the reconstruction process. However, methods based on linear parametric models cannot generalize well for facial images in the wild with various ages, ethnicity, expressions, poses, and lightings. Recent methods aim to learn a nonlinear parametric model using convolutional neural networks (CNN) to regress the face shape and texture directly. However, the models were only trained on a dataset that is generated from a linear 3DMM. Moreover, the identity and expression representations are entangled in these models, which hurdles many facial editing applications. In this paper, we train our model with adversarial loss in a semi-supervised manner on hybrid batches of unlabeled and labeled face images to exploit the value of large amounts of unlabeled face images from unconstrained photo collections. A novel center loss is introduced to make sure that different facial images from the same person have the same identity shape and albedo. Besides, our proposed model disentangles identity, expression, pose, and lighting representations, which improves the overall reconstruction performance and facilitates facial editing applications, e.g., expression transfer. Comprehensive experiments demonstrate that our model produces high-quality reconstruction compared to state-of-the-art methods and is robust to various expression, pose, and lighting conditions.\n", "output": [ "We train our face reconstruction model with adversarial loss in semi-supervised manner on hybrid batches of unlabeled and labeled face images to exploit the value of large amounts of unlabeled face images from unconstrained photo collections.", "This paper proposes a semi-supervised and adversarial training process to exact nonlinear disentangled representations from a face image with loss functions, achieving state-of-the-art performance in face reconstruction." ] }, { "id": "task668-edf2285de09e4f81af41b3f4c59748dc", "input": "Determining the appropriate batch size for mini-batch gradient descent is always time consuming as it often relies on grid search. This paper considers a resizable mini-batch gradient descent (RMGD) algorithm based on a multi-armed bandit that achieves performance equivalent to that of best fixed batch-size. At each epoch, the RMGD samples a batch size according to a certain probability distribution proportional to a batch being successful in reducing the loss function. Sampling from this probability provides a mechanism for exploring different batch size and exploiting batch sizes with history of success. After obtaining the validation loss at each epoch with the sampled batch size, the probability distribution is updated to incorporate the effectiveness of the sampled batch size. Experimental results show that the RMGD achieves performance better than the best performing single batch size. It is surprising that the RMGD achieves better performance than grid search. Furthermore, it attains this performance in a shorter amount of time than grid search.", "output": [ "An optimization algorithm that explores various batch sizes based on probability and automatically exploits successful batch size which minimizes validation loss." ] }, { "id": "task668-b7cffa1b8694468f8a1d5aa0a81f2405", "input": "We present a framework for automatically ordering image patches that enables in-depth analysis of dataset relationship to learnability of a classification task using convolutional neural network. An image patch is a group of pixels residing in a continuous area contained in the sample. Our preliminary experimental results show that an informed smart shuffling of patches at a sample level can expedite training by exposing important features at early stages of training. In addition, we conduct systematic experiments and provide evidence that CNN’s generalization capabilities do not correlate with human recognizable features present in training samples. We utilized the framework not only to show that spatial locality of features within samples do not correlate with generalization, but also to expedite convergence while achieving similar generalization performance. Using multiple network architectures and datasets, we show that ordering image regions using mutual information measure between adjacent patches, enables CNNs to converge in a third of the total steps required to train the same network without patch ordering.", "output": [ "Develop new techniques that rely on patch reordering to enable detailed analysis of data-set relationship to training and generalization performances." ] }, { "id": "task668-1c6bd0986ed74ed087483c83cdb44d9e", "input": "The application of multi-modal generative models by means of a Variational Auto Encoder (VAE) is an upcoming research topic for sensor fusion and bi-directional modality exchange.\n This contribution gives insights into the learned joint latent representation and shows that expressiveness and coherence are decisive properties for multi-modal datasets.\n Furthermore, we propose a multi-modal VAE derived from the full joint marginal log-likelihood that is able to learn the most meaningful representation for ambiguous observations.\n Since the properties of multi-modal sensor setups are essential for our approach but hardly available, we also propose a technique to generate correlated datasets from uni-modal ones.\n", "output": [ "Deriving a general formulation of a multi-modal VAE from the joint marginal log-likelihood.", "Proposes a multi-modal VAE with a variational bound derived from the chain rule.", "This paper proposes an objective, M^2VAE, for multi-modal VAEs, which is supposed to learn a more meaningful latent space representation." ] }, { "id": "task668-86bd269f84db4a2da45a1cfdb3851794", "input": "Natural language understanding research has recently shifted towards complex Machine Learning and Deep Learning algorithms. Such models often outperform their simpler counterparts significantly. However, their performance relies on the availability of large amounts of labeled data, which are rarely available. To tackle this problem, we propose a methodology for extending training datasets to arbitrarily big sizes and training complex, data-hungry models using weak supervision. We apply this methodology on biomedical relation extraction, a task where training datasets are excessively time-consuming and expensive to create, yet has a major impact on downstream applications such as drug discovery. We demonstrate in two small-scale controlled experiments that our method consistently enhances the performance of an LSTM network, with performance improvements comparable to hand-labeled training data. Finally, we discuss the optimal setting for applying weak supervision using this methodology.", "output": [ "We propose and apply a meta-learning methodology based on Weak Supervision, for combining Semi-Supervised and Ensemble Learning on the task of Biomedical Relationship Extraction.", "A semi-supervised method for relation classification, which trains multiple base learners using a small labeled dataset and applies some of them to annotate unlabeled examples for semi-supervised learning.", "This paper addresses the problem of generating training data for biological relation extraction, and uses predictions from data labeled by weak classifiers as additional training data for a meta learning algorithm.", "This paper proposes a combination of semi-supervised learning and ensemble learning for information extraction, with experiments conducted on a biomedical relation extraction task" ] }, { "id": "task668-92ef362dd0ea47f68d143e935ded3c96", "input": "When communicating, humans rely on internally-consistent language representations. That is, as speakers, we expect listeners to behave the same way we do when we listen. This work proposes several methods for encouraging such internal consistency in dialog agents in an emergent communication setting. We consider two hypotheses about the effect of internal-consistency constraints: 1) that they improve agents’ ability to refer to unseen referents, and 2) that they improve agents’ ability to generalize across communicative roles (e.g. performing as a speaker de- spite only being trained as a listener). While we do not find evidence in favor of the former, our results show significant support for the latter.", "output": [ "Internal-consistency constraints improve agents ability to develop emergent protocols that generalize across communicative roles." ] }, { "id": "task668-c6e2ba83933f4190b5db3a58169e2048", "input": "Learning policies for complex tasks that require multiple different skills is a major challenge in reinforcement learning (RL). It is also a requirement for its deployment in real-world scenarios. This paper proposes a novel framework for efficient multi-task reinforcement learning. Our framework trains agents to employ hierarchical policies that decide when to use a previously learned policy and when to learn a new skill. This enables agents to continually acquire new skills during different stages of training. Each learned task corresponds to a human language description. Because agents can only access previously learned skills through these descriptions, the agent can always provide a human-interpretable description of its choices. In order to help the agent learn the complex temporal dependencies necessary for the hierarchical policy, we provide it with a stochastic temporal grammar that modulates when to rely on previously learned skills and when to execute new skills. We validate our approach on Minecraft games designed to explicitly test the ability to reuse previously learned skills while simultaneously learning new skills.", "output": [ "A novel hierarchical policy network which can reuse previously learned skills alongside and as subcomponents of new skills by discovering the underlying relations between skills.", "This paper aims to learn hierarchical policies by using a recursive policy structure regulated by a stochastic temporal grammar", "This paper proposes an approach to learning hierarchical policies in a lifelong learning context by stacking policies and then using an explicit \"switch\" policy." ] }, { "id": "task668-63788ede9695476f8cbaa8f5bb881224", "input": "Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attributes of neural networks can be exposed from a sequence of queries. This has multiple implications. On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks -- we show that the revealed internal information helps generate more effective adversarial examples against the black box model. On the other hand, this technique can be used for better protection of private content from automatic recognition models using adversarial examples. Our paper suggests that it is actually hard to draw a line between white box and black box models.", "output": [ "Querying a black-box neural network reveals a lot of information about it; we propose novel \"metamodels\" for effectively extracting information from a black box." ] }, { "id": "task668-647ee581a99648e398fb147d03af9f9a", "input": "Person re-identification (re-ID) aims at identifying the same persons' images across different cameras. However, domain diversities between different datasets pose an evident challenge for adapting the re-ID model trained on one dataset to another one. State-of-the-art unsupervised domain adaptation methods for person re-ID transferred the learned knowledge from the source domain by optimizing with pseudo labels created by clustering algorithms on the target domain. Although they achieved state-of-the-art performances, the inevitable label noise caused by the clustering procedure was ignored. Such noisy pseudo labels substantially hinders the model's capability on further improving feature representations on the target domain. In order to mitigate the effects of noisy pseudo labels, we propose to softly refine the pseudo labels in the target domain by proposing an unsupervised framework, Mutual Mean-Teaching (MMT), to learn better features from the target domain via off-line refined hard pseudo labels and on-line refined soft pseudo labels in an alternative training manner. In addition, the common practice is to adopt both the classification loss and the triplet loss jointly for achieving optimal performances in person re-ID models. However, conventional triplet loss cannot work with softly refined labels. To solve this problem, a novel soft softmax-triplet loss is proposed to support learning with soft pseudo triplet labels for achieving the optimal domain adaptation performance. The proposed MMT framework achieves considerable improvements of 14.4%, 18.2%, 13.1% and 16.4% mAP on Market-to-Duke, Duke-to-Market, Market-to-MSMT and Duke-to-MSMT unsupervised domain adaptation tasks.", "output": [ "A framework that conducts online refinement of pseudo labels with a novel soft softmax-triplet loss for unsupervised domain adaptation on person re-identification." ] }, { "id": "task668-9fe1cba8d8664cbc92f11418a79a75ab", "input": "Despite the recent successes in robotic locomotion control, the design of robot relies heavily on human engineering. Automatic robot design has been a long studied subject, but the recent progress has been slowed due to the large combinatorial search space and the difficulty in evaluating the found candidates. To address the two challenges, we formulate automatic robot design as a graph search problem and perform evolution search in graph space. We propose Neural Graph Evolution (NGE), which performs selection on current candidates and evolves new ones iteratively. Different from previous approaches, NGE uses graph neural networks to parameterize the control policies, which reduces evaluation cost on new candidates with the help of skill transfer from previously evaluated designs. In addition, NGE applies Graph Mutation with Uncertainty (GM-UC) by incorporating model uncertainty, which reduces the search space by balancing exploration and exploitation. We show that NGE significantly outperforms previous methods by an order of magnitude. As shown in experiments, NGE is the first algorithm that can automatically discover kinematically preferred robotic graph structures, such as a fish with two symmetrical flat side-fins and a tail, or a cheetah with athletic front and back legs. Instead of using thousands of cores for weeks, NGE efficiently solves searching problem within a day on a single 64 CPU-core Amazon EC2\n machine.\n", "output": [ "Automatic robotic design search with graph neural networks", "Proposes an approach for automatic robot design based on Neural graph evolution. The experiments demonstrate that optimizing both controller and hardware is better than optimizing just the controller.", "The authors propose a scheme based on a graph representation of the robot structure, and a graph-neural-network as controllers to optimize robot structures, combined with their controllers. " ] }, { "id": "task668-2bd4c48d58ec4e938f575c19e293401a", "input": "We study the use of the Wave-U-Net architecture for speech enhancement, a model introduced by Stoller et al for the separation of music vocals and accompaniment. This end-to-end learning method for audio source separation operates directly in the time domain, permitting the integrated modelling of phase information and being able to take large temporal contexts into account. Our experiments show that the proposed method improves several metrics, namely PESQ, CSIG, CBAK, COVL and SSNR, over the state-of-the-art with respect to the speech enhancement task on the Voice Bank corpus (VCTK) dataset. We find that a reduced number of hidden layers is sufficient for speech enhancement in comparison to the original system designed for singing voice separation in music. We see this initial result as an encouraging signal to further explore speech enhancement in the time-domain, both as an end in itself and as a pre-processing step to speech recognition systems.", "output": [ "The Wave-U-Net architecture, recently introduced by Stoller et al for music source separation, is highly effective for speech enhancement, beating the state of the art." ] }, { "id": "task668-73c5dd954e3e4e05b74d7815fa68fcdc", "input": "Edge intelligence especially binary neural network (BNN) has attracted considerable attention of the artificial intelligence community recently. BNNs significantly reduce the computational cost, model size, and memory footprint. However, there is still a performance gap between the successful full-precision neural network with ReLU activation and BNNs. We argue that the accuracy drop of BNNs is due to their geometry. \n We analyze the behaviour of the full-precision neural network with ReLU activation and compare it with its binarized counterpart. This comparison suggests random bias initialization as a remedy to activation saturation in full-precision networks and leads us towards an improved BNN training. Our numerical experiments confirm our geometric intuition.", "output": [ "Improve saturating activations (sigmoid, tanh, htanh etc.) and Binarized Neural Network with Bias Initialization" ] }, { "id": "task668-c2853b34fe7348d892844fa43eb3eff2", "input": "Heuristic search research often deals with finding algorithms for offline planning which aim to minimize the number of expanded nodes or planning time. In online planning, algorithms for real-time search or deadline-aware search have been considered before. However, in this paper, we are interested in the problem of {\\em situated temporal planning} in which an agent's plan can depend on exogenous events in the external world, and thus it becomes important to take the passage of time into account during the planning process. \n Previous work on situated temporal planning has proposed simple pruning strategies, as well as complex schemes for a simplified version of the associated metareasoning problem. \n In this paper, we propose a simple metareasoning technique, called the crude greedy scheme, which can be applied in a situated temporal planner. Our empirical evaluation shows that the crude greedy scheme outperforms standard heuristic search based on cost-to-go estimates.", "output": [ "Metareasoning in a Situated Temporal Planner", "This paper addresses the problem of situated temporal planning, proposing a further simplification on greedy strategies previously proposed by Shperberg." ] }, { "id": "task668-dff22e01a89b4548a007f91d230e092b", "input": "Performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Many image classification tasks, such as those related to cellular imaging, exhibit invariance to rotation. In particular, to aid convolutional neural networks in learning rotation invariance, we consider a simple, efficient conic convolutional scheme that encodes rotational equivariance, along with a method for integrating the magnitude response of the 2D-discrete-Fourier transform (2D-DFT) to encode global rotational invariance. We call our new method the Conic Convolution and DFT Network (CFNet). We evaluated the efficacy of CFNet as compared to a standard CNN and group-equivariant CNN (G-CNN) for several different image classification tasks and demonstrated improved performance, including classification accuracy, computational efficiency, and its robustness to hyperparameter selection. Taken together, we believe CFNet represents a new scheme that has the potential to improve many imaging analysis applications.", "output": [ "We propose conic convolution and the 2D-DFT to encode rotation equivariance into an neural network.", "In the context of image classification, the paper proposes a convolutional neural network architecture with rotation-equivariant feature maps that are eventually made rotation-invariant by using the magnitude of the 2D discrete Fourier transform (DFT).", "Authors provide a rotation invariant neural network via combining conic convolution and 2D-DFT" ] }, { "id": "task668-13a2cef6fa664398a4cc146bb4844577", "input": "Memorization in over-parameterized neural networks can severely hurt generalization in the presence of mislabeled examples. However, mislabeled examples are to hard avoid in extremely large datasets. We address this problem using the implicit regularization effect of stochastic gradient descent with large learning rates, which we find to be able to separate clean and mislabeled examples with remarkable success using loss statistics. We leverage this to identify and on-the-fly discard mislabeled examples using a threshold on their losses. This leads to On-the-fly Data Denoising (ODD), a simple yet effective algorithm that is robust to mislabeled examples, while introducing almost zero computational overhead. Empirical results demonstrate the effectiveness of ODD on several datasets containing artificial and real-world mislabeled examples.", "output": [ "We introduce a fast and easy-to-implement algorithm that is robust to dataset noise.", "The paper aims to remove potential examples with label noise by discarding the ones with large losses in the training procedure." ] }, { "id": "task668-090cdc0e5d8240bd8bca411a56f11d39", "input": "One can substitute each neuron in any neural network with a kernel machine and obtain a counterpart powered by kernel machines. The new network inherits the expressive power and architecture of the original but works in a more intuitive way since each node enjoys the simple interpretation as a hyperplane (in a reproducing kernel Hilbert space). Further, using the kernel multilayer perceptron as an example, we prove that in classification, an optimal representation that minimizes the risk of the network can be characterized for each hidden layer. This result removes the need of backpropagation in learning the model and can be generalized to any feedforward kernel network. Moreover, unlike backpropagation, which turns models into black boxes, the optimal hidden representation enjoys an intuitive geometric interpretation, making the dynamics of learning in a deep kernel network simple to understand. Empirical results are provided to validate our theory.", "output": [ "We combine kernel method with connectionist models and show that the resulting deep architectures can be trained layer-wise and have more transparent learning dynamics. " ] }, { "id": "task668-49b73c03ea7942f8b4c4d001b01f2a4c", "input": "Multi-relational graph embedding which aims at achieving effective representations with reduced low-dimensional parameters, has been widely used in knowledge base completion. Although knowledge base data usually contains tree-like or cyclic structure, none of existing approaches can embed these data into a compatible space that in line with the structure. To overcome this problem, a novel framework, called Riemannian TransE, is proposed in this paper to embed the entities in a Riemannian manifold. Riemannian TransE models each relation as a move to a point and defines specific novel distance dissimilarity for each relation, so that all the relations are naturally embedded in correspondence to the structure of data. Experiments on several knowledge base completion tasks have shown that, based on an appropriate choice of manifold, Riemannian TransE achieves good performance even with a significantly reduced parameters.", "output": [ "Multi-relational graph embedding with Riemannian manifolds and TransE-like loss function. " ] }, { "id": "task668-036e0bcb585740e39f5cae9b983d88cf", "input": "In this paper, we study the implicit regularization of the gradient descent algorithm in homogeneous neural networks, including fully-connected and convolutional neural networks with ReLU or LeakyReLU activations. In particular, we study the gradient descent or gradient flow (i.e., gradient descent with infinitesimal step size) optimizing the logistic loss or cross-entropy loss of any homogeneous model (possibly non-smooth), and show that if the training loss decreases below a certain threshold, then we can define a smoothed version of the normalized margin which increases over time. We also formulate a natural constrained optimization problem related to margin maximization, and prove that both the normalized margin and its smoothed version converge to the objective value at a KKT point of the optimization problem. Our results generalize the previous results for logistic regression with one-layer or multi-layer linear networks, and provide more quantitative convergence results with weaker assumptions than previous results for homogeneous smooth neural networks. We conduct several experiments to justify our theoretical finding on MNIST and CIFAR-10 datasets. Finally, as margin is closely related to robustness, we discuss potential benefits of training longer for improving the robustness of the model.", "output": [ "We study the implicit bias of gradient descent and prove under a minimal set of assumptions that the parameter direction of homogeneous models converges to KKT points of a natural margin maximization problem." ] }, { "id": "task668-855034882e0f45aabe27362240baa71b", "input": "Deep learning natural language processing models often use vector word embeddings, such as word2vec or GloVe, to represent words. A discrete sequence of words can be much more easily integrated with downstream neural layers if it is represented as a sequence of continuous vectors. Also, semantic relationships between words, learned from a text corpus, can be encoded in the relative configurations of the embedding vectors. However, storing and accessing embedding vectors for all words in a dictionary requires large amount of space, and may stain systems with limited GPU memory. Here, we used approaches inspired by quantum computing to propose two related methods, word2ket and word2ketXS, for storing word embedding matrix during training and inference in a highly efficient way. Our approach achieves a hundred-fold or more reduction in the space required to store the embeddings with almost no relative drop in accuracy in practical natural language processing tasks.", "output": [ "We use ideas from quantum computing to proposed word embeddings that utilize much fewer trainable parameters." ] }, { "id": "task668-f927069ac58f44779030bf0bffa011af", "input": "Wide adoption of complex RNN based models is hindered by their inference performance, cost and memory requirements. To address this issue, we develop AntMan, combining structured sparsity with low-rank decomposition synergistically, to reduce model computation, size and execution time of RNNs while attaining desired accuracy. AntMan extends knowledge distillation based training to learn the compressed models efficiently. Our evaluation shows that AntMan offers up to 100x computation reduction with less than 1pt accuracy drop for language and machine reading comprehension models. Our evaluation also shows that for a given accuracy target, AntMan produces 5x smaller models than the state-of-art. Lastly, we show that AntMan offers super-linear speed gains compared to theoretical speedup, demonstrating its practical value on commodity hardware.", "output": [ "Reducing computation and memory complexity of RNN models by up to 100x using sparse low-rank compression modules, trained via knowledge distillation." ] }, { "id": "task668-1453656104f84d15bbb8d229da33042e", "input": "The key attribute that drives the unprecedented success of modern Recurrent Neural Networks (RNNs) on learning tasks which involve sequential data, is their ever-improving ability to model intricate long-term temporal dependencies. However, a well established measure of RNNs' long-term memory capacity is lacking, and thus formal understanding of their ability to correlate data throughout time is limited. Though depth efficiency in convolutional networks is well established by now, it does not suffice in order to account for the success of deep RNNs on inputs of varying lengths, and the need to address their 'time-series expressive power' arises. In this paper, we analyze the effect of depth on the ability of recurrent networks to express correlations ranging over long time-scales. To meet the above need, we introduce a measure of the information flow across time that can be supported by the network, referred to as the Start-End separation rank. Essentially, this measure reflects the distance of the function realized by the recurrent network from a function that models no interaction whatsoever between the beginning and end of the input sequence. We prove that deep recurrent networks support Start-End separation ranks which are exponentially higher than those supported by their shallow counterparts. Moreover, we show that the ability of deep recurrent networks to correlate different parts of the input sequence increases exponentially as the input sequence extends, while that of vanilla shallow recurrent networks does not adapt to the sequence length at all. Thus, we establish that depth brings forth an overwhelming advantage in the ability of recurrent networks to model long-term dependencies, and provide an exemplar of quantifying this key attribute which may be readily extended to other RNN architectures of interest, e.g. variants of LSTM networks. We obtain our results by considering a class of recurrent networks referred to as Recurrent Arithmetic Circuits (RACs), which merge the hidden state with the input via the Multiplicative Integration operation.", "output": [ "We propose a measure of long-term memory and prove that deep recurrent networks are much better fit to model long-term temporal dependencies than shallow ones." ] }, { "id": "task668-3e5dfc7b347d4579b145280ebc29d93a", "input": "Generating and scheduling activities is particularly challenging\n when considering both consumptive resources and\n complex resource interactions such as time-dependent resource\n usage.We present three methods of determining valid\n temporal placement intervals for an activity in a temporally\n grounded plan in the presence of such constraints. We introduce\n the Max Duration and Probe algorithms which are\n sound, but incomplete, and the Linear algorithm which is\n sound and complete for linear rate resource consumption.\n We apply these techniques to the problem of scheduling\n awakes for a planetary rover where the awake durations\n are affected by existing activities. We demonstrate how the\n Probe algorithm performs competitively with the Linear algorithm\n given an advantageous problem space and well-defined\n heuristics. We show that the Probe and Linear algorithms\n outperform the Max Duration algorithm empirically.\n We then empirically present the runtime differences between\n the three algorithms. The Probe algorithm is currently base-lined\n for use in the onboard scheduler for NASA’s next planetary\n rover, the Mars 2020 rover.", "output": [ "This paper describes and analyzes three methods to schedule non-fixed duration activities in the presence of consumptive resources.", "The paper presents three approaches for on-board scheduling of activities in a planetary rover under reservoir resource constraints." ] }, { "id": "task668-0cf45b1ecd91443eb2cc7e66b10cedad", "input": "We introduce the deep abstaining classifier -- a deep neural network trained with a novel loss function that provides an abstention option during training. This allows the DNN to abstain on confusing or difficult-to-learn examples while improving performance on the non-abstained samples. We show that such deep abstaining classifiers can: (i) learn representations for structured noise -- where noisy training labels or confusing examples are correlated with underlying features -- and then learn to abstain based on such features; (ii) enable robust learning in the presence of arbitrary or unstructured noise by identifying noisy samples; and (iii) be used as an effective out-of-category detector that learns to reliably abstain when presented with samples from unknown classes. We provide analytical results on loss function behavior that enable automatic tuning of accuracy and coverage, and demonstrate the utility of the deep abstaining classifier using multiple image benchmarks, Results indicate significant improvement in learning in the presence of label noise.", "output": [ "A deep abstaining neural network trained with a novel loss function that learns representations for when to abstain enabling robust learning in the presence of different types of noise.", "A new loss function for training a deep neural network which can abstain, with performance looked at from angles in existence of structured noise, in existence of unstructured noise, and open world detection.", "This manuscript introduces deep abstaining classifiers which modifies the multiclass cross-entropy loss with an abstention loss, which is then applied to perturbed image classification tasks" ] }, { "id": "task668-85368bc60797419caeb3e138e39a8c5a", "input": "End-to-end automatic speech recognition (ASR) commonly transcribes audio signals into sequences of characters while its performance is evaluated by measuring the word-error rate (WER). This suggests that predicting sequences of words directly may be helpful instead. However, training with word-level supervision can be more difficult due to the sparsity of examples per label class. In this paper we analyze an end-to-end ASR model that combines a word-and-character representation in a multi-task learning (MTL) framework. We show that it improves on the WER and study how the word-level model can benefit from character-level supervision by analyzing the learned inductive preference bias of each model component empirically. We find that by adding character-level supervision, the MTL model interpolates between recognizing more frequent words (preferred by the word-level model) and shorter words (preferred by the character-level model).", "output": [ "Multi-task learning improves word-and-character-level speech recognition by interpolating the preference biases of its components: frequency- and word length-preference." ] }, { "id": "task668-6852ea92c4a54a4b9dce2278ca29d144", "input": "When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a binding agreement on a joint plan, and execute it. However, when agents are self-interested, the gains from team formation must be allocated appropriately to incentivize agreement. Various approaches for multi-agent negotiation have been proposed, but typically only work for particular negotiation protocols. More general methods usually require human input or domain-specific data, and so do not scale. To address this, we propose a framework for training agents to negotiate and form teams using deep reinforcement learning. Importantly, our method makes no assumptions about the specific negotiation protocol, and is instead completely experience driven. We evaluate our approach on both non-spatial and spatially extended team-formation negotiation environments, demonstrating that our agents beat hand-crafted bots and reach negotiation outcomes consistent with fair solutions predicted by cooperative game theory. Additionally, we investigate how the physical location of agents influences negotiation outcomes.", "output": [ "Reinforcement learning can be used to train agents to negotiate team formation across many negotiation protocols", "This paper studies deep multi-agent RL in settings where all of the agents must cooperate to accomplish a task (e.g., search and rescue, multi-player video games), and uses simple cooperative weighted voting games to study the efficacy of deep RL and to compare solutions found by deep RL to a fair solution.", "A reinforcement learning approach for negotiating coalitions in cooperative game theory settings that can be used in cases where unlimited training simulations are available." ] }, { "id": "task668-12eee66c177d4436bea26359a4cbbc04", "input": "We present a framework for interpretable continual learning (ICL). We show that explanations of previously performed tasks can be used to improve performance on future tasks. ICL generates a good explanation of a finished task, then uses this to focus attention on what is important when facing a new task. The ICL idea is general and may be applied to many continual learning approaches. Here we focus on the variational continual learning framework to take advantage of its flexibility and efficacy in overcoming catastrophic forgetting. We use saliency maps to provide explanations of performed tasks and propose a new metric to assess their quality. Experiments show that ICL achieves state-of-the-art results in terms of overall continual learning performance as measured by average classification accuracy, and also in terms of its explanations, which are assessed qualitatively and quantitatively using the proposed metric.", "output": [ "The paper develops an interpretable continual learning framework where explanations of the finished tasks are used to enhance the attention of the learner during the future tasks, and where an explanation metric is proposed too. ", "The authors propose a framework for continual learning based on explanations for performed classifications of previously learned tasks", "This paper proposes an extension to the continual learning framework using existing variational continual learning as the base method with weight of evidence." ] }, { "id": "task668-d9fb71b15c504698ac775a00b6ab4ac2", "input": "We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels. We accomplish this by developing sparse momentum, an algorithm which uses exponentially smoothed gradients (momentum) to identify layers and weights which reduce the error efficiently. Sparse momentum redistributes pruned weights across layers according to the mean momentum magnitude of each layer. Within a layer, sparse momentum grows weights according to the momentum magnitude of zero-valued weights. We demonstrate state-of-the-art sparse performance on MNIST, CIFAR-10, and ImageNet, decreasing the mean error by a relative 8%, 15%, and 6% compared to other sparse algorithms. Furthermore, we show that sparse momentum reliably reproduces dense performance levels while providing up to 5.61x faster training. In our analysis, ablations show that the benefits of momentum redistribution and growth increase with the depth and size of the network.", "output": [ "Redistributing and growing weights according to the momentum magnitude enables the training of sparse networks from random initializations that can reach dense performance levels with 5% to 50% weights while accelerating training by up to 5.6x." ] }, { "id": "task668-7e33b4ba6e644fc8b473378e18b6f71e", "input": "Existing public face image datasets are strongly biased toward Caucasian faces, and other races (e.g., Latino) are significantly underrepresented. The models trained from such datasets suffer from inconsistent classification accuracy, which limits the applicability of face analytic systems to non-White race groups. To mitigate the race bias problem in these datasets, we constructed a novel face image dataset containing 108,501 images which is balanced on race. We define 7 race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino. Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups. Evaluations were performed on existing face attribute datasets as well as novel image datasets to measure the generalization performance. We find that the model trained from our dataset is substantially more accurate on novel datasets and the accuracy is consistent across race and gender groups. We also compare several commercial computer vision APIs and report their balanced accuracy across gender, race, and age groups.", "output": [ "A new face image dataset for balanced race, gender, and age which can be used for bias measurement and mitigation" ] }, { "id": "task668-cb6f46999d2e4aa8ac22dab8148924d3", "input": "Anomaly detection discovers regular patterns in unlabeled data and identifies the non-conforming data points, which in some cases are the result of malicious attacks by adversaries. Learners such as One-Class Support Vector Machines (OCSVMs) have been successfully in anomaly detection, yet their performance may degrade significantly in the presence of sophisticated adversaries, who target the algorithm itself by compromising the integrity of the training data. With the rise in the use of machine learning in mission critical day-to-day activities where errors may have significant consequences, it is imperative that machine learning systems are made secure. To address this, we propose a defense mechanism that is based on a contraction of the data, and we test its effectiveness using OCSVMs. The proposed approach introduces a layer of uncertainty on top of the OCSVM learner, making it infeasible for the adversary to guess the specific configuration of the learner. We theoretically analyze the effects of adversarial perturbations on the separating margin of OCSVMs and provide empirical evidence on several benchmark datasets, which show that by carefully contracting the data in low dimensional spaces, we can successfully identify adversarial samples that would not have been identifiable in the original dimensional space. The numerical results show that the proposed method improves OCSVMs performance significantly (2-7%)", "output": [ "A novel method to increase the resistance of OCSVMs against targeted, integrity attacks by selective nonlinear transformations of data to lower dimensions.", "The authors propose a defense against attacks on the security of one-class SVM based anomaly detectors", "This paper explores how random projections can be used to make OCSVM robust to adversarially perturbed training data." ] }, { "id": "task668-424406eef6c148b09ee4a379cc58ee00", "input": "The goal of imitation learning (IL) is to enable a learner to imitate an expert’s behavior given the expert’s demonstrations. Recently, generative adversarial imitation learning (GAIL) has successfully achieved it even on complex continuous control tasks. However, GAIL requires a huge number of interactions with environment during training. We believe that IL algorithm could be more applicable to the real-world environments if the number of interactions could be reduced. To this end, we propose a model free, off-policy IL algorithm for continuous control. The keys of our algorithm are two folds: 1) adopting deterministic policy that allows us to derive a novel type of policy gradient which we call deterministic policy imitation gradient (DPIG), 2) introducing a function which we call state screening function (SSF) to avoid noisy policy updates with states that are not typical of those appeared on the expert’s demonstrations. Experimental results show that our algorithm can achieve the goal of IL with at least tens of times less interactions than GAIL on a variety of continuous control tasks.", "output": [ "We propose a model free imitation learning algorithm that is able to reduce number of interactions with environment in comparison with state-of-the-art imitation learning algorithm namely GAIL.", "Proposes to extend the determinist policy gradient algorithm to learn from demonstrations, while combined with a type of density estimation of the expert.", "This paper considers the problem of model-free imitation learning and proposes an extension of the generative adversarial imitation learning algorithm by replacing the stochastic policy of the learner with a deterministic one.", "The paper combines IRL, adversarial training, and ideas from deterministic policy gradients with the goal of decreasng sample complexity" ] }, { "id": "task668-66fccc9fa95c4b91a56c39713c0ed4d8", "input": "We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary.\n Our study shows that maximizing margins can be achieved by minimizing the adversarial loss on the decision boundary at the \"shortest successful perturbation\", demonstrating a close connection between adversarial losses and the margins. We propose Max-Margin Adversarial (MMA) training to directly maximize the margins to achieve adversarial robustness. \n Instead of adversarial training with a fixed $\\epsilon$, MMA offers an improvement by enabling adaptive selection of the \"correct\" $\\epsilon$ as the margin individually for each datapoint. In addition, we rigorously analyze adversarial training with the perspective of margin maximization, and provide an alternative interpretation for adversarial training, maximizing either a lower or an upper bound of the margins. Our experiments empirically confirm our theory and demonstrate MMA training's efficacy on the MNIST and CIFAR10 datasets w.r.t. $\\ell_\\infty$ and $\\ell_2$ robustness.", "output": [ "We propose MMA training to directly maximize input space margin in order to improve adversarial robustness primarily by removing the requirement of specifying a fixed distortion bound.", "An adaptive margin-based adversarial training approach to train robust DNNs, by maximizing the shortest margin of inputs to the decision boundary, that makes adversarial training with large perturbation possible.", "A method for robust learning against adversarial attacks where the input space margin is directly maximized and a softmax variant of the max-margin is introduced." ] }, { "id": "task668-d6f2f242f1a54de4b25e21be4698b9f4", "input": "Graph Neural Networks (GNNs) are a powerful representational tool for solving problems on graph-structured inputs. In almost all cases so far, however, they have been applied to directly recovering a final solution from raw inputs, without explicit guidance on how to structure their problem-solving. Here, instead, we focus on learning in the space of algorithms: we train several state-of-the-art GNN architectures to imitate individual steps of classical graph algorithms, parallel (breadth-first search, Bellman-Ford) as well as sequential (Prim's algorithm). As graph algorithms usually rely on making discrete decisions within neighbourhoods, we hypothesise that maximisation-based message passing neural networks are best-suited for such objectives, and validate this claim empirically. We also demonstrate how learning in the space of algorithms can yield new opportunities for positive transfer between tasks---showing how learning a shortest-path algorithm can be substantially improved when simultaneously learning a reachability algorithm.", "output": [ "We supervise graph neural networks to imitate intermediate and step-wise outputs of classical graph algorithms, recovering highly favourable insights.", "Suggests training neural networks to imitate graph algorithms by learning primitives and subroutines rather than the final output." ] }, { "id": "task668-9a996e8ba7ac49d6af8fb933dd902190", "input": "Augmented Reality (AR) can assist with physical tasks such as object assembly through the use of “situated instructions”. These instructions can be in the form of videos, pictures, text or guiding animations, where the most helpful media among these is highly dependent on both the user and the nature of the task. Our work supports the authoring of AR tutorials for assembly tasks with little overhead beyond simply performing the task itself. The presented system, AuthAR reduces the time and effort required to build interactive AR tutorials by automatically generating key components of the AR tutorial while the author is assembling the physical pieces. Further, the system guides authors through the process of adding videos, pictures, text and animations to the tutorial. This concurrent assembly and tutorial generation approach allows for authoring of portable tutorials that fit the preferences of different end users.", "output": [ "We present a mixed media assembly tutorial authoring system that streamlines creation of videos, images, text and dynamic instructions in situ." ] }, { "id": "task668-d002439458974d8e82d8a3cfd06b3147", "input": "A key problem in neuroscience, and life sciences more generally, is that data is generated by a hierarchy of dynamical systems. One example of this is in \\textit{in-vivo} calcium imaging data, where data is generated by a lower-order dynamical system governing calcium flux in neurons, which itself is driven by a higher-order dynamical system of neural computation. Ideally, life scientists would be able to infer the dynamics of both the lower-order systems and the higher-order systems, but this is difficult in high-dimensional regimes. A recent approach using sequential variational auto-encoders demonstrated it was possible to learn the latent dynamics of a single dynamical system for computations during reaching behaviour in the brain, using spiking data modelled as a Poisson process. Here we extend this approach using a ladder method to infer a hierarchy of dynamical systems, allowing us to capture calcium dynamics as well as neural computation. In this approach, spiking events drive lower-order calcium dynamics, and are themselves controlled by a higher-order latent dynamical system. We generate synthetic data by generating firing rates, sampling spike trains, and converting spike trains to fluorescence transients, from two dynamical systems that have been used as key benchmarks in recent literature: a Lorenz attractor, and a chaotic recurrent neural network. We show that our model is better able to reconstruct Lorenz dynamics from fluorescence data than competing methods. However, though our model can reconstruct underlying spike rates and calcium transients from the chaotic neural network well, it does not perform as well at reconstructing firing rates as basic techniques for inferring spikes from calcium data. These results demonstrate that VLAEs are a promising approach for modelling hierarchical dynamical systems data in the life sciences, but that inferring the dynamics of lower-order systems can potentially be better achieved with simpler methods.", "output": [ "We extend a successful recurrent variational autoencoder for dynamic systems to model an instance of dynamic systems hierarchy in neuroscience using the ladder method." ] }, { "id": "task668-d1d7447c4eef4db18df85ac2ed17ac76", "input": "Recent deep generative models can provide photo-realistic images as well as visual or textual content embeddings useful to address various tasks of computer vision and natural language processing. Their usefulness is nevertheless often limited by the lack of control over the generative process or the poor understanding of the learned representation. To overcome these major issues, very recent works have shown the interest of studying the semantics of the latent space of generative models. In this paper, we propose to advance on the interpretability of the latent space of generative models by introducing a new method to find meaningful directions in the latent space of any generative model along which we can move to control precisely specific properties of the generated image like position or scale of the object in the image. Our method is weakly supervised and particularly well suited for the search of directions encoding simple transformations of the generated image, such as translation, zoom or color variations. We demonstrate the effectiveness of our method qualitatively and quantitatively, both for GANs and variational auto-encoders.", "output": [ "A model to control the generation of images with GAN and beta-VAE with regard to scale and position of the objects" ] }, { "id": "task668-28aa4a50331248d18803711fa07343e5", "input": "Discriminative question answering models can overfit to superficial biases in datasets, because their loss function saturates when any clue makes the answer likely. We introduce generative models of the joint distribution of questions and answers, which are trained to explain the whole question, not just to answer it. Our question answering (QA) model is implemented by learning a prior over answers, and a conditional language model to generate the question given the answer—allowing scalable and interpretable many-hop reasoning as the question is generated word-by-word. Our model achieves competitive performance with specialised discriminative models on the SQUAD and CLEVR benchmarks, indicating that it is a more general architecture for language understanding and reasoning than previous work. The model greatly improves generalisation both from biased training data and to adversarial testing data, achieving a new state-of-the-art on ADVERSARIAL SQUAD. We will release our code.", "output": [ "Question answering models that model the joint distribution of questions and answers can learn more than discriminative models", "This paper proposes a generative approach to textual and visual QA, where a joint distribution over the question and answer space given the context is learned, which captures more complex relationships.", "This paper introduces a generative model for question answering and proposes to model p(q,a|c), factorized as p(a|c) * p(q|a,c). ", "The authors proposes a generative QA model, which optimizes jointly the distribution of questions and answering given a document/context. " ] }, { "id": "task668-ae94d59955c24c09aed751427e322e17", "input": "The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains. Devising a definitive defense against such attacks is proven to be challenging, and the methods relying on detecting adversarial samples are only valid when the attacker is oblivious to the detection mechanism. In this paper, we consider the adversarial detection problem under the robust optimization framework. We partition the input space into subspaces and train adversarial robust subspace detectors using asymmetrical adversarial training (AAT). The integration of the classifier and detectors presents a detection mechanism that provides a performance guarantee to the adversary it considered. We demonstrate that AAT promotes the learning of class-conditional distributions, which further gives rise to generative detection/classification approaches that are both robust and more interpretable. We provide comprehensive evaluations of the above methods, and demonstrate their competitive performances and compelling properties on adversarial detection and robust classification problems.", "output": [ "A new generative modeling technique based on asymmetrical adversarial training, and its applications to adversarial example detection and robust classification" ] }, { "id": "task668-c6487dd0a3b54c4798f6af2d06d77734", "input": "While it has not yet been proven, empirical evidence suggests that model generalization is related to local properties of the optima which can be described via the Hessian. We connect model generalization with the local property of a solution under the PAC-Bayes paradigm. In particular, we prove that model generalization ability is related to the Hessian, the higher-order \"smoothness\" terms characterized by the Lipschitz constant of the Hessian, and the scales of the parameters. Guided by the proof, we propose a metric to score the generalization capability of the model, as well as an algorithm that optimizes the perturbed model accordingly.", "output": [ "a theory connecting Hessian of the solution and the generalization power of the model" ] }, { "id": "task668-883b1eb7fd484f79a66c74c5aa896085", "input": "Delusional bias is a fundamental source of error in approximate Q-learning. To date, the only techniques that explicitly address delusion require comprehensive search using tabular value estimates. In this paper, we develop efficient methods to mitigate delusional bias by training Q-approximators with labels that are \"consistent\" with the underlying greedy policy class. We introduce a simple penalization scheme that encourages Q-labels used across training batches to remain (jointly) consistent with the expressible policy class. We also propose a search framework that allows multiple Q-approximators to be generated and tracked, thus mitigating the effect of premature (implicit) policy commitments. Experimental results demonstrate that these methods can improve the performance of Q-learning in a variety of Atari games, sometimes dramatically.", "output": [ "We developed a search framework and consistency penalty to mitigate delusional bias." ] }, { "id": "task668-8c6f256b401f49b0878fec48b5b23cdb", "input": "Fine-tuning from pre-trained ImageNet models has become the de-facto standard for various computer vision tasks. Current practices for fine-tuning typically involve selecting an ad-hoc choice of hyper-parameters and keeping them fixed to values normally used for training from scratch. This paper re-examines several common practices of setting hyper-parameters for fine-tuning. Our findings are based on extensive empirical evaluation for fine-tuning on various transfer learning benchmarks. (1) While prior works have thoroughly investigated learning rate and batch size, momentum for fine-tuning is a relatively unexplored parameter. We find that picking the right value for momentum is critical for fine-tuning performance and connect it with previous theoretical findings. (2) Optimal hyper-parameters for fine-tuning in particular the effective learning rate are not only dataset dependent but also sensitive to the similarity between the source domain and target domain. This is in contrast to hyper-parameters for training from scratch. (3) Reference-based regularization that keeps models close to the initial model does not necessarily apply for \"dissimilar\" datasets. Our findings challenge common practices of fine- tuning and encourages deep learning practitioners to rethink the hyper-parameters for fine-tuning.", "output": [ "This paper re-examines several common practices of setting hyper-parameters for fine-tuning." ] }, { "id": "task668-4eb58dd03d7249fdbb7c66c19676d6be", "input": "An important property of image classification systems in the real world is that they both accurately classify objects from target classes (``knowns'') and safely reject unknown objects (``unknowns'') that belong to classes not present in the training data. Unfortunately, although the strong generalization ability of existing CNNs ensures their accuracy when classifying known objects, it also causes them to often assign an unknown to a target class with high confidence. As a result, simply using low-confidence detections as a way to detect unknowns does not work well. In this work, we propose an Unknown-aware Deep Neural Network (UDN for short) to solve this challenging problem. The key idea of UDN is to enhance existing CNNs to support a product operation that models the product relationship among the features produced by convolutional layers. This way, missing a single key feature of a target class will greatly reduce the probability of assigning an object to this class. UDN uses a learned ensemble of these product operations, which allows it to balance the contradictory requirements of accurately classifying known objects and correctly rejecting unknowns. To further improve the performance of UDN at detecting unknowns, we propose an information-theoretic regularization strategy that incorporates the objective of rejecting unknowns into the learning process of UDN. We experiment on benchmark image datasets including MNIST, CIFAR-10, CIFAR-100, and SVHN, adding unknowns by injecting one dataset into another. Our results demonstrate that UDN significantly outperforms state-of-the-art methods at rejecting unknowns by 25 percentage points improvement in accuracy, while still preserving the classification accuracy.", "output": [ "A CNN architecture that can effective rejects the unknowns in test objects" ] }, { "id": "task668-5d7e1650b2a2416cb460d80bd6322070", "input": "We are reporting the SHINRA project, a project for structuring Wikipedia with collaborative construction scheme. The goal of the project is to create a huge and well-structured knowledge base to be used in NLP applications, such as QA, Dialogue systems and explainable NLP systems. It is created based on a scheme of ”Resource by Collaborative Contribution (RbCC)”. We conducted a shared task of structuring Wikipedia, and at the same, submitted results are used to construct a knowledge base.\n There are machine readable knowledge bases such as CYC, DBpedia, YAGO, Freebase Wikidata and so on, but each of them has problems to be solved. CYC has a coverage problem, and others have a coherence problem due to the fact that these are based on Wikipedia and/or created by many but inherently incoherent crowd workers. In order to solve the later problem, we started a project for structuring Wikipedia using automatic knowledge base construction shared-task.\n The automatic knowledge base construction shared-tasks have been popular and well studied for decades. However, these tasks are designed only to compare the performances of different systems, and to find which system ranks the best on limited test data. The results of the participated systems are not shared and the systems may be abandoned once the task is over.\n We believe this situation can be improved by the following changes:\n 1. designing the shared-task to construct knowledge base rather than evaluating only limited test data\n 2. making the outputs of all the systems open to public so that we can run ensemble learning to create the better results than the best systems\n 3. repeating the task so that we can run the task with the larger and better training data from the output of the previous task (bootstrapping and active learning)\n We conducted “SHINRA2018” with the above mentioned scheme and in this paper\n we report the results and the future directions of the project. The task is to extract the values of the pre-defined attributes from Wikipedia pages. We have categorized most of the entities in Japanese Wikipedia (namely 730 thousand entities) into the 200 ENE categories. Based on this data, the shared-task is to extract the values of the attributes from Wikipedia pages. We gave out the 600 training data and the participants are required to submit the attribute-values for all remaining entities of the same category type. Then 100 data out of them for each category are used to evaluate the system output in the shared-task.\n We conducted a preliminary ensemble learning on the outputs and found 15 F1 score improvement on a category and the average of 8 F1 score improvements on all 5 categories we tested over a strong baseline. Based on this promising results, we decided to conduct three tasks in 2019; multi-lingual categorization task (ML), extraction for the same 5 categories in Japanese with a larger training data (JP-5) and extraction for 34 new categories in Japanese (JP-34).\n", "output": [ "We introduce a \"Resource by Collaborative Construction\" scheme to create KB, structured Wikipedia " ] }, { "id": "task668-dbe65828bb3147c18660cb5296f2a49b", "input": "Stochastic Gradient Descent (SGD) with Nesterov's momentum is a widely used optimizer in deep learning, which is observed to have excellent generalization performance. However, due to the large stochasticity, SGD with Nesterov's momentum is not robust, i.e., its performance may deviate significantly from the expectation. In this work, we propose Amortized Nesterov's Momentum, a special variant of Nesterov's momentum which has more robust iterates, faster convergence in the early stage and higher efficiency. Our experimental results show that this new momentum achieves similar (sometimes better) generalization performance with little-to-no tuning. In the convex case, we provide optimal convergence rates for our new methods and discuss how the theorems explain the empirical results.", "output": [ "Amortizing Nesterov's momentum for more robust, lightweight and fast deep learning training." ] }, { "id": "task668-9f2442c572c64d92a4aaf31eb750e2af", "input": "Currently the only techniques for sharing governance of a deep learning model are homomorphic encryption and secure multiparty computation. Unfortunately, neither of these techniques is applicable to the training of large neural networks due to their large computational and communication overheads. As a scalable technique for shared model governance, we propose splitting deep learning model between multiple parties. This paper empirically investigates the security guarantee of this technique, which is introduced as the problem of model completion: Given the entire training data set or an environment simulator, and a subset of the parameters of a trained deep learning model, how much training is required to recover the model’s original performance? We define a metric for evaluating the hardness of the model completion problem and study it empirically in both supervised learning on ImageNet and reinforcement learning on Atari and DeepMind Lab. Our experiments show that (1) the model completion problem is harder in reinforcement learning than in supervised learning because of the unavailability of the trained agent’s trajectories, and (2) its hardness depends not primarily on the number of parameters of the missing part, but more so on their type and location. Our results suggest that model splitting might be a feasible technique for shared model governance in some settings where training is very expensive.", "output": [ "We study empirically how hard it is to recover missing parts of trained models" ] }, { "id": "task668-024e4c1a00a848c5b5610379fc096049", "input": "We study the problem of learning similarity functions over very large corpora using neural network embedding models. These models are typically trained using SGD with random sampling of unobserved pairs, with a sample size that grows quadratically with the corpus size, making it expensive to scale.\n We propose new efficient methods to train these models without having to sample unobserved pairs. Inspired by matrix factorization, our approach relies on adding a global quadratic penalty and expressing this term as the inner-product of two generalized Gramians. We show that the gradient of this term can be efficiently computed by maintaining estimates of the Gramians, and develop variance reduction schemes to improve the quality of the estimates. We conduct large-scale experiments that show a significant improvement both in training time and generalization performance compared to sampling methods.", "output": [ "We develop efficient methods to train neural embedding models with a dot-product structure, by reformulating the objective function in terms of generalized Gram matrices, and maintaining estimates of those matrices." ] }, { "id": "task668-5b5680c2854f4eba968004af3b04070e", "input": "Deep neural networks (DNNs) have witnessed as a powerful approach in this year by solving long-standing Artificial\n intelligence (AI) supervised and unsupervised tasks exists in natural language processing, speech processing, computer vision and others. In this paper, we attempt to apply DNNs on three different cyber security use cases: Android malware classification, incident detection and fraud detection. The data set of each use case contains real known benign and malicious activities samples. These use cases are part of Cybersecurity Data Mining Competition (CDMC) 2017. The efficient network architecture for DNNs is chosen by conducting various trails of experiments for network parameters and network structures. The experiments of such chosen efficient configurations of DNNs are run up to 1000 epochs with learning rate set in the range [0.01-0.5]. Experiments of DNNs performed well in comparison to the classical machine learning algorithm in all cases of experiments of cyber security use cases. This is due to the fact that DNNs implicitly extract and build better features, identifies the characteristics of the data that lead to better accuracy. The best accuracy obtained by DNNs and XGBoost on Android malware classification 0.940 and 0.741, incident detection 1.00 and 0.997, and fraud detection 0.972 and 0.916 respectively. The accuracy obtained by DNNs varies -0.05%, +0.02%, -0.01% from the top scored system in CDMC 2017 tasks.", "output": [ "Deep-Net: Deep Neural Network for Cyber Security Use Cases" ] }, { "id": "task668-0b579b58435448248e524979ba53a757", "input": "Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions to a large set of types spanning diverse domains such as biomedical, finance and sports. We observe that when the type set spans several domains, detection of entity mention becomes a limitation for supervised learning models. The primary reason being lack of dataset where entity boundaries are properly annotated while covering a large spectrum of entity types. Our work directly addresses this issue. We propose Heuristics Allied with Distant Supervision (HAnDS) framework to automatically construct a quality dataset suitable for the FgER task. HAnDS framework exploits the high interlink among Wikipedia and Freebase in a pipelined manner, reducing annotation errors introduced by naively using distant supervision approach. Using HAnDS framework, we create two datasets, one suitable for building FgER systems recognizing up to 118 entity types based on the FIGER type hierarchy and another for up to 1115 entity types based on the TypeNet hierarchy. Our extensive empirical experimentation warrants the quality of the generated datasets. Along with this, we also provide a manually annotated dataset for benchmarking FgER systems.", "output": [ "We initiate a push towards building ER systems to recognize thousands of types by providing a method to automatically construct suitable datasets based on the type hierarchy. " ] }, { "id": "task668-95be0d8b52c34bb39d9e4f8c37bf444e", "input": "Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application. \n However, we currently lack quantitative methods for model assessment. Because of this, while many GAN variants being proposed, we have relatively little understanding of their relative abilities. In this paper, we evaluate the performance of various types of GANs using divergence and distance functions typically used only for training. We observe consistency across the various proposed metrics and, interestingly, the test-time metrics do not favour networks that use the same training-time criterion. We also compare the proposed metrics to human perceptual scores.", "output": [ "An empirical evaluation on generative adversarial networks" ] }, { "id": "task668-f1dad532d24145f8b889865e2d249346", "input": "Permutations and matchings are core building blocks in a variety of latent variable models, as they allow us to align, canonicalize, and sort data. Learning in such models is difficult, however, because exact marginalization over these combinatorial objects is intractable. In response, this paper introduces a collection of new methods for end-to-end learning in such models that approximate discrete maximum-weight matching using the continuous Sinkhorn operator. Sinkhorn iteration is attractive because it functions as a simple, easy-to-implement analog of the softmax operator. With this, we can define the Gumbel-Sinkhorn method, an extension of the Gumbel-Softmax method (Jang et al. 2016, Maddison2016 et al. 2016) to distributions over latent matchings. We demonstrate the effectiveness of our method by outperforming competitive baselines on a range of qualitatively different tasks: sorting numbers, solving jigsaw puzzles, and identifying neural signals in worms.", "output": [ "A new method for gradient-descent inference of permutations, with applications to latent matching inference and supervised learning of permutations with neural networks", "The paper utilizes finite approximation of the Sinkhorn operator to describe how one can construct a neural network for learning from permutation valued training data. ", "The paper proposes a new method that approximates the discrete max-weight for learning latent permutations" ] }, { "id": "task668-6eee126f34a54b788c4a9621920f789c", "input": "Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant amount of hyperparameter tuning, neural architecture engineering, and a non-trivial amount of ``tricks\". The success in many practical applications coupled with the lack of a measure to quantify the failure modes of GANs resulted in a plethora of proposed losses, regularization and normalization schemes, and neural architectures. In this work we take a sober view of the current state of GANs from a practical perspective. We reproduce the current state of the art and go beyond fairly exploring the GAN landscape. We discuss common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub.", "output": [ "A sober view on the current state of GANs from a practical perspective" ] }, { "id": "task668-353cb5d6998d4944a0f772f6c63da34d", "input": "Previous work showed empirically that large neural networks can be significantly reduced in size while preserving their accuracy. Model compression became a central research topic, as it is crucial for deployment of neural networks on devices with limited computational and memory resources. The majority of the compression methods are based on heuristics and offer no worst-case guarantees on the trade-off between the compression rate and the approximation error for an arbitrarily new sample.\n\n We propose the first efficient, data-independent neural pruning algorithm with a provable trade-off between its compression rate and the approximation error for any future test sample. Our method is based on the coreset framework, which finds a small weighted subset of points that provably approximates the original inputs. Specifically, we approximate the output of a layer of neurons by a coreset of neurons in the previous layer and discard the rest. We apply this framework in a layer-by-layer fashion from the top to the bottom. Unlike previous works, our coreset is data independent, meaning that it provably guarantees the accuracy of the function for any input $x\\in \\mathbb{R}^d$, including an adversarial one. We demonstrate the effectiveness of our method on popular network architectures. In particular, our coresets yield 90% compression of the LeNet-300-100 architecture on MNIST while improving the accuracy.", "output": [ "We propose an efficient, provable and data independent method for network compression via neural pruning using coresets of neurons -- a novel construction proposed in this paper." ] }, { "id": "task668-b3a3a15cc04944de91c0a7f668d3b93d", "input": "Stochastic AUC maximization has garnered an increasing interest due to better fit to imbalanced data classification. However, existing works are limited to stochastic AUC maximization with a linear predictive model, which restricts its predictive power when dealing with extremely complex data. In this paper, we consider stochastic AUC maximization problem with a deep neural network as the predictive model. Building on the saddle point reformulation of a surrogated loss of AUC, the problem can be cast into a {\\it non-convex concave} min-max problem. The main contribution made in this paper is to make stochastic AUC maximization more practical for deep neural networks and big data with theoretical insights as well. In particular, we propose to explore Polyak-\\L{}ojasiewicz (PL) condition that has been proved and observed in deep learning, which enables us to develop new stochastic algorithms with even faster convergence rate and more practical step size scheme. An AdaGrad-style algorithm is also analyzed under the PL condition with adaptive convergence rate. Our experimental results demonstrate the effectiveness of the proposed algorithms.", "output": [ "The paper designs two algorithms for the stochastic AUC maximization problem with state-of-the-art complexities when using deep neural network as predictive model, which are also verified by empirical studies." ] }, { "id": "task668-2f3b7dd7c56f43b7849c0ee3c8a07d80", "input": "In this paper we investigate the family of functions representable by deep neural networks (DNN) with rectified linear units (ReLU). We give an algorithm to train a ReLU DNN with one hidden layer to {\\em global optimality} with runtime polynomial in the data size albeit exponential in the input dimension. Further, we improve on the known lower bounds on size (from exponential to super exponential) for approximating a ReLU deep net function by a shallower ReLU net. Our gap theorems hold for smoothly parametrized families of ``hard'' functions, contrary to countable, discrete families known in the literature. An example consequence of our gap theorems is the following: for every natural number $k$ there exists a function representable by a ReLU DNN with $k^2$ hidden layers and total size $k^3$, such that any ReLU DNN with at most $k$ hidden layers will require at least $\\frac12k^{k+1}-1$ total nodes. Finally, for the family of $\\R^n\\to \\R$ DNNs with ReLU activations, we show a new lowerbound on the number of affine pieces, which is larger than previous constructions in certain regimes of the network architecture and most distinctively our lowerbound is demonstrated by an explicit construction of a \\emph{smoothly parameterized} family of functions attaining this scaling. Our construction utilizes the theory of zonotopes from polyhedral theory.", "output": [ "This paper 1) characterizes functions representable by ReLU DNNs, 2) formally studies the benefit of depth in such architectures, 3) gives an algorithm to implement empirical risk minimization to global optimality for two layer ReLU nets." ] }, { "id": "task668-1e7be9fe229a4cab91884df4644e2e55", "input": "Predicting outcomes and planning interactions with the physical world are long-standing goals for machine learning. A variety of such tasks involves continuous physical systems, which can be described by partial differential equations (PDEs) with many degrees of freedom. Existing methods that aim to control the dynamics of such systems are typically limited to relatively short time frames or a small number of interaction parameters. We present a novel hierarchical predictor-corrector scheme which enables neural networks to learn to understand and control complex nonlinear physical systems over long time frames. We propose to split the problem into two distinct tasks: planning and control. To this end, we introduce a predictor network that plans optimal trajectories and a control network that infers the corresponding control parameters. Both stages are trained end-to-end using a differentiable PDE solver. We demonstrate that our method successfully develops an understanding of complex physical systems and learns to control them for tasks involving PDEs such as the incompressible Navier-Stokes equations.", "output": [ "We train a combination of neural networks to predict optimal trajectories for complex physical systems." ] }, { "id": "task668-b8584d490734444a8f89ac4c1606f2b6", "input": "Interpolation of data in deep neural networks has become a subject of significant research interest. We prove that over-parameterized single layer fully connected autoencoders do not merely interpolate, but rather, memorize training data: they produce outputs in (a non-linear version of) the span of the training examples. In contrast to fully connected autoencoders, we prove that depth is necessary for memorization in convolutional autoencoders. Moreover, we observe that adding nonlinearity to deep convolutional autoencoders results in a stronger form of memorization: instead of outputting points in the span of the training images, deep convolutional autoencoders tend to output individual training images. Since convolutional autoencoder components are building blocks of deep convolutional networks, we envision that our findings will shed light on the important question of the inductive bias in over-parameterized deep networks.\n", "output": [ " We identify memorization as the inductive bias of interpolation in overparameterized fully connected and convolutional auto-encoders. " ] }, { "id": "task668-d79f80b23d534fe5b21b4881d7759eae", "input": "Large-scale Long Short-Term Memory (LSTM) cells are often the building blocks of many state-of-the-art algorithms for tasks in Natural Language Processing (NLP). However, LSTMs are known to be computationally inefficient because the memory capacity of the models depends on the number of parameters, and the inherent recurrence that models the temporal dependency is not parallelizable. In this paper, we propose simple, but effective, low-rank matrix factorization (MF) algorithms to compress network parameters and significantly speed up LSTMs with almost no loss of performance (and sometimes even gain). To show the effectiveness of our method across different tasks, we examine two settings: 1) compressing core LSTM layers in Language Models, 2) compressing biLSTM layers of ELMo~\\citep{ELMo} and evaluate in three downstream NLP tasks (Sentiment Analysis, Textual Entailment, and Question Answering). The latter is particularly interesting as embeddings from large pre-trained biLSTM Language Models are often used as contextual word representations. Finally, we discover that matrix factorization performs better in general, additive recurrence is often more important than multiplicative recurrence, and we identify an interesting correlation between matrix norms and compression performance.\n\n", "output": [ "We propose simple, but effective, low-rank matrix factorization (MF) algorithms to speed up in running time, save memory, and improve the performance of LSTMs.", "Proposes to accelerate LSTM by using MF as the post-processing compression strategy and conducts extensive experiements to show the performance." ] }, { "id": "task668-012177819602495eac2bb84daa97c264", "input": "Language and vision are processed as two different modal in current work for image captioning. However, recent work on Super Characters method shows the effectiveness of two-dimensional word embedding, which converts text classification problem into image classification problem. In this paper, we propose the SuperCaptioning method, which borrows the idea of two-dimensional word embedding from Super Characters method, and processes the information of language and vision together in one single CNN model. The experimental results on Flickr30k data shows the proposed method gives high quality image captions. An interactive demo is ready to show at the workshop.", "output": [ "Image captioning using two-dimensional word embedding." ] }, { "id": "task668-49c05073af494cbbb9e903a7ab012a59", "input": "The geometric properties of loss surfaces, such as the local flatness of a solution, are associated with generalization in deep learning. The Hessian is often used to understand these geometric properties. We investigate the differences between the eigenvalues of the neural network Hessian evaluated over the empirical dataset, the Empirical Hessian, and the eigenvalues of the Hessian under the data generating distribution, which we term the True Hessian. Under mild assumptions, we use random matrix theory to show that the True Hessian has eigenvalues of smaller absolute value than the Empirical Hessian. We support these results for different SGD schedules on both a 110-Layer ResNet and VGG-16. To perform these experiments we propose a framework for spectral visualization, based on GPU accelerated stochastic Lanczos quadrature. This approach is an order of magnitude faster than state-of-the-art methods for spectral visualization, and can be generically used to investigate the spectral properties of matrices in deep learning.", "output": [ "Understanding the neural network Hessian eigenvalues under the data generating distribution.", "This paper analyzes the spectrum of the Hessian matrix of large neural networks, with an analysis of max/min eigenvalues and visualization of spectra using a Lanczos quadrature approach.", "This paper uses the random matrix theory to study the spectrum distribution of the empirical Hessian and true Hessian for deep learning, and proposes an efficient spectrum visualization methods." ] }, { "id": "task668-bd1ca94c9b5d47a59e41ae92c9cec4c1", "input": "In this paper, we aim to develop a novel mechanism to preserve differential privacy (DP) in adversarial learning for deep neural networks, with provable robustness to adversarial examples. We leverage the sequential composition theory in DP, to establish a new connection between DP preservation and provable robustness. To address the trade-off among model utility, privacy loss, and robustness, we design an original, differentially private, adversarial objective function, based on the post-processing property in DP, to tighten the sensitivity of our model. An end-to-end theoretical analysis and thorough evaluations show that our mechanism notably improves the robustness of DP deep neural networks.", "output": [ "Preserving Differential Privacy in Adversarial Learning with Provable Robustness to Adversarial Examples" ] }, { "id": "task668-6919b863213e49138fbaf71b337cbae3", "input": "In the adversarial-perturbation problem of neural networks, an adversary starts with a neural network model $F$ and a point $\\bfx$ that $F$ classifies correctly, and applies a \\emph{small perturbation} to $\\bfx$ to produce another point $\\bfx'$ that $F$ classifies \\emph{incorrectly}. In this paper, we propose taking into account \\emph{the inherent confidence information} produced by models when studying adversarial perturbations, where a natural measure of ``confidence'' is \\|F(\\bfx)\\|_\\infty$ (i.e. how confident $F$ is about its prediction?) . Motivated by a thought experiment based on the manifold assumption, we propose a ``goodness property'' of models which states that \\emph{confident regions of a good model should be well separated}. We give formalizations of this property and examine existing robust training objectives in view of them. Interestingly, we find that a recent objective by Madry et al. encourages training a model that satisfies well our formal version of the goodness property, but has a weak control of points that are wrong but with low confidence. However, if Madry et al.'s model is indeed a good solution to their objective, then good and bad points are now distinguishable and we can try to embed uncertain points back to the closest confident region to get (hopefully) correct predictions. We thus propose embedding objectives and algorithms, and perform an empirical study using this method. Our experimental results are encouraging: Madry et al.'s model wrapped with our embedding procedure achieves almost perfect success rate in defending against attacks that the base model fails on, while retaining good generalization behavior.\n", "output": [ "Defending against adversarial perturbations of neural networks from manifold assumption ", "The manuscript proposes two objective functions based on the manifold assumption as defense mechanisms against adversarial examples.", "Defending against adversarial attacks based on the manifold assumption of natural data" ] }, { "id": "task668-6d59f8006e5646799180e35603e4aba3", "input": "We introduce a novel method that enables parameter-efficient transfer and multi-task learning with deep neural networks. The basic approach is to learn a model patch - a small set of parameters - that will specialize to each task, instead of fine-tuning the last layer or the entire network. For instance, we show that learning a set of scales and biases is sufficient to convert a pretrained network to perform well on qualitatively different problems (e.g. converting a Single Shot MultiBox Detection (SSD) model into a 1000-class image classification model while reusing 98% of parameters of the SSD feature extractor). Similarly, we show that re-learning existing low-parameter layers (such as depth-wise convolutions) while keeping the rest of the network frozen also improves transfer-learning accuracy significantly. Our approach allows both simultaneous (multi-task) as well as sequential transfer learning. In several multi-task learning problems, despite using much fewer parameters than traditional logits-only fine-tuning, we match single-task performance. \n", "output": [ "A novel and practically effective method to adapt pretrained neural networks to new tasks by retraining a minimal (e.g., less than 2%) number of parameters" ] }, { "id": "task668-3b9b603ddad64b51aac2a9e6304a4f5a", "input": "We propose a method to incrementally learn an embedding space over the domain of network architectures, to enable the careful selection of architectures for evaluation during compressed architecture search. Given a teacher network, we search for a compressed network architecture by using Bayesian Optimization (BO) with a kernel function defined over our proposed embedding space to select architectures for evaluation. We demonstrate that our search algorithm can significantly outperform various baseline methods, such as random search and reinforcement learning (Ashok et al., 2018). The compressed architectures found by our method are also better than the state-of-the-art manually-designed compact architecture ShuffleNet (Zhang et al., 2018). We also demonstrate that the learned embedding space can be transferred to new settings for architecture search, such as a larger teacher network or a teacher network in a different architecture family, without any training.", "output": [ "We propose a method to incrementally learn an embedding space over the domain of network architectures, to enable the careful selection of architectures for evaluation during compressed architecture search." ] }, { "id": "task668-7341269e69ee4497a12ea7f7d4596a20", "input": "Generative models are important tools to capture and investigate the properties of complex empirical data. Recent developments such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) use two very similar, but \\textit{reverse}, deep convolutional architectures, one to generate and one to extract information from data. Does learning the parameters of both architectures obey the same rules? We exploit the causality principle of independence of mechanisms to quantify how the weights of successive layers adapt to each other. Using the recently introduced Spectral Independence Criterion, we quantify the dependencies between the kernels of successive convolutional layers and show that those are more independent for the generative process than for information extraction, in line with results from the field of causal inference. In addition, our experiments on generation of human faces suggest that more independence between successive layers of generators results in improved performance of these architectures.\n", "output": [ "We use causal inference to characterise the architecture of generative models", "This paper examines the nature of convolutional filters in the encoder and a decoder of a VAE, and a generator and a discriminator of a GAN.", "This work exploits the causality principle to quantify how the weights of successive layers adapt to each other." ] }, { "id": "task668-134ef69582c640c59bf00a71a9e6f83e", "input": "Given the variety of the visual world there is not one true scale for recognition: objects may appear at drastically different sizes across the visual field. Rather than enumerate variations across filter channels or pyramid levels, dynamic models locally predict scale and adapt receptive fields accordingly. The degree of variation and diversity of inputs makes this a difficult task. Existing methods either learn a feedforward predictor, which is not itself totally immune to the scale variation it is meant to counter, or select scales by a fixed algorithm, which cannot learn from the given task and data. We extend dynamic scale inference from feedforward prediction to iterative optimization for further adaptivity. We propose a novel entropy minimization objective for inference and optimize over task and structure parameters to tune the model to each input. Optimization during inference improves semantic segmentation accuracy and generalizes better to extreme scale variations that cause feedforward dynamic inference to falter.", "output": [ "Unsupervised optimization during inference gives top-down feedback to iteratively adjust feedforward prediction of scale variation for more equivariant recognition." ] }, { "id": "task668-1590865c5d744bdca18e2dc5ba70fed3", "input": "Brushing techniques have a long history with the first interactive selection tools appearing in the 1990's. Since then, many additional techniques have been developed to address selection accuracy, scalability and flexibility issues. Selection is especially difficult in large datasets where many visual items tangle and create overlapping. This paper investigates a novel brushing technique which not only relies on the actual brushing location but also on the shape of the brushed area. Firstly, the user brushes the region where trajectories of interest are visible. Secondly, the shape of the brushed area is used to select similar items. Thirdly, the user can adjust the degree of similarity to filter out the requested trajectories. This technique encompasses two types of comparison metrics, the piece-wise Pearson correlation and the similarity measurement based on information geometry. We apply it to concrete scenarios with datasets from air traffic control, eye-tracking data and GPS trajectories.\n", "output": [ "Interactive technique to improve brushing in dense trajectory datasets by taking into account the shape of the brush." ] }, { "id": "task668-f005a1f51bab4c059114ca0747a40195", "input": "Parametric texture models have been applied successfully to synthesize artificial images. Psychophysical studies show that under defined conditions observers are unable to differentiate between model-generated and original natural textures. In industrial applications the reverse case is of interest: a texture analysis system should decide if human observers are able to discriminate between a reference and a novel texture. For example, in case of inspecting decorative surfaces the de- tection of visible texture anomalies without any prior knowledge is required. Here, we implemented a human-vision-inspired novelty detection approach. Assuming that the features used for texture synthesis are important for human texture percep- tion, we compare psychophysical as well as learnt texture representations based on activations of a pretrained CNN in a novelty detection scenario. Additionally, we introduce a novel objective function to train one-class neural networks for novelty detection and compare the results to standard one-class SVM approaches. Our experiments clearly show the differences between human-vision-inspired texture representations and learnt features in detecting visual anomalies. Based on a dig- ital print inspection scenario we show that psychophysical texture representations are able to outperform CNN-encoded features.", "output": [ "Comparison of psychophysical and CNN-encoded texture representations in a one-class neural network novelty detection application.", "This paper focuses on novelty detection and shows that psychophysical representations can outperform VGG-encoder features in some part of this task", "This paper considers detecting anomalies in textures and proposes original loss function.", "Proposes training two anomaly detectors from three different models to detect perceptual anomalies in visual textures." ] }, { "id": "task668-f0350e42e9fc47b09ef4772beb80fbaf", "input": "Neural networks can converge faster with help from a smarter batch selection strategy. In this regard, we propose Ada-Boundary, a novel adaptive-batch selection algorithm that constructs an effective mini-batch according to the learning progress of the model.Our key idea is to present confusing samples what the true label is. Thus, the samples near the current decision boundary are considered as the most effective to expedite convergence. Taking advantage of our design, Ada-Boundary maintains its dominance in various degrees of training difficulty. We demonstrate the advantage of Ada-Boundary by extensive experiments using two convolutional neural networks for three benchmark data sets. The experiment results show that Ada-Boundary improves the training time by up to 31.7% compared with the state-of-the-art strategy and by up to 33.5% compared with the baseline strategy.", "output": [ "We suggest a smart batch selection technique called Ada-Boundary." ] }, { "id": "task668-67489ccb84f44ce2933f085370b9f4b2", "input": "Existing neural question answering (QA) models are required to reason over and draw complicated inferences from a long context for most large-scale QA datasets. However, if we view QA as a combined retrieval and reasoning task, we can assume the existence of a minimal context which is necessary and sufficient to answer a given question. Recent work has shown that a sentence selector module that selects a shorter context and feeds it to the downstream QA model achieves performance comparable to a QA model trained on full context, while also being more interpretable. Recent work has also shown that most state-of-the-art QA models break when adversarially generated sentences are appended to the context. While humans are immune to such distractor sentences, QA models get easily misled into selecting answers from these sentences. We hypothesize that the sentence selector module can filter out extraneous context, thereby allowing the downstream QA model to focus and reason over the parts of the context that are relevant to the question. In this paper, we show that the sentence selector itself is susceptible to adversarial inputs. However, we demonstrate that a pipeline consisting of a sentence selector module followed by the QA model can be made more robust to adversarial attacks in comparison to a QA model trained on full context. Thus, we provide evidence towards a modular approach for question answering that is more robust and interpretable.", "output": [ "A modular approach consisting of a sentence selector module followed by the QA model can be made more robust to adversarial attacks in comparison to a QA model trained on full context." ] }, { "id": "task668-4366cb1466ae4badb09c84740a9d3a47", "input": "Generative adversarial networks are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributions. GANs, as normally formulated, rely on the generated samples being completely differentiable w.r.t. the generative parameters, and thus do not work for discrete data. We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator. The importance weights have a strong connection to the decision boundary of the discriminator, and we call our method boundary-seeking GANs (BGANs). We demonstrate the effectiveness of the proposed algorithm with discrete image and character-based natural language generation. In addition, the boundary-seeking objective extends to continuous data, which can be used to improve stability of training, and we demonstrate this on Celeba, Large-scale Scene Understanding (LSUN) bedrooms, and Imagenet without conditioning.", "output": [ "We address training GANs with discrete data by formulating a policy gradient that generalizes across f-divergences" ] }, { "id": "task668-aa01c63dbc154e4baee25457839fb34e", "input": "The extended Kalman filter (EKF) is a classical signal processing algorithm which performs efficient approximate Bayesian inference in non-conjugate models by linearising the local measurement function, avoiding the need to compute intractable integrals when calculating the posterior. In some cases the EKF outperforms methods which rely on cubature to solve such integrals, especially in time-critical real-world problems. The drawback of the EKF is its local nature, whereas state-of-the-art methods such as variational inference or expectation propagation (EP) are considered global approximations. We formulate power EP as a nonlinear Kalman filter, before showing that linearisation results in a globally iterated algorithm that exactly matches the EKF on the first pass through the data, and iteratively improves the linearisation on subsequent passes. An additional benefit is the ability to calculate the limit as the EP power tends to zero, which removes the instability of the EP-like algorithm. The resulting inference scheme solves non-conjugate temporal Gaussian process models in linear time, $\\mathcal{O}(n)$, and in closed form.", "output": [ "We unify the extended Kalman filter (EKF) and the state space approach to power expectation propagation (PEP) by solving the intractable moment matching integrals in PEP via linearisation. This leads to a globally iterated extension of the EKF." ] }, { "id": "task668-12bee064d5b043fd841407dd18136371", "input": "We study the problem of generating adversarial examples in a black-box setting in which only loss-oracle access to a model is available. We introduce a framework that conceptually unifies much of the existing work on black-box attacks, and demonstrate that the current state-of-the-art methods are optimal in a natural sense. Despite this optimality, we show how to improve black-box attacks by bringing a new element into the problem: gradient priors. We give a bandit optimization-based algorithm that allows us to seamlessly integrate any such priors, and we explicitly identify and incorporate two examples. The resulting methods use two to four times fewer queries and fail two to five times less than the current state-of-the-art. The code for reproducing our work is available at https://git.io/fAjOJ.", "output": [ "We present a unifying view on black-box adversarial attacks as a gradient estimation problem, and then present a framework (based on bandits optimization) to integrate priors into gradient estimation, leading to significantly increased performance." ] }, { "id": "task668-6249838611a64eb2ade73ac2e9d920d5", "input": "We consider a problem of learning the reward and policy from expert examples under unknown dynamics. Our proposed method builds on the framework of generative adversarial networks and introduces the empowerment-regularized maximum-entropy inverse reinforcement learning to learn near-optimal rewards and policies. Empowerment-based regularization prevents the policy from overfitting to expert demonstrations, which advantageously leads to more generalized behaviors that result in learning near-optimal rewards. Our method simultaneously learns empowerment through variational information maximization along with the reward and policy under the adversarial learning formulation. We evaluate our approach on various high-dimensional complex control tasks. We also test our learned rewards in challenging transfer learning problems where training and testing environments are made to be different from each other in terms of dynamics or structure. The results show that our proposed method not only learns near-optimal rewards and policies that are matching expert behavior but also performs significantly better than state-of-the-art inverse reinforcement learning algorithms.", "output": [ "Our method introduces the empowerment-regularized maximum-entropy inverse reinforcement learning to learn near-optimal rewards and policies from expert demonstrations." ] }, { "id": "task668-26028e854383488b8ed9b9db2a6e6a85", "input": "Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as point clouds. We introduce a deep autoencoder (AE) network with excellent reconstruction quality and generalization ability. The learned representations outperform the state of the art in 3D recognition tasks and enable basic shape editing applications via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation. We also perform a thorough study of different generative models including GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space our AEs and, Gaussian mixture models (GMM). Interestingly, GMMs trained in the latent space of our AEs produce samples of the best fidelity and diversity.\n To perform our quantitative evaluation of generative models, we propose simple measures of fidelity and diversity based on optimally matching between sets point clouds.", "output": [ "Deep autoencoders to learn a good representation for geometric 3D point-cloud data; Generative models for point clouds.", "Approaches to learn GAN-type generative models using PointNet architecture and latent-space GAN." ] }, { "id": "task668-f8e681144d6d4e718fc0e7ca8518e7e5", "input": "Batch Normalization (BN) is one of the most widely used techniques in Deep Learning field. But its performance can awfully degrade with insufficient batch size. This weakness limits the usage of BN on many computer vision tasks like detection or segmentation, where batch size is usually small due to the constraint of memory consumption. Therefore many modified normalization techniques have been proposed, which either fail to restore the performance of BN completely, or have to introduce additional nonlinear operations in inference procedure and increase huge consumption. In this paper, we reveal that there are two extra batch statistics involved in backward propagation of BN, on which has never been well discussed before. The extra batch statistics associated with gradients also can severely affect the training of deep neural network. Based on our analysis, we propose a novel normalization method, named Moving Average Batch Normalization (MABN). MABN can completely restore the performance of vanilla BN in small batch cases, without introducing any additional nonlinear operations in inference procedure. We prove the benefits of MABN by both theoretical analysis and experiments. Our experiments demonstrate the effectiveness of MABN in multiple computer vision tasks including ImageNet and COCO. The code has been released in https://github.com/megvii-model/MABN.", "output": [ "We propose a novel normalization method to handle small batch size cases.", "A method to deal with the small batch size problem of BN which applies moving average operation without too much overhead and reduces the number of statistics of BN for better stability." ] }, { "id": "task668-a68698ba6eb1457cb2fdacfa3c93ac48", "input": "Variational inference (VI) methods and especially variational autoencoders (VAEs) specify scalable generative models that enjoy an intuitive connection to manifold learning --- with many default priors the posterior/likelihood pair $q(z|x)$/$p(x|z)$ can be viewed as an approximate homeomorphism (and its inverse) between the data manifold and a latent Euclidean space. However, these approximations are well-documented to become degenerate in training. Unless the subjective prior is carefully chosen, the topologies of the prior and data distributions often will not match.\n Conversely, diffusion maps (DM) automatically \\textit{infer} the data topology and enjoy a rigorous connection to manifold learning, but do not scale easily or provide the inverse homeomorphism.\n In this paper, we propose \\textbf{a)} a principled measure for recognizing the mismatch between data and latent distributions and \\textbf{b)} a method that combines the advantages of variational inference and diffusion maps to learn a homeomorphic generative model. The measure, the \\textit{locally bi-Lipschitz property}, is a sufficient condition for a homeomorphism and easy to compute and interpret. The method, the \\textit{variational diffusion autoencoder} (VDAE), is a novel generative algorithm that first infers the topology of the data distribution, then models a diffusion random walk over the data. To achieve efficient computation in VDAEs, we use stochastic versions of both variational inference and manifold learning optimization. We prove approximation theoretic results for the dimension dependence of VDAEs, and that locally isotropic sampling in the latent space results in a random walk over the reconstructed manifold.\n Finally, we demonstrate the utility of our method on various real and synthetic datasets, and show that it exhibits performance superior to other generative models.", "output": [ "We combine variational inference and manifold learning (specifically VAEs and diffusion maps) to build a generative model based on a diffusion random walk on a data manifold; we generate samples by drawing from the walk's stationary distribution." ] }, { "id": "task668-f80f50a86e5a4a90887be8829a2fc9ac", "input": "We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels. Our method produces a sequence of feature maps, iteratively refining the SVM margin. We provide rigorous guarantees for optimality and generalization, interpreting our algorithm as online equilibrium-finding dynamics in a certain two-player min-max game. Evaluations on synthetic and real-world datasets demonstrate scalability and consistent improvements over related random features-based methods.", "output": [ "A simple and practical algorithm for learning a margin-maximizing translation-invariant or spherically symmetric kernel from training data, using tools from Fourier analysis and regret minimization.", "The paper proposes to learn a custom translation or rotation invariant kernal in the Fourier representation to maximize the margin of SVM.", "The authors propose an interesting algorithm for learning the l1-SVM and the Fourier represented kernel together", "The authors consider learning directly Fourier representations of shift/translation invariant kernels for machine learning applications with the alignment of the kernel to data as the objective function to optimize." ] }, { "id": "task668-08dd591bcd7646a8a78ba433b90c2078", "input": "We present a tool for Interactive Visual Exploration of Latent Space (IVELS) for model selection. Evaluating generative models of discrete sequences from a continuous latent space is a challenging problem, since their optimization involves multiple competing objective terms. We introduce a model-selection pipeline to compare and filter models throughout consecutive stages of more complex and expensive metrics. We present the pipeline in an interactive visual tool to enable the exploration of the metrics, analysis of the learned latent space, and selection of the best model for a given task. We focus specifically on the variational auto-encoder family in a case study of modeling peptide sequences, which are short sequences of amino acids. This task is especially interesting due to the presence of multiple attributes we want to model. We demonstrate how an interactive visual comparison can assist in evaluating how well an unsupervised auto-encoder meaningfully captures the attributes of interest in its latent space.", "output": [ "We present a visual tool to interactively explore the latent space of an auto-encoder for peptide sequences and their attributes." ] }, { "id": "task668-5d9323976d964cf6bea07149253b0ef9", "input": "Challenges in natural sciences can often be phrased as optimization problems. Machine learning techniques have recently been applied to solve such problems. One example in chemistry is the design of tailor-made organic materials and molecules, which requires efficient methods to explore the chemical space. We present a genetic algorithm (GA) that is enhanced with a neural network (DNN) based discriminator model to improve the diversity of generated molecules and at the same time steer the GA. We show that our algorithm outperforms other generative models in optimization tasks. We furthermore present a way to increase interpretability of genetic algorithms, which helped us to derive design principles", "output": [ "Tackling inverse design via genetic algorithms augmented with deep neural networks. " ] }, { "id": "task668-d76c2b7514154a7882dbaa4fd2bee974", "input": "Adversarial examples are a pervasive phenomenon of machine learning models where seemingly imperceptible perturbations to the input lead to misclassifications for otherwise statistically accurate models. We propose a geometric framework, drawing on tools from the manifold reconstruction literature, to analyze the high-dimensional geometry of adversarial examples. In particular, we highlight the importance of codimension: for low-dimensional data manifolds embedded in high-dimensional space there are many directions off the manifold in which to construct adversarial examples. Adversarial examples are a natural consequence of learning a decision boundary that classifies the low-dimensional data manifold well, but classifies points near the manifold incorrectly. Using our geometric framework we prove (1) a tradeoff between robustness under different norms, (2) that adversarial training in balls around the data is sample inefficient, and (3) sufficient sampling conditions under which nearest neighbor classifiers and ball-based adversarial training are robust.", "output": [ "We present a geometric framework for proving robustness guarantees and highlight the importance of codimension in adversarial examples. ", "This paper gives a theoretical analysis of adversarial examples, showing that there exists a tradeoff between robustness in different norms, adversarial training is sample inefficient, and the nearest neighbor classifier can be robust under certain conditions." ] }, { "id": "task668-72c02748dab4458193e3ea15a3f78c71", "input": "Actor-critic methods solve reinforcement learning problems by updating a parameterized policy known as an actor in a direction that increases an estimate of the expected return known as a critic. However, existing actor-critic methods only use values or gradients of the critic to update the policy parameter. In this paper, we propose a novel actor-critic method called the guide actor-critic (GAC). GAC firstly learns a guide actor that locally maximizes the critic and then it updates the policy parameter based on the guide actor by supervised learning. Our main theoretical contributions are two folds. First, we show that GAC updates the guide actor by performing second-order optimization in the action space where the curvature matrix is based on the Hessians of the critic. Second, we show that the deterministic policy gradient method is a special case of GAC when the Hessians are ignored. Through experiments, we show that our method is a promising reinforcement learning method for continuous controls.\n", "output": [ "This paper proposes a novel actor-critic method that uses Hessians of a critic to update an actor." ] }, { "id": "task668-7849d1c399ce44549096e30467dd68df", "input": "In this paper, we introduce a method to compress intermediate feature maps of deep neural networks (DNNs) to decrease memory storage and bandwidth requirements during inference. Unlike previous works, the proposed method is based on converting fixed-point activations into vectors over the smallest GF(2) finite field followed by nonlinear dimensionality reduction (NDR) layers embedded into a DNN. Such an end-to-end learned representation finds more compact feature maps by exploiting quantization redundancies within the fixed-point activations along the channel or spatial dimensions. We apply the proposed network architecture to the tasks of ImageNet classification and PASCAL VOC object detection. Compared to prior approaches, the conducted experiments show a factor of 2 decrease in memory requirements with minor degradation in accuracy while adding only bitwise computations.", "output": [ "Feature map compression method that converts quantized activations into binary vectors followed by nonlinear dimensionality reduction layers embedded into a DNN" ] }, { "id": "task668-995c668961bb4a7ba4e426011f4d83cd", "input": "Deep neural networks have achieved great success in classification tasks during the last years. However, one major problem to the path towards artificial intelligence is the inability of neural networks to accurately detect samples from novel class distributions and therefore, most of the existent classification algorithms assume that all classes are known prior to the training stage. In this work, we propose a methodology for training a neural network that allows it to efficiently detect out-of-distribution (OOD) examples without compromising much of its classification accuracy on the test examples from known classes. Based on the Outlier Exposure (OE) technique, we propose a novel loss function that achieves state-of-the-art results in out-of-distribution detection with OE both on image and text classification tasks. Additionally, the way this method was constructed makes it suitable for training any classification algorithm that is based on Maximum Likelihood methods.", "output": [ "We propose a novel loss function that achieves state-of-the-art results in out-of-distribution detection with Outlier Exposure both on image and text classification tasks.", "This paper tackles the problems of out-of-distribution detection and model calibration by adapting the loss function of the Outlier Exposure technique, with results demonstrating increased performance over OE on vision and text benchmarks and improved model calibration.", "Proposal for a new loss function to train the network with Outlier Exposure which leads to better OOD detection compared to simple loss functions using KL divergence." ] }, { "id": "task668-b65f630f0e034f34ace54e505e884793", "input": "In this paper, we investigate lossy compression of deep neural networks (DNNs) by weight quantization and lossless source coding for memory-efficient deployment. Whereas the previous work addressed non-universal scalar quantization and entropy coding of DNN weights, we for the first time introduce universal DNN compression by universal vector quantization and universal source coding. In particular, we examine universal randomized lattice quantization of DNNs, which randomizes DNN weights by uniform random dithering before lattice quantization and can perform near-optimally on any source without relying on knowledge of its probability distribution. Moreover, we present a method of fine-tuning vector quantized DNNs to recover the performance loss after quantization. Our experimental results show that the proposed universal DNN compression scheme compresses the 32-layer ResNet (trained on CIFAR-10) and the AlexNet (trained on ImageNet) with compression ratios of $47.1$ and $42.5$, respectively.", "output": [ "We introduce the universal deep neural network compression scheme, which is applicable universally for compression of any models and can perform near-optimally regardless of their weight distribution.", "Introduces a pipeline for network compression that is similar to deep compression and uses randomized lattice quantization instead of the classical vector quantization, and uses universal source coding (bzip2) instead of Huffman coding." ] }, { "id": "task668-6d84f74515d34448812829cfb9f312de", "input": "This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. Text-based computer games describe their world to the player through natural language and expect the player to interact with the game using text. These games are of interest as they can be seen as a testbed for language understanding, problem-solving, and language generation by artificial agents. Moreover, they provide a learning environment in which these skills can be acquired through interactions with an environment rather than using fixed corpora. \n One aspect that makes these games particularly challenging for learning agents is the combinatorially large action space.\n Existing methods for solving text-based games are limited to games that are either very simple or have an action space restricted to a predetermined set of admissible actions. In this work, we propose to use the exploration approach of Go-Explore (Ecoffet et al., 2019) for solving text-based games. More specifically, in an initial exploration phase, we first extract trajectories with high rewards, after which we train a policy to solve the game by imitating these trajectories.\n Our experiments show that this approach outperforms existing solutions in solving text-based games, and it is more sample efficient in terms of the number of interactions with the environment. Moreover, we show that the learned policy can generalize better than existing solutions to unseen games without using any restriction on the action space.", "output": [ "This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. " ] }, { "id": "task668-ac880743aeec457697b8f59c9f551beb", "input": "In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem. Wasserstein GANs have largely promoted the stability over the original GANs by introducing Wasserstein distance, but still remain unstable and are prone to a variety of failure modes. In this paper, we present a general framework named Wasserstein-Bounded GAN (WBGAN), which improves a large family of WGAN-based approaches by simply adding an upper-bound constraint to the Wasserstein term. Furthermore, we show that WBGAN can reasonably measure the difference of distributions which almost have no intersection. Experiments demonstrate that WBGAN can stabilize as well as accelerate convergence in the training processes of a series of WGAN-based variants.", "output": [ "Propose an improved framework for WGANs and demonstrate its better performance in theory and practice." ] }, { "id": "task668-a37a0d7a60f444b3a2a813aa2812f608", "input": "Humans can understand and produce new utterances effortlessly, thanks to their systematic compositional skills. Once a person learns the meaning of a new verb \"dax,\" he or she can immediately understand the meaning of \"dax twice\" or \"sing and dax.\" In this paper, we introduce the SCAN domain, consisting of a set of simple compositional navigation commands paired with the corresponding action sequences. We then test the zero-shot generalization capabilities of a variety of recurrent neural networks (RNNs) trained on SCAN with sequence-to-sequence methods. We find that RNNs can generalize well when the differences between training and test commands are small, so that they can apply \"mix-and-match\" strategies to solve the task. However, when generalization requires systematic compositional skills (as in the \"dax\" example above), RNNs fail spectacularly. We conclude with a proof-of-concept experiment in neural machine translation, supporting the conjecture that lack of systematicity is an important factor explaining why neural networks need very large training sets.", "output": [ "Using a simple language-driven navigation task, we study the compositional capabilities of modern seq2seq recurrent networks.", "\nThis paper focuses on the zero-shot learning compositional capabilities of modern sequence-to-sequence RNNs and exposes the short-comings of current seq2seq RNN architectures.", "The paper analyzes the composition abilities of RNNs, specifically, the generalization ability of RNNs on random subset of SCAN commands, on longer SCAN commands, and of composition over primitive commands. ", "The authors introduce a new dataset that facilitates the analysis of a Seq2Seq learning case" ] }, { "id": "task668-d7c47efc962e4ef4afa2c061faddc12e", "input": "The recently presented idea to learn heuristics for combinatorial optimization problems is promising as it can save costly development. However, to push this idea towards practical implementation, we need better models and better ways of training. We contribute in both directions: we propose a model based on attention layers with benefits over the Pointer Network and we show how to train this model using REINFORCE with a simple baseline based on a deterministic greedy rollout, which we find is more efficient than using a value function. We significantly improve over recent learned heuristics for the Travelling Salesman Problem (TSP), getting close to optimal results for problems up to 100 nodes. With the same hyperparameters, we learn strong heuristics for two variants of the Vehicle Routing Problem (VRP), the Orienteering Problem (OP) and (a stochastic variant of) the Prize Collecting TSP (PCTSP), outperforming a wide range of baselines and getting results close to highly optimized and specialized algorithms.", "output": [ "Attention based model trained with REINFORCE with greedy rollout baseline to learn heuristics with competitive results on TSP and other routing problems", "Presents an attention-based approach to learning a policy for solving TSP and other routing-type combinatorial optimzation problems.", "This paper trys to learn heuristics for solving combinatorial optimisation problems" ] }, { "id": "task668-9e13ed55032b4ef8b66331baa38fe002", "input": "Parallel developments in neuroscience and deep learning have led to mutually productive exchanges, pushing our understanding of real and artificial neural networks in sensory and cognitive systems. However, this interaction between fields is less developed in the study of motor control. In this work, we develop a virtual rodent as a platform for the grounded study of motor activity in artificial models of embodied control. We then use this platform to study motor activity across contexts by training a model to solve four complex tasks. Using methods familiar to neuroscientists, we describe the behavioral representations and algorithms employed by different layers of the network using a neuroethological approach to characterize motor activity relative to the rodent's behavior and goals. We find that the model uses two classes of representations which respectively encode the task-specific behavioral strategies and task-invariant behavioral kinematics. These representations are reflected in the sequential activity and population dynamics of neural subpopulations. Overall, the virtual rodent facilitates grounded collaborations between deep reinforcement learning and motor neuroscience.", "output": [ "We built a physical simulation of a rodent, trained it to solve a set of tasks, and analyzed the resulting networks." ] }, { "id": "task668-163b3163808a47b4a898f6429ee0c277", "input": "We address the problem of learning to discover 3D parts for objects in unseen categories. Being able to learn the geometry prior of parts and transfer this prior to unseen categories pose fundamental challenges on data-driven shape segmentation approaches. Formulated as a contextual bandit problem, we propose a learning-based iterative grouping framework which learns a grouping policy to progressively merge small part proposals into bigger ones in a bottom-up fashion. At the core of our approach is to restrict the local context for extracting part-level features, which encourages the generalizability to novel categories. On a recently proposed large-scale fine-grained 3D part dataset, PartNet, we demonstrate that our method can transfer knowledge of parts learned from 3 training categories to 21 unseen testing categories without seeing any annotated samples. Quantitative comparisons against four strong shape segmentation baselines show that we achieve the state-of-the-art performance.", "output": [ "A zero-shot segmentation framework for 3D object part segmentation. Model the segmentation as a decision-making process and solve as a contextual bandit problem.", "A method for segmenting 3D point clouds of objects into component parts, focused on generalizing part groupings to novel object categories unseen during training, that shows strong performance relative to baselines.", "This paper proposes a method for part segmentation in object point clouds." ] }, { "id": "task668-b7d5bd8f91534f0eb6c278d93b78ce7d", "input": "The incorporation of prior knowledge into learning is essential in achieving good performance based on small noisy samples. Such knowledge is often incorporated through the availability of related data arising from domains and tasks similar to the one of current interest. Ideally one would like to allow both the data for the current task and for previous related tasks to self-organize the learning system in such a way that commonalities and differences between the tasks are learned in a data-driven fashion. We develop a framework for learning multiple tasks simultaneously, based on sharing features that are common to all tasks, achieved through the use of a modular deep feedforward neural network consisting of shared branches, dealing with the common features of all tasks, and private branches, learning the specific unique aspects of each task. Once an appropriate weight sharing architecture has been established, learning takes place through standard algorithms for feedforward networks, e.g., stochastic gradient descent and its variations. The method deals with meta-learning (such as domain adaptation, transfer and multi-task learning) in a unified fashion, and can easily deal with data arising from different types of sources. Numerical experiments demonstrate the effectiveness of learning in domain adaptation and transfer learning setups, and provide evidence for the flexible and task-oriented representations arising in the network.", "output": [ "A generic framework for handling transfer and multi-task learning using pairs of autoencoders with task-specific and shared weights.", "Proposes a generic framework for end-to-end transfer learning / domain adaptation with deep neural networks. ", "This paper proposes a model for allowing deep neural network architectures to share parameters across different datasets, and applies it to transfer learning.", "The paper focuses on learning common features from multiple domains data and ends up with a general architecture for multi-task, semi-supervised and transfer learning" ] }, { "id": "task668-7302a4b0b0da4eb3af0d9913671cf14d", "input": "We introduce a more efficient neural architecture for amortized inference, which combines continuous and conditional normalizing flows using a principled choice of structure. Our gradient flow derives its sparsity pattern from the minimally faithful inverse of its underlying graphical model. We find that this factorization reduces the necessary numbers both of parameters in the neural network and of adaptive integration steps in the ODE solver. Consequently, the throughput at training time and inference time is increased, without decreasing performance in comparison to unconstrained flows. By expressing the structural inversion and the flow construction as compilation passes of a probabilistic programming language, we demonstrate their applicability to the stochastic inversion of realistic models such as convolutional neural networks (CNN).", "output": [ "We introduce a more efficient neural architecture for amortized inference, which combines continuous and conditional normalizing flows using a principled choice of sparsity structure." ] }, { "id": "task668-2a624467b8084ac2afbc0aff3c0a0165", "input": "Semantic structure extraction for spreadsheets includes detecting table regions, recognizing structural components and classifying cell types. Automatic semantic structure extraction is key to automatic data transformation from various table structures into canonical schema so as to enable data analysis and knowledge discovery. However, they are challenged by the diverse table structures and the spatial-correlated semantics on cell grids. To learn spatial correlations and capture semantics on spreadsheets, we have developed a novel learning-based framework for spreadsheet semantic structure extraction. First, we propose a multi-task framework that learns table region, structural components and cell types jointly; second, we leverage the advances of the recent language model to capture semantics in each cell value; third, we build a large human-labeled dataset with broad coverage of table structures. Our evaluation shows that our proposed multi-task framework is highly effective that outperforms the results of training each task separately.", "output": [ "We propose a novel multi-task framework that learns table detection, semantic component recognition and cell type classification for spreadsheet tables with promising results." ] }, { "id": "task668-d8b9d85ddfd648bf9870b2758ef2f644", "input": "Motion is an important signal for agents in dynamic environments, but learning to represent motion from unlabeled video is a difficult and underconstrained problem. We propose a model of motion based on elementary group properties of transformations and use it to train a representation of image motion. While most methods of estimating motion are based on pixel-level constraints, we use these group properties to constrain the abstract representation of motion itself. We demonstrate that a deep neural network trained using this method captures motion in both synthetic 2D sequences and real-world sequences of vehicle motion, without requiring any labels. Networks trained to respect these constraints implicitly identify the image characteristic of motion in different sequence types. In the context of vehicle motion, this method extracts information useful for localization, tracking, and odometry. Our results demonstrate that this representation is useful for learning motion in the general setting where explicit labels are difficult to obtain.", "output": [ "We propose of method of using group properties to learn a representation of motion without labels and demonstrate the use of this method for representing 2D and 3D motion.", "Proposes to learn the rigid motion group from a latent representation of image sequences without the need for explicit labels and experimentally demonstrates method on sequences of MINST digits and the KITTI dataset.", "This paper proposes an approach for learning video motion features in an unsupervised manner, using constraints to optimize the neural network to produce features that can be used to regress odometry." ] }, { "id": "task668-b31e8a7a2b2e4c899eb03c72e4459274", "input": "We study the problem of learning permutation invariant representations that can capture containment relations. We propose training a model on a novel task: predicting the size of the symmetric difference between pairs of multisets, sets which may contain multiple copies of the same object. With motivation from fuzzy set theory, we formulate both multiset representations and how to predict symmetric difference sizes given these representations. We model multiset elements as vectors on the standard simplex and multisets as the summations of such vectors, and we predict symmetric difference as the l1-distance between multiset representations. We demonstrate that our representations more effectively predict the sizes of symmetric differences than DeepSets-based approaches with unconstrained object representations. Furthermore, we demonstrate that the model learns meaningful representations, mapping objects of different classes to different standard basis vectors.", "output": [ "Based on fuzzy set theory, we propose a model that given only the sizes of symmetric differences between pairs of multisets, learns representations of such multisets and their elements.", "This paper proposes a new task of set learning, predicting the size of the symmetric difference between multisets, and gives a method to solve the task based on fuzzy set theory." ] }, { "id": "task668-9806c1b5bb8544acac441c5540f5b72d", "input": "We argue that symmetry is an important consideration in addressing the problem\n of systematicity and investigate two forms of symmetry relevant to symbolic processes. \n We implement this approach in terms of convolution and show that it can\n be used to achieve effective generalisation in three toy problems: rule learning,\n composition and grammar learning.", "output": [ "We use convolution to make neural networks behave more like symbolic systems." ] }, { "id": "task668-935656b9c50e432b940008025ef5f682", "input": "It has been argued that the brain is a prediction machine that continuously learns how to make better predictions about the stimuli received from the external environment. For this purpose, it builds a model of the world around us and uses this model to infer the external stimulus. Predictive coding has been proposed as a mechanism through which the brain might be able to build such a model of the external environment. However, it is not clear how predictive coding can be used to build deep neural network models of the brain while complying with the architectural constraints imposed by the brain. In this paper, we describe an algorithm to build a deep generative model using predictive coding that can be used to infer latent representations about the stimuli received from external environment. Specifically, we used predictive coding to train a deep neural network on real-world images in a unsupervised learning paradigm. To understand the capacity of the network with regards to modeling the external environment, we studied the latent representations generated by the model on images of objects that are never presented to the model during training. Despite the novel features of these objects the model is able to infer the latent representations for them. Furthermore, the reconstructions of the original images obtained from these latent representations preserve the important details of these objects.", "output": [ "A predictive coding based learning algorithm for building deep neural network models of the brain", "The paper considers learning of a generative neural network using a predictive coding setup" ] }, { "id": "task668-b03fb0debbb6428e91dc87a2c4e32f8e", "input": "We focus on temporal self-supervision for GAN-based video generation tasks. While adversarial training successfully yields generative models for a variety of areas, temporal relationship in the generated data is much less explored. This is crucial for sequential generation tasks, e.g. video super-resolution and unpaired video translation. For the former, state-of-the-art methods often favor simpler norm losses such as L2 over adversarial training. However, their averaging nature easily leads to temporally smooth results with an undesirable lack of spatial detail. For unpaired video translation, existing approaches modify the generator networks to form spatio-temporal cycle consistencies. In contrast, we focus on improving the learning objectives and propose a temporally self-supervised algorithm. For both tasks, we show that temporal adversarial learning is key to achieving temporally coherent solutions without sacrificing spatial detail. We also propose a novel Ping-Pong loss to improve the long-term temporal consistency. It effectively prevents recurrent networks from accumulating artifacts temporally without depressing detailed features. We also propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution. A series of user studies confirms the rankings computed with these metrics.", "output": [ "We propose temporal self-supervisions for learning stable temporal functions with GANs." ] }, { "id": "task668-4d2c9638fc714da993c28cc0f4b8e8ad", "input": "Recent advances have made it possible to create deep complex-valued neural networks. Despite this progress, many challenging learning tasks have yet to leverage the power of complex representations. Building on recent advances, we propose a new deep complex-valued method for signal retrieval and extraction in the frequency domain. As a case study, we perform audio source separation in the Fourier domain. Our new method takes advantage of the convolution theorem which states that the Fourier transform of two convolved signals is the elementwise product of their Fourier transforms. Our novel method is based on a complex-valued version of Feature-Wise Linear Modulation (FiLM) and serves as the keystone of our proposed signal extraction method. We also introduce a new and explicit amplitude and phase-aware loss, which is scale and time invariant, taking into account the complex-valued components of the spectrogram. Using the Wall Street Journal Dataset, we compared our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.", "output": [ "New Signal Extraction Method in the Fourier Domain" ] }, { "id": "task668-6b5cb855f2554a6592e89e6ea4675b17", "input": "We introduce MultiGrain, a neural network architecture that generates compact image embedding vectors that solve multiple tasks of different granularity: class, instance, and copy recognition. MultiGrain is trained jointly for classification by optimizing the cross-entropy loss and for instance/copy recognition by optimizing a self-supervised ranking loss. The self-supervised loss only uses data augmentation and thus does not require additional labels. Remarkably, the unified embeddings are not only much more compact than using several specialized embeddings, but they also have the same or better accuracy. When fed to a linear classifier, MultiGrain using ResNet-50 achieves 79.4% top-1 accuracy on ImageNet, a +1.8% absolute improvement over the the current state-of-the-art AutoAugment method. The same embeddings perform on par with state-of-the-art instance retrieval with images of moderate resolution. An ablation study shows that our approach benefits from the self-supervision, the pooling method and the mini-batches with repeated augmentations of the same image.\n", "output": [ "Combining classification and image retrieval in a neural network architecture, we obtain an improvement for both tasks.", "This paper proposes a unified embedding for image classification and instance retrieval to enhance the performance for both tasks.", "The paper proposes to jointy train a deep neural net for image classification, instance, and copy recognition." ] }, { "id": "task668-b7691b576c21480d8aea3055cd95a6fd", "input": "The tremendous success of deep neural networks has motivated the need to better understand the fundamental properties of these networks, but many of the theoretical results proposed have only been for shallow networks. In this paper, we study an important primitive for understanding the meaningful input space of a deep network: span recovery. For $k