Datasets:

allenai

/

scitldr

like 21

[ "We propose a novel unsupervised generative model, Elastic-InfoGAN, that learns to disentangle object identity from other low-level aspects in class-imbalanced datasets.", "We first investigate the issues surrounding the assumptions about uniformity made by InfoGAN, and demonstrate its ineffectiveness to properly disentangle object identity in imbalanced data.", "Our key idea is to make the discovery of the discrete latent factor of variation invariant to identity-preserving transformations in real images, and use that as the signal to learn the latent distribution's parameters.", "Experiments on both artificial (MNIST) and real-world (YouTube-Faces) datasets demonstrate the effectiveness of our approach in imbalanced data by:", "(i) better disentanglement of object identity as a latent factor of variation; and", "(ii) better approximation of class imbalance in the data, as reflected in the learned parameters of the latent distribution." ]

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[ 0.15789473056793213, 0.1463414579629898, 0.1818181723356247, 0.2222222238779068, 0.13793103396892548, 0.1875 ]

[ "Elastic-InfoGAN is a modification of InfoGAN that learns, without any supervision, disentangled representations in class imbalanced data" ]

[ "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." ]

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[ 0.1249999925494194, 0.14999999105930328, 0.07407406717538834, 0.3571428656578064, 0.10526315122842789, 0.11764705181121826, 0 ]

[ "a distributed latent-space based knowledge-sharing framework for deep multi-task learning" ]

[ "Board games often rely on visual information such as the location of the game pieces and textual information on cards.", "Due to this reliance on visual feedback, blind players are at a disadvantage because they cannot read the cards or see the location of the game pieces and may be unable to play a game without sighted help.", "We present Game Changer, an augmented workspace that provides both audio descriptions and tactile additions to make the state of the board game accessible to blind and visually impaired players.", "In this paper, we describe the design of Game Changer and present findings from a user study in which 7 blind participants used Game Changer to play against a sighted partner.", "Most players stated the game was more accessible with the additions from Game Changer and felt that Game Changer could be used to augment other games." ]

[ 0, 0, 1, 0, 0 ]

[ 0.1904761791229248, 0.27586206793785095, 0.807692289352417, 0.30188679695129395, 0.4166666567325592 ]

[ "Game Changer is a system that provides both audio descriptions and tactile additions to make the state of the board game accessible to blind and visually impaired players." ]

[ "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." ]

[ 0, 1, 0, 0, 0 ]

[ 0.1764705777168274, 0.34285715222358704, 0.13793103396892548, 0.29999998211860657, 0.19607843458652496 ]

[ "Coupled rule-exemplar supervision and a implication loss helps to jointly learn to denoise rules and imply labels." ]

[ "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." ]

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[ 0.29411762952804565, 0.7317073345184326, 0.3499999940395355, 0.2702702581882477, 0, 0.21276594698429108, 0.3829787075519562 ]

[ "Our method introduces the empowerment-regularized maximum-entropy inverse reinforcement learning to learn near-optimal rewards and policies from expert demonstrations." ]

[ "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" ]

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[ 0.19999998807907104, 0.2222222238779068, 0, 0.1599999964237213, 0.08163265138864517, 0.20338982343673706 ]

[ "RNNs implicitly implement tensor-product representations, a principled and interpretable method for representing symbolic structures in continuous space." ]

[ "We address the problem of teaching an RNN to approximate list-processing algorithms given a small number of input-output training examples.", "Our approach is to generalize the idea of parametricity from programming language theory to formulate a semantic property that distinguishes common algorithms from arbitrary non-algorithmic functions.", "This characterization leads naturally to a learned data augmentation scheme that encourages RNNs to learn algorithmic behavior and enables small-sample learning in a variety of list-processing tasks." ]

[ 0, 0, 1 ]

[ 0.17142856121063232, 0.14999999105930328, 0.2926829159259796 ]

[ "Learned data augmentation instills algorithm-favoring inductive biases that let RNNs learn list-processing algorithms from fewer examples." ]

[ "The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts.", "Previous works of MLL mainly focused on the setting where the concept set is assumed to be fixed, while many real-world applications require introducing new concepts into the set to meet new demands.", "One common need is to refine the original coarse concepts and split them into finer-grained ones, where the refinement process typically begins with limited labeled data for the finer-grained concepts.", "To address the need, we propose a special weakly supervised MLL problem that not only focuses on the situation of limited fine-grained supervision but also leverages the hierarchical relationship between the coarse concepts and the fine-grained ones.", "The problem can be reduced to a multi-label version of negative-unlabeled learning problem using the hierarchical relationship.", "We tackle the reduced problem with a meta-learning approach that learns to assign pseudo-labels to the unlabeled entries.", "Experimental results demonstrate that our proposed method is able to assign accurate pseudo-labels, and in turn achieves superior classification performance when compared with other existing methods." ]

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[ 0.24390242993831635, 0.07999999821186066, 0.1249999925494194, 0.2222222238779068, 0.31578946113586426, 0.5263158082962036, 0.1666666567325592 ]

[ "We propose a special weakly-supervised multi-label learning problem along with a newly tailored algorithm that learns the underlying classifier by learning to assign pseudo-labels." ]

[ "We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent.", "Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities.", "Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming.", "We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent.", "We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user.", "We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes.", "Our experiments show that the agent learns to achieve higher rewards and better states." ]

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[ 0.19512194395065308, 0.1904761791229248, 0.15789473056793213, 0.05128204822540283, 0.24242423474788666, 0.05128204822540283, 0 ]

[ "A Reinforcement Learning based conversational search assistant which provides contextual assistance in subjective search (like digital assets)." ]

[ "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." ]

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[ "Convolutional Neural Networks behave as Compositional Nearest Neighbors!" ]

[ "Consider a world in which events occur that involve various entities.", "Learning how to predict future events from patterns of past events becomes more difficult as we consider more types of events.", "Many of the patterns detected in the dataset by an ordinary LSTM will be spurious since the number of potential pairwise correlations, for example, grows quadratically with the number of events.", "We propose a type of factorial LSTM architecture where different blocks of LSTM cells are responsible for capturing different aspects of the world state.", "We use Datalog rules to specify how to derive the LSTM structure from a database of facts about the entities in the world.", "This is analogous to how a probabilistic relational model (Getoor & Taskar, 2007) specifies a recipe for deriving a graphical model structure from a database.", "In both cases, the goal is to obtain useful inductive biases by encoding informed independence assumptions into the model.", "We specifically consider the neural Hawkes process, which uses an LSTM to modulate the rate of instantaneous events in continuous time.", "In both synthetic and real-world domains, we show that we obtain better generalization by using appropriate factorial designs specified by simple Datalog programs.\n" ]

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[ 0, 0.060606054961681366, 0.09756097197532654, 0.0555555522441864, 0.1666666567325592, 0.05405404791235924, 0.1764705777168274, 0.1111111044883728, 0.2631579041481018 ]

[ "Factorize LSTM states and zero-out/tie LSTM weight matrices according to real-world structural biases expressed by Datalog programs." ]

[ "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." ]

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[ 0.26923075318336487, 0.19999998807907104, 0.04999999329447746, 0.3333333432674408, 0.4680851101875305, 0.35999998450279236, 0.19607841968536377 ]

[ "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." ]

[ "Generating formal-language represented by relational tuples, such as Lisp programs or mathematical expressions, from a natural-language input is an extremely challenging task because it requires to explicitly capture discrete symbolic structural information from the input to generate the output.", "Most state-of-the-art neural sequence models do not explicitly capture such structure information, and thus do not perform well on these tasks.", "In this paper, we propose a new encoder-decoder model based on Tensor Product Representations (TPRs) for Natural- to Formal-language generation, called TP-N2F.", "The encoder of TP-N2F employs TPR 'binding' to encode natural-language symbolic structure in vector space and the decoder uses TPR 'unbinding' to generate a sequence of relational tuples, each consisting of a relation (or operation) and a number of arguments, in symbolic space.", "TP-N2F considerably outperforms LSTM-based Seq2Seq models, creating a new state of the art results on two benchmarks: the MathQA dataset for math problem solving, and the AlgoList dataset for program synthesis.", "Ablation studies show that improvements are mainly attributed to the use of TPRs in both the encoder and decoder to explicitly capture relational structure information for symbolic reasoning." ]

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[ 0.0714285671710968, 0.04999999329447746, 0.9767441749572754, 0.11320754140615463, 0.2083333283662796, 0.08510638028383255 ]

[ "In this paper, we propose a new encoder-decoder model based on Tensor Product Representations for Natural- to Formal-language generation, called TP-N2F." ]

[ "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." ]

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[ 0.7777777910232544, 0.045454539358615875, 0.10810810327529907, 0.060606054961681366 ]

[ "This paper presents a defogger, a model that learns to predict future hidden information from partial observations, applied to a StarCraft dataset." ]

[ "In this work we study generalization of neural networks in gradient-based meta-learning by analyzing various properties of the objective landscapes.", "We experimentally demonstrate that as meta-training progresses, the meta-test solutions obtained by adapting the meta-train solution of the model to new tasks via few steps of gradient-based fine-tuning, become flatter, lower in loss, and further away from the meta-train solution.", "We also show that those meta-test solutions become flatter even as generalization starts to degrade, thus providing an experimental evidence against the correlation between generalization and flat minima in the paradigm of gradient-based meta-leaning.", "Furthermore, we provide empirical evidence that generalization to new tasks is correlated with the coherence between their adaptation trajectories in parameter space, measured by the average cosine similarity between task-specific trajectory directions, starting from a same meta-train solution.", "We also show that coherence of meta-test gradients, measured by the average inner product between the task-specific gradient vectors evaluated at meta-train solution, is also correlated with generalization." ]

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[ 0.7222222089767456, 0.23529411852359772, 0.2448979616165161, 0.15094339847564697, 0.23255813121795654 ]

[ "We study generalization of neural networks in gradient-based meta- learning by analyzing various properties of the objective landscape." ]

[ "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" ]

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[ 0.13636362552642822, 0.1249999925494194, 0.2222222238779068, 0.25641024112701416, 0.1538461446762085, 0 ]

[ "We present a generative model that proves state-of-the-art results on gray-scale and natural images." ]

[ "Plain recurrent networks greatly suffer from the vanishing gradient problem while Gated Neural Networks (GNNs) such as Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) deliver promising results in many sequence learning tasks through sophisticated network designs.", "This paper shows how we can address this problem in a plain recurrent network by analyzing the gating mechanisms in GNNs.", "We propose a novel network called the Recurrent Identity Network (RIN) which allows a plain recurrent network to overcome the vanishing gradient problem while training very deep models without the use of gates.", "We compare this model with IRNNs and LSTMs on multiple sequence modeling benchmarks.", "The RINs demonstrate competitive performance and converge faster in all tasks.", "Notably, small RIN models produce 12%–67% higher accuracy on the Sequential and Permuted MNIST datasets and reach state-of-the-art performance on the bAbI question answering dataset." ]

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[ 0.24242423474788666, 0.2448979616165161, 1, 0.0476190447807312, 0, 0.07843136787414551 ]

[ "We propose a novel network called the Recurrent Identity Network (RIN) which allows a plain recurrent network to overcome the vanishing gradient problem while training very deep models without the use of gates." ]

[ "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." ]

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[ 0.07692307233810425, 0.10526315122842789, 0.2631579041481018, 0.13333332538604736, 0.054054051637649536, 0 ]

[ "The paper tackles fault-tolerance under random and adversarial stoppages." ]

[ "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." ]

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[ 0.5454545617103577, 0.27272728085517883, 0.3255814015865326, 0.2790697515010834, 0.10526315122842789, 0.11999999731779099, 0 ]

[ "We present a novel unified architecture that restores video frames from a single motion-blurred image in an end-to-end manner." ]

[ "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." ]

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[ "We propose a novel structured class-blind pruning technique to produce highly compressed neural networks." ]

[ "The recent success of neural networks for solving difficult decision tasks has incentivized incorporating smart decision making \"at the edge.\"", "However, this work has traditionally focused on neural network inference, rather than training, due to memory and compute limitations, especially in emerging non-volatile memory systems, where writes are energetically costly and reduce lifespan.", "Yet, the ability to train at the edge is becoming increasingly important as it enables applications such as real-time adaptability to device drift and environmental variation, user customization, and federated learning across devices.", "In this work, we address four key challenges for training on edge devices with non-volatile memory: low weight update density, weight quantization, low auxiliary memory, and online learning.", "We present a low-rank training scheme that addresses these four challenges while maintaining computational efficiency.", "We then demonstrate the technique on a representative convolutional neural network across several adaptation problems, where it out-performs standard SGD both in accuracy and in number of weight updates." ]

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[ 0.1904761791229248, 0.22641508281230927, 0.11764705181121826, 0.2916666567325592, 0.2702702581882477, 0.1599999964237213 ]

[ "We use Kronecker sum approximations for low-rank training to address challenges in training neural networks on edge devices that utilize emerging memory technologies." ]

[ "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." ]

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[ 0.10256409645080566, 0.052631575614213943, 0.3214285671710968, 0.31372547149658203, 0.12244897335767746, 0.23255813121795654, 0.19512194395065308 ]

[ "Systematically examines how well we can explain the hidden features of a deep network in terms of logical rules." ]

[ "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." ]

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[ 0, 0.27586206793785095, 0.1666666567325592, 0.07407406717538834, 0.1599999964237213 ]

[ "Improved likelihood estimates in variational autoencoders using self-supervised feature learning" ]

[ "Direct policy gradient methods for reinforcement learning and continuous control problems are a popular\napproach for a variety of reasons: \n1) they are easy to implement without explicit knowledge of the underlying model;\n2) they are an \"end-to-end\" approach, directly optimizing the performance metric of interest;\n3) they inherently allow for richly parameterized policies.\n", "A notable drawback is that even in the most basic continuous control problem (that of linear quadratic regulators), these methods must solve a non-convex optimization problem, where little is understood about their efficiency from both computational and statistical perspectives.", "In contrast, system identification and model based planning in optimal control theory have a much more solid theoretical footing, where much is known with regards to their computational and statistical properties. ", "This work bridges this gap showing that (model free) policy gradient methods globally converge to the optimal solution and are efficient (polynomially so in relevant problem dependent quantities) with regards to their sample and computational complexities." ]

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[ 0.2461538463830948, 0.17241378128528595, 0.11999999731779099, 0.37037035822868347 ]

[ "This paper shows that model-free policy gradient methods can converge to the global optimal solution for non-convex linearized control problems." ]

[ "Low-dimensional vector embeddings, computed using LSTMs or simpler techniques, are a popular approach for capturing the “meaning” of text and a form of unsupervised learning useful for downstream tasks.", "However, their power is not theoretically understood.", "The current paper derives formal understanding by looking at the subcase of linear embedding schemes.", "Using the theory of compressed sensing we show that representations combining the constituent word vectors are essentially information-preserving linear measurements of Bag-of-n-Grams (BonG) representations of text.", "This leads to a new theoretical result about LSTMs: low-dimensional embeddings derived from a low-memory LSTM are provably at least as powerful on classification tasks, up to small error, as a linear classifier over BonG vectors, a result that extensive empirical work has thus far been unable to show.", "Our experiments support these theoretical findings and establish strong, simple, and unsupervised baselines on standard benchmarks that in some cases are state of the art among word-level methods.", "We also show a surprising new property of embeddings such as GloVe and word2vec: they form a good sensing matrix for text that is more efficient than random matrices, the standard sparse recovery tool, which may explain why they lead to better representations in practice." ]

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[ 0.1666666567325592, 0, 0.21621620655059814, 0.40909090638160706, 0.25, 0.16326530277729034, 0.2461538463830948 ]

[ "We use the theory of compressed sensing to prove that LSTMs can do at least as well on linear text classification as Bag-of-n-Grams." ]

[ " Some conventional transforms such as Discrete Walsh-Hadamard Transform (DWHT) and Discrete Cosine Transform (DCT) have been widely used as feature extractors in image processing but rarely applied in neural networks.", "However, we found that these conventional transforms have the ability to capture the cross-channel correlations without any learnable parameters in DNNs.", "This paper firstly proposes to apply conventional transforms on pointwise convolution, showing that such transforms significantly reduce the computational complexity of neural networks without accuracy performance degradation.", "Especially for DWHT, it requires no floating point multiplications but only additions and subtractions, which can considerably reduce computation overheads.", "In addition, its fast algorithm further reduces complexity of floating point addition from O(n^2) to O(nlog n).", "These non-parametric and low computational properties construct extremely efficient networks in the number parameters and operations, enjoying accuracy gain.", "Our proposed DWHT-based model gained 1.49% accuracy increase with 79.4% reduced parameters and 48.4% reduced FLOPs compared with its baseline model (MoblieNet-V1) on the CIFAR 100 dataset." ]

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[ 0.1904761791229248, 0.1666666567325592, 0.1904761791229248, 0, 0.060606054961681366, 0.11764705181121826, 0.0476190410554409 ]

[ "We introduce new pointwise convolution layers equipped with extremely fast conventional transforms in deep neural network." ]

[ "We introduce the notion of \\emph{lattice representation learning}, in which the representation for some object of interest (e.g. a sentence or an image) is a lattice point in an Euclidean space.", "Our main contribution is a result for replacing an objective function which employs lattice quantization with an objective function in which quantization is absent, thus allowing optimization techniques based on gradient descent to apply; we call the resulting algorithms \\emph{dithered stochastic gradient descent} algorithms as they are designed explicitly to allow for an optimization procedure where only local information is employed.", "We also argue that a technique commonly used in Variational Auto-Encoders (Gaussian priors and Gaussian approximate posteriors) is tightly connected with the idea of lattice representations, as the quantization error in good high dimensional lattices can be modeled as a Gaussian distribution.", "We use a traditional encoder/decoder architecture to explore the idea of latticed valued representations, and provide experimental evidence of the potential of using lattice representations by modifying the \\texttt{OpenNMT-py} generic \\texttt{seq2seq} architecture so that it can implement not only Gaussian dithering of representations, but also the well known straight-through estimator and its application to vector quantization. \n" ]

[ 0, 0, 0, 1 ]

[ 0.09090908616781235, 0.12121211737394333, 0.1818181723356247, 0.1846153736114502 ]

[ "We propose to use lattices to represent objects and prove a fundamental result on how to train networks that use them." ]

[ "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." ]

[ 0, 0, 1, 0, 0, 0, 0 ]

[ 0.27586206793785095, 0.24242423474788666, 0.2857142686843872, 0.13333332538604736, 0.13636362552642822, 0.06896550953388214, 0.04999999701976776 ]

[ "We try to design and train a classifier whose adversarial robustness is more resemblance to robustness of human." ]

[ "Generating complex discrete distributions remains as one of the challenging problems in machine learning.", "Existing techniques for generating complex distributions with high degrees of freedom depend on standard generative models like Generative Adversarial Networks (GAN), Wasserstein GAN, and associated variations.", "Such models are based on an optimization involving the distance between two continuous distributions.", "We introduce a Discrete Wasserstein GAN (DWGAN) model which is based on a dual formulation of the Wasserstein distance between two discrete distributions.", "We derive a novel training algorithm and corresponding network architecture based on the formulation.", "Experimental results are provided for both synthetic discrete data, and real discretized data from MNIST handwritten digits." ]

[ 0, 0, 0, 1, 0, 0 ]

[ 0.22857142984867096, 0.1702127605676651, 0.4000000059604645, 0.9523809552192688, 0.34285715222358704, 0.052631575614213943 ]

[ "We propose a Discrete Wasserstein GAN (DWGAN) model which is based on a dual formulation of the Wasserstein distance between two discrete distributions." ]

[ "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." ]

[ 1, 0, 0, 0, 0, 0 ]

[ 0.2916666567325592, 0.13333332538604736, 0.178571417927742, 0.14999999105930328, 0.2666666507720947, 0.07999999821186066 ]

[ "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. " ]

[ "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." ]

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[ 0.1764705777168274, 0.178571417927742, 0.1395348757505417, 0.23255813121795654, 0.0624999962747097, 0.23999999463558197 ]

[ "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." ]

[ "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." ]

[ 1, 0, 0, 0 ]

[ 0.6000000238418579, 0.2380952388048172, 0.1860465109348297, 0.3255814015865326 ]

[ "We present a novel algorithm for solving reinforcement learning and bandit structured prediction problems with very sparse loss feedback." ]

[ "Adversarial neural networks solve many important problems in data science, but are notoriously difficult to train.", "These difficulties come from the fact that optimal weights for adversarial nets correspond to saddle points, and not minimizers, of the loss function.", "The alternating stochastic gradient methods typically used for such problems do not reliably converge to saddle points, and when convergence does happen it is often highly sensitive to learning rates.", "We propose a simple modification of stochastic gradient descent that stabilizes adversarial networks.", "We show, both in theory and practice, that the proposed method reliably converges to saddle points.", "This makes adversarial networks less likely to \"collapse,\" and enables faster training with larger learning rates." ]

[ 0, 0, 0, 1, 0, 0 ]

[ 0.11428570747375488, 0.24390242993831635, 0.0833333283662796, 0.5, 0.22857142984867096, 0.17142856121063232 ]

[ "We present a simple modification to the alternating SGD method, called a prediction step, that improves the stability of adversarial networks." ]

[ "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." ]

[ 0, 0, 0, 1, 0, 0 ]

[ 0.11764705181121826, 0.158730149269104, 0.17391303181648254, 0.8695651888847351, 0.13333332538604736, 0.1764705777168274 ]

[ "This paper introduces domain-independent compilations of user questions into constraints for contrastive explanations." ]

[ "Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks.", "We propose Graph2Gauss - an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification.", "Unlike most approaches that represent nodes as point vectors in a low-dimensional continuous space, we embed each node as a Gaussian distribution, allowing us to capture uncertainty about the representation.", "Furthermore, we propose an unsupervised method that handles inductive learning scenarios and is applicable to different types of graphs: plain/attributed, directed/undirected.", "By leveraging both the network structure and the associated node attributes, we are able to generalize to unseen nodes without additional training.", "To learn the embeddings we adopt a personalized ranking formulation w.r.t. the node distances that exploits the natural ordering of the nodes imposed by the network structure.", "Experiments on real world networks demonstrate the high performance of our approach, outperforming state-of-the-art network embedding methods on several different tasks.", "Additionally, we demonstrate the benefits of modeling uncertainty - by analyzing it we can estimate neighborhood diversity and detect the intrinsic latent dimensionality of a graph." ]

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[ 0.21052631735801697, 0.08888888359069824, 0.5777777433395386, 0.052631575614213943, 0.10810810327529907, 0.0952380895614624, 0, 0.14999999105930328 ]

[ " We embed nodes in a graph as Gaussian distributions allowing us to capture uncertainty about their representation." ]

[ "While great progress has been made at making neural networks effective across a wide range of tasks, many are surprisingly vulnerable to small, carefully chosen perturbations of their input, known as adversarial examples.", "In this paper, we advocate for and experimentally investigate the use of logit regularization techniques as an adversarial defense, which can be used in conjunction with other methods for creating adversarial robustness at little to no cost.", "We demonstrate that much of the effectiveness of one recent adversarial defense mechanism can be attributed to logit regularization and show how to improve its defense against both white-box and black-box attacks, in the process creating a stronger black-box attacks against PGD-based models.\n" ]

[ 0, 1, 0 ]

[ 0.08888888359069824, 0.25, 0.23999999463558197 ]

[ "Logit regularization methods help explain and improve state of the art adversarial defenses" ]

[ "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" ]

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[ "We describe a modular and composable language for describing expressive search spaces over architectures and simple model search algorithms applied to these search spaces. " ]

[ "Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection.", "The performant systems, however, typically involve big models with numerous parameters.", "Once trained, a challenging aspect for such top performing models is deployment on resource constrained inference systems -- the models (often deep networks or wide networks or both) are compute and memory intensive.", "Low precision numerics and model compression using knowledge distillation are popular techniques to lower both the compute requirements and memory footprint of these deployed models.", "In this paper, we study the combination of these two techniques and show that the performance of low precision networks can be significantly improved by using knowledge distillation techniques.", "We call our approach Apprentice and show state-of-the-art accuracies using ternary precision and 4-bit precision for many variants of ResNet architecture on ImageNet dataset.", "We study three schemes in which one can apply knowledge distillation techniques to various stages of the train-and-deploy pipeline." ]

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[ 0.1538461446762085, 0, 0.1538461446762085, 0.260869562625885, 0.4583333432674408, 0.3636363446712494, 0.2926829159259796 ]

[ "We show that knowledge transfer techniques can improve the accuracy of low precision networks and set new state-of-the-art accuracy for ternary and 4-bits precision. " ]

[ "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." ]

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[ "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" ]

[ "Clustering is a fundamental machine learning method.", "The quality of its results is dependent on the data distribution.", "For this reason, deep neural networks can be used for learning better representations of the data.", "In this paper, we propose a systematic taxonomy for clustering with deep learning, in addition to a review of methods from the field.", "Based on our taxonomy, creating new methods is more straightforward.", "We also propose a new approach which is built on the taxonomy and surpasses some of the limitations of some previous work.", "Our experimental evaluation on image datasets shows that the method approaches state-of-the-art clustering quality, and performs better in some cases." ]

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[ 0, 0, 0.23999999463558197, 0.19354838132858276, 0, 0, 0.06896550953388214 ]

[ "Unifying framework to perform clustering using deep neural networks" ]

[ "Generative models often use human evaluations to determine and justify progress.", "Unfortunately, existing human evaluation methods are ad-hoc: there is currently no standardized, validated evaluation that: (1) measures perceptual fidelity, (2) is reliable, (3) separates models into clear rank order, and (4) ensures high-quality measurement without intractable cost.", "In response, we construct Human-eYe Perceptual Evaluation (HYPE), a human metric that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) results in separable model performances, and (4) efficient in cost and time.", "We introduce two methods.", "The first, HYPE-Time, measures visual perception under adaptive time constraints to determine the minimum length of time (e.g., 250ms) that model output such as a generated face needs to be visible for people to distinguish it as real or fake.", "The second, HYPE-Infinity, measures human error rate on fake and real images with no time constraints, maintaining stability and drastically reducing time and cost.", "We test HYPE across four state-of-the-art generative adversarial networks (GANs) on unconditional image generation using two datasets, the popular CelebA and the newer higher-resolution FFHQ, and two sampling techniques of model outputs.", "By simulating HYPE's evaluation multiple times, we demonstrate consistent ranking of different models, identifying StyleGAN with truncation trick sampling (27.6% HYPE-Infinity deception rate, with roughly one quarter of images being misclassified by humans) as superior to StyleGAN without truncation (19.0%) on FFHQ." ]

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[ 0.06896550953388214, 0.11320754140615463, 0.20689654350280762, 0, 0.1071428507566452, 0.10256409645080566, 0.1702127605676651, 0.10344827175140381 ]

[ "HYPE is a reliable human evaluation metric for scoring generative models, starting with human face generation across 4 GANs." ]

[ "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." ]

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[ 0.2916666567325592, 0.08695651590824127, 0.1599999964237213, 0.3265306055545807, 0.16326530277729034, 0.29032257199287415 ]

[ "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." ]

[ "We derive a new intrinsic social motivation for multi-agent reinforcement learning (MARL), in which agents are rewarded for having causal influence over another agent's actions, where causal influence is assessed using counterfactual reasoning.", "The reward does not depend on observing another agent's reward function, and is thus a more realistic approach to MARL than taken in previous work.", "We show that the causal influence reward is related to maximizing the mutual information between agents' actions.", "We test the approach in challenging social dilemma environments, where it consistently leads to enhanced cooperation between agents and higher collective reward.", "Moreover, we find that rewarding influence can lead agents to develop emergent communication protocols.", "Therefore, we also employ influence to train agents to use an explicit communication channel, and find that it leads to more effective communication and higher collective reward.", "Finally, we show that influence can be computed by equipping each agent with an internal model that predicts the actions of other agents.", "This allows the social influence reward to be computed without the use of a centralised controller, and as such represents a significantly more general and scalable inductive bias for MARL with independent agents." ]

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[ 0.23728813230991364, 0.22641508281230927, 0.4000000059604645, 0.2745097875595093, 0.3255814015865326, 0.307692289352417, 0.31372547149658203, 0.33898305892944336 ]

[ "We reward agents for having a causal influence on the actions of other agents, and show that this gives rise to better cooperation and more meaningful emergent communication protocols. " ]

[ "This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting.", "We combine this within a distributed framework for off-policy learning in order to develop what we call the Distributed Distributional Deep Deterministic Policy Gradient algorithm, D4PG.", "We also combine this technique with a number of additional, simple improvements such as the use of N-step returns and prioritized experience replay.", "Experimentally we examine the contribution of each of these individual components, and show how they interact, as well as their combined contributions.", "Our results show that across a wide variety of simple control tasks, difficult manipulation tasks, and a set of hard obstacle-based locomotion tasks the D4PG algorithm achieves state of the art performance." ]

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[ 0.17777776718139648, 0.4528301954269409, 0.20408162474632263, 0.12765957415103912, 0.3333333134651184 ]

[ "We develop an agent that we call the Distributional Deterministic Deep Policy Gradient algorithm, which achieves state of the art performance on a number of challenging continuous control problems." ]

[ "State-action value functions (i.e., Q-values) are ubiquitous in reinforcement learning (RL), giving rise to popular algorithms such as SARSA and Q-learning.", "We propose a new notion of action value defined by a Gaussian smoothed version of the expected Q-value used in SARSA.", "We show that such smoothed Q-values still satisfy a Bellman equation, making them naturally learnable from experience sampled from an environment.", "Moreover, the gradients of expected reward with respect to the mean and covariance of a parameterized Gaussian policy can be recovered from the gradient and Hessian of the smoothed Q-value function.", "Based on these relationships we develop new algorithms for training a Gaussian policy directly from a learned Q-value approximator.", "The approach is also amenable to proximal optimization techniques by augmenting the objective with a penalty on KL-divergence from a previous policy.", "We find that the ability to learn both a mean and covariance during training allows this approach to achieve strong results on standard continuous control benchmarks." ]

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[ 0.0555555522441864, 0.4375, 0.1818181723356247, 0.2631579041481018, 0.25806450843811035, 0.05882352590560913, 0.15789473056793213 ]

[ "We propose a new Q-value function that enables better learning of Gaussian policies." ]

[ "Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language.", "They are ideal environments for studying how to extend reinforcement learning agents to meet the challenges of natural language understanding, partial observability, and action generation in combinatorially-large text-based action spaces.", "We present KG-A2C, an agent that builds a dynamic knowledge graph while exploring and generates actions using a template-based action space.", "We contend that the dual uses of the knowledge graph to reason about game state and to constrain natural language generation are the keys to scalable exploration of combinatorially large natural language actions.", "Results across a wide variety of IF games show that KG-A2C outperforms current IF agents despite the exponential increase in action space size." ]

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[ 0.19607841968536377, 0.2950819730758667, 0.6792452931404114, 0.2711864411830902, 0.3272727131843567 ]

[ "We present KG-A2C, a reinforcement learning agent that builds a dynamic knowledge graph while exploring and generates natural language using a template-based action space - outperforming all current agents on a wide set of text-based games." ]

[ "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." ]

[ 1, 0, 0 ]

[ 0.3589743673801422, 0.23728813230991364, 0.11538460850715637 ]

[ "We prove that deep neural networks are exponentially more efficient than shallow ones at approximating sparse multivariate polynomials." ]

[ "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." ]

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[ 0.13793103396892548, 0.21052631735801697, 0.1071428507566452, 0.21917808055877686, 0.1599999964237213, 0.08163265138864517 ]

[ "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." ]

[ "This work presents a modular and hierarchical approach to learn policies for exploring 3D environments.", "Our approach leverages the strengths of both classical and learning-based methods, by using analytical path planners with learned mappers, and global and local policies.", "Use of learning provides flexibility with respect to input modalities (in mapper), leverages structural regularities of the world (in global policies), and provides robustness to errors in state estimation (in local policies).", "Such use of learning within each module retains its benefits, while at the same time, hierarchical decomposition and modular training allow us to sidestep the high sample complexities associated with training end-to-end policies.", "Our experiments in visually and physically realistic simulated 3D environments demonstrate the effectiveness of our proposed approach over past learning and geometry-based approaches." ]

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[ 0.8148148059844971, 0.1764705777168274, 0.10256409645080566, 0.23255813121795654, 0.23529411852359772 ]

[ "A modular and hierarchical approach to learn policies for exploring 3D environments." ]

[ "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." ]

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[ 0.06451612710952759, 0.1111111044883728, 0.19999998807907104, 0.17142856121063232, 0.14814814925193787, 0.1395348757505417, 0.2666666507720947 ]

[ "An unsupervised sim-to-real domain adaptation method for semantic segmentation using privileged information from a simulator with GAN-based image translation." ]

[ "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" ]

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[ 0.2857142686843872, 0.3636363446712494, 0.11764705181121826, 0.1538461446762085, 0.1111111044883728, 0.0714285671710968, 0.1666666567325592, 0.04999999701976776, 0.0714285671710968, 0.1111111044883728, 0.1599999964237213, 0.1395348757505417, 0 ]

[ "The first rigor diagnose of large-scale adversarial training on ImageNet" ]

[ "The gradient of a deep neural network (DNN) w.r.t. the input provides information that can be used to explain the output prediction in terms of the input features and has been widely studied to assist in interpreting DNNs.", " In a linear model (i.e., $g(x)=wx+b$), the gradient corresponds solely to the weights $w$.", "Such a model can reasonably locally linearly approximate a smooth nonlinear DNN, and hence the weights of this local model are the gradient.", "The other part, however, of a local linear model, i.e., the bias $b$, is usually overlooked in attribution methods since it is not part of the gradient.", "In this paper, we observe that since the bias in a DNN also has a non-negligible contribution to the correctness of predictions, it can also play a significant role in understanding DNN behaviors.", "In particular, we study how to attribute a DNN's bias to its input features.", "We propose a backpropagation-type algorithm ``bias back-propagation (BBp)'' that starts at the output layer and iteratively attributes the bias of each layer to its input nodes as well as combining the resulting bias term of the previous layer.", "This process stops at the input layer, where summing up the attributions over all the input features exactly recovers $b$.", "Together with the backpropagation of the gradient generating $w$, we can fully recover the locally linear model $g(x)=wx+b$.", "Hence, the attribution of the DNN outputs to its inputs is decomposed into two parts, the gradient $w$ and the bias attribution, providing separate and complementary explanations.", "We study several possible attribution methods applied to the bias of each layer in BBp.", "In experiments, we show that BBp can generate complementary and highly interpretable explanations of DNNs in addition to gradient-based attributions." ]

[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 ]

[ 0.4000000059604645, 0.1463414579629898, 0.17391303181648254, 0.19230768084526062, 0.22641508281230927, 0.25641024112701416, 0.2857142686843872, 0.1860465109348297, 0.0952380895614624, 0.2857142686843872, 0.24390242993831635, 0.52173912525177 ]

[ "Attribute the bias terms of deep neural networks to input features by a backpropagation-type algorithm; Generate complementary and highly interpretable explanations of DNNs in addition to gradient-based attributions." ]

[ "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." ]

[ 1, 0, 0, 0, 0 ]

[ 0.625, 0.1463414579629898, 0.15789473056793213, 0.21052631735801697, 0.060606054961681366 ]

[ "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)" ]

[ "We present an adversarial exploration strategy, a simple yet effective imitation learning scheme that incentivizes exploration of an environment without any extrinsic reward or human demonstration.", "Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each other.", "The former collects training samples for the latter, and its objective is to maximize the error of the latter.", "The latter is trained with samples collected by the former, and generates rewards for the former when it fails to predict the actual action taken by the former.", "In such a competitive setting, the DRL agent learns to generate samples that the inverse dynamics model fails to predict correctly, and the inverse dynamics model learns to adapt to the challenging samples.", "We further propose a reward structure that ensures the DRL agent collects only moderately hard samples and not overly hard ones that prevent the inverse model from imitating effectively.", "We evaluate the effectiveness of our method on several OpenAI gym robotic arm and hand manipulation tasks against a number of baseline models.", "Experimental results show that our method is comparable to that directly trained with expert demonstrations, and superior to the other baselines even without any human priors." ]

[ 1, 0, 0, 0, 0, 0, 0, 0 ]

[ 0.8636363744735718, 0.1538461446762085, 0.05405404791235924, 0, 0.0476190410554409, 0.08695651590824127, 0.0476190410554409, 0.1818181723356247 ]

[ "A simple yet effective imitation learning scheme that incentivizes exploration of an environment without any extrinsic reward or human demonstration." ]

[ "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." ]

[ 1, 0, 0, 0, 0, 0, 0, 0, 0 ]

[ 0.555555522441864, 0.10810810327529907, 0.43478259444236755, 0.2142857164144516, 0.05882352590560913, 0.29411762952804565, 0.31111109256744385, 0.23255813121795654, 0.25641024112701416 ]

[ " a new framework using dual space for generating images corresponding to multiclass labels when the number of class is large" ]

[ "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." ]

[ 0, 0, 0, 0, 1, 0 ]

[ 0.20408162474632263, 0.1666666567325592, 0.1702127605676651, 0.11538460850715637, 0.36734694242477417, 0.317460298538208 ]

[ "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." ]

[ "OCR is inevitably linked to NLP since its final output is in text.", "Advances in document intelligence are driving the need for a unified technology that integrates OCR with various NLP tasks, especially semantic parsing.", "Since OCR and semantic parsing have been studied as separate tasks so far, the datasets for each task on their own are rich, while those for the integrated post-OCR parsing tasks are relatively insufficient.", "In this study, we publish a consolidated dataset for receipt parsing as the first step towards post-OCR parsing tasks.", "The dataset consists of thousands of Indonesian receipts, which contains images and box/text annotations for OCR, and multi-level semantic labels for parsing.", "The proposed dataset can be used to address various OCR and parsing tasks." ]

[ 0, 0, 0, 1, 0, 0 ]

[ 0, 0.1875, 0.20512820780277252, 0.5, 0.20689654350280762, 0.260869562625885 ]

[ "We introduce a large-scale receipt dataset for post-OCR parsing tasks." ]

[ "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." ]

[ 0, 0, 0, 0, 0, 0, 0, 1 ]

[ 0.1621621549129486, 0.0714285671710968, 0.0952380895614624, 0.1666666567325592, 0.16129031777381897, 0, 0.1538461446762085, 0.1860465109348297 ]

[ "Accelerating CNN training on a Pipeline of Accelerators with Stale Weights" ]

[ "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." ]

[ 0, 0, 0, 1, 0, 0, 0, 0, 0 ]

[ 0.1304347813129425, 0.11999999731779099, 0.045454543083906174, 0.9166666865348816, 0.08888888359069824, 0.039215680211782455, 0.1249999925494194, 0.0714285671710968, 0.04255318641662598 ]

[ "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." ]

[ "Flow-based generative models are powerful exact likelihood models with efficient sampling and inference. \n", "Despite their computational efficiency, flow-based models generally have much worse density modeling performance compared to state-of-the-art autoregressive models.", "In this paper, we investigate and improve upon three limiting design choices employed by flow-based models in prior work: the use of uniform noise for dequantization, the use of inexpressive affine flows, and the use of purely convolutional conditioning networks in coupling layers.", "Based on our findings, we propose Flow++, a new flow-based model that is now the state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks.", "Our work has begun to close the significant performance gap that has so far existed between autoregressive models and flow-based models." ]

[ 0, 0, 0, 1, 0 ]

[ 0.2222222238779068, 0.19354838132858276, 0.16326530277729034, 0.2631579041481018, 0.1818181723356247 ]

[ "Improved training of current flow-based generative models (Glow and RealNVP) on density estimation benchmarks" ]

[ "Modern deep artificial neural networks have achieved impressive results through models with orders of magnitude more parameters than training examples which control overfitting with the help of regularization.", "Regularization can be implicit, as is the case of stochastic gradient descent and parameter sharing in convolutional layers, or explicit.", "Explicit regularization techniques, most common forms are weight decay and dropout, have proven successful in terms of improved generalization, but they blindly reduce the effective capacity of the model, introduce sensitive hyper-parameters and require deeper and wider architectures to compensate for the reduced capacity.", "In contrast, data augmentation techniques exploit domain knowledge to increase the number of training examples and improve generalization without reducing the effective capacity and without introducing model-dependent parameters, since it is applied on the training data.", "In this paper we systematically contrast data augmentation and explicit regularization on three popular architectures and three data sets.", "Our results demonstrate that data augmentation alone can achieve the same performance or higher as regularized models and exhibits much higher adaptability to changes in the architecture and the amount of training data." ]

[ 0, 0, 0, 0, 0, 1 ]

[ 0.24561403691768646, 0.23529411852359772, 0.260869562625885, 0.2295081913471222, 0.21276594698429108, 0.4067796468734741 ]

[ "Deep neural networks trained with data augmentation do not require any other explicit regularization (such as weight decay and dropout) and exhibit greater adaptaibility to changes in the architecture and the amount of training data." ]

[ "Adversarial feature learning (AFL) is one of the promising ways for explicitly constrains neural networks to learn desired representations; for example, AFL could help to learn anonymized representations so as to avoid privacy issues.", "AFL learn such a representations by training the networks to deceive the adversary that predict the sensitive information from the network, and therefore, the success of the AFL heavily relies on the choice of the adversary.", "This paper proposes a novel design of the adversary, {\\em multiple adversaries over random subspaces} (MARS) that instantiate the concept of the {\\em volunerableness}.", "The proposed method is motivated by an assumption that deceiving an adversary could fail to give meaningful information if the adversary is easily fooled, and adversary rely on single classifier suffer from this issues. \n", "In contrast, the proposed method is designed to be less vulnerable, by utilizing the ensemble of independent classifiers where each classifier tries to predict sensitive variables from a different {\\em subset} of the representations. \n", "The empirical validations on three user-anonymization tasks show that our proposed method achieves state-of-the-art performances in all three datasets without significantly harming the utility of data. \n", "This is significant because it gives new implications about designing the adversary, which is important to improve the performance of AFL." ]

[ 0, 0, 1, 0, 0, 0, 0 ]

[ 0.25, 0.19230768084526062, 0.260869562625885, 0.21052631735801697, 0.24561403691768646, 0.1538461446762085, 0.17777776718139648 ]

[ "This paper improves the quality of the recently proposed adversarial feature leaning (AFL) approach for incorporating explicit constrains to representations, by introducing the concept of the {\\em vulnerableness} of the adversary. " ]

[ "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." ]

[ 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 ]

[ 0.2380952388048172, 0.12244897335767746, 0.21739129722118378, 0.2857142686843872, 0.3478260934352875, 0.04878048226237297, 0.19999998807907104, 0.14999999105930328, 0.1090909019112587, 0.2222222238779068 ]

[ "We extend a successful recurrent variational autoencoder for dynamic systems to model an instance of dynamic systems hierarchy in neuroscience using the ladder method." ]

[ "With the proliferation of specialized neural network processors that operate on low-precision integers, the performance of Deep Neural Network inference becomes increasingly dependent on the result of quantization.", "Despite plenty of prior work on the quantization of weights or activations for neural networks, there is still a wide gap between the software quantizers and the low-precision accelerator implementation, which degrades either the efficiency of networks or that of the hardware for the lack of software and hardware coordination at design-phase.", "In this paper, we propose a learned linear symmetric quantizer for integer neural network processors, which not only quantizes neural parameters and activations to low-bit integer but also accelerates hardware inference by using batch normalization fusion and low-precision accumulators (e.g., 16-bit) and multipliers (e.g., 4-bit).", "We use a unified way to quantize weights and activations, and the results outperform many previous approaches for various networks such as AlexNet, ResNet, and lightweight models like MobileNet while keeping friendly to the accelerator architecture.", "Additional, we also apply the method to object detection models and witness high performance and accuracy in YOLO-v2.", "Finally, we deploy the quantized models on our specialized integer-arithmetic-only DNN accelerator to show the effectiveness of the proposed quantizer.", "We show that even with linear symmetric quantization, the results can be better than asymmetric or non-linear methods in 4-bit networks.", "In evaluation, the proposed quantizer induces less than 0.4\\% accuracy drop in ResNet18, ResNet34, and AlexNet when quantizing the whole network as required by the integer processors." ]

[ 1, 0, 0, 0, 0, 0, 0, 0 ]

[ 0.3499999940395355, 0.2181818187236786, 0.10169491171836853, 0.12244897335767746, 0.05882352590560913, 0.17142856121063232, 0.10526315122842789, 0.04651162400841713 ]

[ "We introduce an efficient quantization process that allows for performance acceleration on specialized integer-only neural network accelerator." ]

[ "The prohibitive energy cost of running high-performance Convolutional Neural Networks (CNNs) has been limiting their deployment on resource-constrained platforms including mobile and wearable devices.", "We propose a CNN for energy-aware dynamic routing, called the EnergyNet, that achieves adaptive-complexity inference based on the inputs, leading to an overall reduction of run time energy cost without noticeably losing (or even improving) accuracy.", "That is achieved by proposing an energy loss that captures both computational and data movement costs.", "We combine it with the accuracy-oriented loss, and learn a dynamic routing policy for skipping certain layers in the networks, that optimizes the hybrid loss. ", "Our empirical results demonstrate that, compared to the baseline CNNs, EnergyNetcan trim down the energy cost up to 40% and 65%, during inference on the CIFAR10 and Tiny ImageNet testing sets, respectively, while maintaining the same testing accuracies. ", "It is further encouraging to observe that the energy awareness might serve as a training regularization and can even improve prediction accuracy: our models can achieve 0.7% higher top-1 testing accuracy than the baseline on CIFAR-10 when saving up to 27% energy, and 1.0% higher top-5 testing accuracy on Tiny ImageNet when saving up to 50% energy, respectively." ]

[ 0, 1, 0, 0, 0, 0 ]

[ 0.09090908616781235, 0.290909081697464, 0.1111111044883728, 0.22727271914482117, 0.15094339847564697, 0.11940298229455948 ]

[ "This paper proposes a new CNN model that combines energy cost with a dynamic routing strategy to enable adaptive energy-efficient inference." ]

[ "Log-linear models models are widely used in machine learning, and in particular are ubiquitous in deep learning architectures in the form of the softmax.", "While exact inference and learning of these requires linear time, it can be done approximately in sub-linear time with strong concentrations guarantees.", "In this work, we present LSH Softmax, a method to perform sub-linear learning and inference of the softmax layer in the deep learning setting.", "Our method relies on the popular Locality-Sensitive Hashing to build a well-concentrated gradient estimator, using nearest neighbors and uniform samples.", "We also present an inference scheme in sub-linear time for LSH Softmax using the Gumbel distribution.", "On language modeling, we show that Recurrent Neural Networks trained with LSH Softmax perform on-par with computing the exact softmax while requiring sub-linear computations." ]

[ 0, 0, 1, 0, 0, 0 ]

[ 0.12765957415103912, 0.23529411852359772, 0.4313725531101227, 0.12244897335767746, 0.2222222238779068, 0.23076923191547394 ]

[ "we present LSH Softmax, a softmax approximation layer for sub-linear learning and inference with strong theoretical guarantees; we showcase both its applicability and efficiency by evaluating on a real-world task: language modeling." ]

[ "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." ]

[ 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0 ]

[ 0.06451612710952759, 0.05405404791235924, 0.0624999962747097, 0, 0.06896550953388214, 0.07999999821186066, 0.06896550953388214, 0, 0.1875, 0, 0.04255318641662598 ]

[ "RL can solve (stochastic) multi-robot/scheduling problems scalably and transferably using graph embedding" ]

[ "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." ]

[ 1, 0, 0, 0, 0, 0, 0 ]

[ 0.2857142686843872, 0.25, 0.20512819290161133, 0.07692307233810425, 0.0952380895614624, 0.22727271914482117, 0.17142856121063232 ]

[ "We present a new algorithm for learning by curriculum based on the notion of mastering rate that outperforms previous algorithms." ]

[ "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" ]

[ 0, 0, 0, 0, 1, 0, 0, 0 ]

[ 0.07407406717538834, 0.11428570747375488, 0.13333332538604736, 0.1428571343421936, 0.1875, 0.1304347813129425, 0.17142856121063232, 0.11538460850715637 ]

[ "Inspiration from local dendritic processes of neocortical learning to make unsupervised learning great again." ]

[ "Real-world Question Answering (QA) tasks consist of thousands of words that often represent many facts and entities.", "Existing models based on LSTMs require a large number of parameters to support external memory and do not generalize well for long sequence inputs.", "Memory networks attempt to address these limitations by storing information to an external memory module but must examine all inputs in the memory.", "Hence, for longer sequence inputs the intermediate memory components proportionally scale in size resulting in poor inference times and high computation costs.\n\n", "In this paper, we present Adaptive Memory Networks (AMN) that process input question pairs to dynamically construct a network architecture optimized for lower inference times.", "During inference, AMN parses input text into entities within different memory slots.", "However, distinct from previous approaches, AMN is a dynamic network architecture that creates variable numbers of memory banks weighted by question relevance.", "Thus, the decoder can select a variable number of memory banks to construct an answer using fewer banks, creating a runtime trade-off between accuracy and speed. \n\n", "AMN is enabled by first, a novel bank controller that makes discrete decisions with high accuracy and second, the capabilities of a dynamic framework (such as PyTorch) that allow for dynamic network sizing and efficient variable mini-batching.", "In our results, we demonstrate that our model learns to construct a varying number of memory banks based on task complexity and achieves faster inference times for standard bAbI tasks, and modified bAbI tasks.", "We achieve state of the art accuracy over these tasks with an average 48% lower entities are examined during inference." ]

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[ 0, 0, 0.1538461446762085, 0.07407407462596893, 0.13333332538604736, 0, 0, 0, 0.052631575614213943, 0.1111111119389534, 0.1599999964237213 ]

[ "Memory networks with faster inference" ]

[ "When a bilingual student learns to solve word problems in math, we expect the student to be able to solve these problem in both languages the student is fluent in, even if the math lessons were only taught in one language.", "However, current representations in machine learning are language dependent.", "In this work, we present a method to decouple the language from the problem by learning language agnostic representations and therefore allowing training a model in one language and applying to a different one in a zero shot fashion.", "We learn these representations by taking inspiration from linguistics, specifically the Universal Grammar hypothesis and learn universal latent representations that are language agnostic (Chomsky, 2014; Montague, 1970).", "We demonstrate the capabilities of these representations by showing that the models trained on a single language using language agnostic representations achieve very similar accuracies in other languages." ]

[ 0, 0, 0, 1, 0 ]

[ 0.17543859779834747, 0.1764705777168274, 0.29629629850387573, 0.5199999809265137, 0.19999998807907104 ]

[ "By taking inspiration from linguistics, specifically the Universal Grammar hypothesis, we learn language agnostic universal representations which we can utilize to do zero-shot learning across languages." ]

[ "Generative models with both discrete and continuous latent variables are highly motivated by the structure of many real-world data sets.", "They present, however, subtleties in training often manifesting in the discrete latent variable not being leveraged.", "In this paper, we show why such models struggle to train using traditional log-likelihood maximization, and that they are amenable to training using the Optimal Transport framework of Wasserstein Autoencoders.", "We find our discrete latent variable to be fully leveraged by the model when trained, without any modifications to the objective function or significant fine tuning.", "Our model generates comparable samples to other approaches while using relatively simple neural networks, since the discrete latent variable carries much of the descriptive burden.", "Furthermore, the discrete latent provides significant control over generation." ]

[ 0, 1, 0, 0, 0, 0 ]

[ 0.25531914830207825, 0.2857142686843872, 0.2545454502105713, 0.23529411852359772, 0.23529411852359772, 0.1666666567325592 ]

[ "This paper shows that the Wasserstein distance objective enables the training of latent variable models with discrete latents in a case where the Variational Autoencoder objective fails to do so." ]

[ "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." ]

[ 0, 0, 1, 0, 0, 0 ]

[ 0.0624999962747097, 0.21621620655059814, 0.29411762952804565, 0.1304347813129425, 0.19512194395065308, 0.060606054961681366 ]

[ "A graph neural network able to automatically learn and leverage a dynamic interactive graph structure" ]

[ "We introduce NAMSG, an adaptive first-order algorithm for training neural networks.", "The method is efficient in computation and memory, and is straightforward to implement.", "It computes the gradients at configurable remote observation points, in order to expedite the convergence by adjusting the step size for directions with different curvatures in the stochastic setting.", "It also scales the updating vector elementwise by a nonincreasing preconditioner to take the advantages of AMSGRAD.", "We analyze the convergence properties for both convex and nonconvex problems by modeling the training process as a dynamic system, and provide a strategy to select the observation factor without grid search.", "A data-dependent regret bound is proposed to guarantee the convergence in the convex setting.", "The method can further achieve a O(log(T)) regret bound for strongly convex functions.", "Experiments demonstrate that NAMSG works well in practical problems and compares favorably to popular adaptive methods, such as ADAM, NADAM, and AMSGRAD." ]

[ 1, 0, 0, 0, 0, 0, 0, 0 ]

[ 0.47999998927116394, 0.07999999821186066, 0.10256409645080566, 0.06666666269302368, 0.1428571343421936, 0.14814814925193787, 0.07407406717538834, 0.34285715222358704 ]

[ "A new algorithm for training neural networks that compares favorably to popular adaptive methods." ]

[ "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." ]

[ 0, 0, 0, 1 ]

[ 0.16326530277729034, 0.1090909019112587, 0.2028985470533371, 0.21875 ]

[ "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." ]

[ "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." ]

[ 0, 1, 0, 0, 0, 0, 0, 0 ]

[ 0.0476190410554409, 0.9583333134651184, 0.0624999962747097, 0.08695651590824127, 0.09999999403953552, 0.09302324801683426, 0.1818181723356247, 0.18867923319339752 ]

[ "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." ]

[ "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." ]

[ 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0 ]

[ 0.07999999821186066, 0, 0, 0.0833333283662796, 0, 0, 0.09090908616781235, 0.31578946113586426, 0, 0.08695651590824127, 0.07692307233810425, 0.07999999821186066, 0, 0, 0 ]

[ "Autoencoders for text with a new method for using discrete latent space." ]

[ " Auto-encoding and generative models have made tremendous successes in image and signal representation learning and generation.", "These models, however, generally employ the full Euclidean space or a bounded subset (such as $[0,1]^l$) as the latent space, whose trivial geometry is often too simplistic to meaningfully reflect the structure of the data.", "This paper aims at exploring a nontrivial geometric structure of the latent space for better data representation.", "Inspired by differential geometry, we propose \\textbf{Chart Auto-Encoder (CAE)}, which captures the manifold structure of the data with multiple charts and transition functions among them.", "CAE translates the mathematical definition of manifold through parameterizing the entire data set as a collection of overlapping charts, creating local latent representations.", "These representations are an enhancement of the single-charted latent space commonly employed in auto-encoding models, as they reflect the intrinsic structure of the manifold. ", "Therefore, CAE achieves a more accurate approximation of data and generates realistic new ones.", "We conduct experiments with synthetic and real-life data to demonstrate the effectiveness of the proposed CAE." ]

[ 0, 0, 1, 0, 0, 0, 0, 0 ]

[ 0.20000000298023224, 0.10810810327529907, 0.260869562625885, 0, 0.07407406717538834, 0.1428571343421936, 0, 0 ]

[ "Manifold-structured latent space for generative models" ]

[ "We tackle the problem of modeling sequential visual phenomena.", "Given examples of a phenomena that can be divided into discrete time steps, we aim to take an input from any such time and realize this input at all other time steps in the sequence.", "Furthermore, we aim to do this \\textit{without} ground-truth aligned sequences --- avoiding the difficulties needed for gathering aligned data.", "This generalizes the unpaired image-to-image problem from generating pairs to generating sequences.", "We extend cycle consistency to \\textit{loop consistency} and alleviate difficulties associated with learning in the resulting long chains of computation.", "We show competitive results compared to existing image-to-image techniques when modeling several different data sets including the Earth's seasons and aging of human faces." ]

[ 0, 1, 0, 0, 0, 0 ]

[ 0.07692307233810425, 0.16326530277729034, 0.11428570747375488, 0.0714285671710968, 0.1621621549129486, 0.04878048226237297 ]

[ "LoopGAN extends cycle length in CycleGAN to enable unaligned sequential transformation for more than two time steps." ]

[ "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." ]

[ 1, 0, 0, 0 ]

[ 0.307692289352417, 0.10526315122842789, 0.0624999962747097, 0.1818181723356247 ]

[ "We propose SWAP, a distributed algorithm for large-batch training of neural networks." ]

[ "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." ]

[ 0, 0, 0, 0, 0, 0, 1 ]

[ 0.25641024112701416, 0.0952380895614624, 0.11428570747375488, 0.2222222238779068, 0.1702127605676651, 0.5238094925880432, 0.5945945978164673 ]

[ "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." ]

[ "Finding an embedding space for a linear approximation of a nonlinear dynamical system enables efficient system identification and control synthesis.", "The Koopman operator theory lays the foundation for identifying the nonlinear-to-linear coordinate transformations with data-driven methods.", "Recently, researchers have proposed to use deep neural networks as a more expressive class of basis functions for calculating the Koopman operators.", "These approaches, however, assume a fixed dimensional state space; they are therefore not applicable to scenarios with a variable number of objects.", "In this paper, we propose to learn compositional Koopman operators, using graph neural networks to encode the state into object-centric embeddings and using a block-wise linear transition matrix to regularize the shared structure across objects.", "The learned dynamics can quickly adapt to new environments of unknown physical parameters and produce control signals to achieve a specified goal.", "Our experiments on manipulating ropes and controlling soft robots show that the proposed method has better efficiency and generalization ability than existing baselines." ]

[ 1, 0, 0, 0, 0, 0, 0 ]

[ 0.41379308700561523, 0.1538461446762085, 0.1818181723356247, 0, 0.1428571343421936, 0.1249999925494194, 0.060606054961681366 ]

[ "Learning compositional Koopman operators for efficient system identification and model-based control." ]

[ "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" ]

[ 1, 0, 0, 0 ]

[ 0.42307692766189575, 0.27272728085517883, 0.23529411852359772, 0.09999999403953552 ]

[ "We present a constant memory gradient computation procedure through solutions of stochastic differential equations (SDEs) and apply the method for learning latent SDE models." ]

[ "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." ]

[ 0, 0, 0, 0, 1, 0, 0, 0 ]

[ 0.09999999403953552, 0.10526315122842789, 0, 0.04999999701976776, 0.13636364042758942, 0, 0.0476190447807312, 0 ]

[ "Learning privacy-preserving transformations from data. A collaborative approach" ]

[ "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." ]

[ 0, 0, 0, 0, 0, 0, 1 ]

[ 0.0952380895614624, 0.11764705181121826, 0.17777776718139648, 0.19999998807907104, 0.2857142686843872, 0.1666666567325592, 0.3050847351551056 ]

[ "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." ]

[ "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." ]

[ 0, 0, 1, 0, 0, 0, 0, 0 ]

[ 0, 0.14999999105930328, 0.4000000059604645, 0.1599999964237213, 0.22727271914482117, 0.25806450843811035, 0.1249999925494194, 0.06896550953388214 ]

[ "We propose to learn synthesizing few-shot classifiers and many-shot classifiers using one single objective function for GFSL." ]

[ "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." ]

[ 1, 0, 0, 0, 0, 0, 0, 0, 0 ]

[ 0.17391303181648254, 0.052631575614213943, 0.09999999403953552, 0.13793103396892548, 0.0833333283662796, 0.14814814925193787, 0.14999999105930328, 0, 0.0624999962747097 ]

[ "We combine A* search with reinforcement learning to speed up machine learning code" ]

[ "Grasping an object and precisely stacking it on another is a difficult task for traditional robotic control or hand-engineered approaches.", "Here we examine the problem in simulation and provide techniques aimed at solving it via deep reinforcement learning.", "We introduce two straightforward extensions to the Deep Deterministic Policy Gradient algorithm (DDPG), which make it significantly more data-efficient and scalable.", "Our results show that by making extensive use of off-policy data and replay, it is possible to find high-performance control policies.", "Further, our results hint that it may soon be feasible to train successful stacking policies by collecting interactions on real robots." ]

[ 0, 0, 0, 0, 1 ]

[ 0.06451612710952759, 0.20689654350280762, 0.0624999962747097, 0.1249999925494194, 0.25 ]

[ "Data-efficient deep reinforcement learning can be used to learning precise stacking policies." ]

[ "In recent years deep reinforcement learning has been shown to be adept at solving sequential decision processes with high-dimensional state spaces such as in the Atari games.", "Many reinforcement learning problems, however, involve high-dimensional discrete action spaces as well as high-dimensional state spaces.", "In this paper, we develop a novel policy gradient methodology for the case of large multidimensional discrete action spaces.", "We propose two approaches for creating parameterized policies: LSTM parameterization and a Modified MDP (MMDP) giving rise to Feed-Forward Network (FFN) parameterization.", "Both of these approaches provide expressive models to which backpropagation can be applied for training.", "We then consider entropy bonus, which is typically added to the reward function to enhance exploration.", "In the case of high-dimensional action spaces, calculating the entropy and the gradient of the entropy requires enumerating all the actions in the action space and running forward and backpropagation for each action, which may be computationally infeasible.", "We develop several novel unbiased estimators for the entropy bonus and its gradient.", "Finally, we test our algorithms on two environments: a multi-hunter multi-rabbit grid game and a multi-agent multi-arm bandit problem." ]

[ 0, 0, 0, 0, 0, 0, 0, 1, 0 ]

[ 0.04878048226237297, 0.14814814925193787, 0.3636363446712494, 0.17142856121063232, 0.06896550953388214, 0.06896550953388214, 0.2380952388048172, 0.37037035822868347, 0.0624999962747097 ]

[ "policy parameterizations and unbiased policy entropy estimators for MDP with large multidimensional discrete action space" ]

[ "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." ]

[ 0, 0, 0, 0, 1 ]

[ 0.07692307233810425, 0.0476190447807312, 0.06666666269302368, 0, 0.08695651590824127 ]

[ "Decompose weights to use fewer FLOPs with SVD" ]

[ "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." ]

[ 0, 0, 0, 0, 1, 0, 0, 0 ]

[ 0.060606054961681366, 0.1304347813129425, 0, 0.17142856121063232, 0.1875, 0.11428570747375488, 0, 0.05882352590560913 ]

[ "Asymptotic convergence for stochastic subgradien method with momentum under general parallel asynchronous computation for general nonconvex nonsmooth optimization" ]

[ "Stochastic neural networks with discrete random variables are an important class of models for their expressivity and interpretability.", "Since direct differentiation and backpropagation is not possible, Monte Carlo gradient estimation techniques have been widely employed for training such models.", "Efficient stochastic gradient estimators, such Straight-Through and Gumbel-Softmax, work well for shallow models with one or two stochastic layers.", "Their performance, however, suffers with increasing model complexity.\n", "In this work we focus on stochastic networks with multiple layers of Boolean latent variables.", "To analyze such such networks, we employ the framework of harmonic analysis for Boolean functions. ", "We use it to derive an analytic formulation for the source of bias in the biased Straight-Through estimator.", "Based on the analysis we propose \\emph{FouST}, a simple gradient estimation algorithm that relies on three simple bias reduction steps.", "Extensive experiments show that FouST performs favorably compared to state-of-the-art biased estimators, while being much faster than unbiased ones.", "To the best of our knowledge FouST is the first gradient estimator to train up very deep stochastic neural networks, with up to 80 deterministic and 11 stochastic layers." ]

[ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ]

[ 0.19354838132858276, 0.11764705181121826, 0.32258063554763794, 0.09090908616781235, 0.2857142686843872, 0.1428571343421936, 0.19999998807907104, 0.06451612710952759, 0, 0.21052631735801697 ]

[ "We present a low-bias estimator for Boolean stochastic variable models with many stochastic layers." ]

[ "We propose a novel generative adversarial network for visual attributes manipulation (ManiGAN), which is able to semantically modify the visual attributes of given images using natural language descriptions.", "The key to our method is to design a novel co-attention module to combine text and image information rather than simply concatenating two features along the channel direction.", "Also, a detail correction module is proposed to rectify mismatched attributes of the synthetic image, and to reconstruct text-unrelated contents.", "Finally, we propose a new metric for evaluating manipulation results, in terms of both the generation of text-related attributes and the reconstruction of text-unrelated contents.", "Extensive experiments on benchmark datasets demonstrate the advantages of our proposed method, regarding the effectiveness of image manipulation and the capability of generating high-quality results." ]

[ 1, 0, 0, 0, 0 ]

[ 0.5641025304794312, 0.20512820780277252, 0.1249999925494194, 0.11428570747375488, 0 ]

[ "We propose a novel method to manipulate given images using natural language descriptions." ]

[ "Optimal Transport (OT) naturally arises in many machine learning applications, where we need to handle cross-modality data from multiple sources.", "Yet the heavy computational burden limits its wide-spread uses.", "To address the scalability issue, we propose an implicit generative learning-based framework called SPOT (Scalable Push-forward of Optimal Transport).", "Specifically, we approximate the optimal transport plan by a pushforward of a reference distribution, and cast the optimal transport problem into a minimax problem.", "We then can solve OT problems efficiently using primal dual stochastic gradient-type algorithms.", "We also show that we can recover the density of the optimal transport plan using neural ordinary differential equations.", "Numerical experiments on both synthetic and real datasets illustrate that SPOT is robust and has favorable convergence behavior.", "SPOT also allows us to efficiently sample from the optimal transport plan, which benefits downstream applications such as domain adaptation." ]

[ 0, 0, 0, 0, 0, 0, 0, 1 ]

[ 0.0714285671710968, 0, 0, 0.1538461446762085, 0.0952380895614624, 0.1538461446762085, 0, 0.2142857164144516 ]

[ "Use GAN-based method to scalably solve optimal transport" ]

[ "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." ]

[ 0, 0, 1, 0 ]

[ 0.3333333432674408, 0.22857142984867096, 0.42105263471603394, 0.3636363446712494 ]

[ "Leveraging control as inference and Sequential Monte Carlo methods, we proposed a probabilistic planning algorithm." ]

[ "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." ]

[ 0, 1, 0, 0, 0, 0, 0 ]

[ 0, 0.37837836146354675, 0, 0, 0, 0.1111111044883728, 0.15789473056793213 ]

[ "A simple GAN modification that improves performance across many losses, architectures, regularization schemes, and datasets. " ]

[ "Extreme Classification Methods have become of paramount importance, particularly for Information Retrieval (IR) problems, owing to the development of smart algorithms that are scalable to industry challenges.", "One of the prime class of models that aim to solve the memory and speed challenge of extreme multi-label learning is Group Testing.", "Multi-label Group Testing (MLGT) methods construct label groups by grouping original labels either randomly or based on some similarity and then train smaller classifiers to first predict the groups and then recover the original label vectors.", "Recently, a novel approach called MACH (Merged Average Classifiers via Hashing) was proposed which projects the huge label vectors to a small and manageable count-min sketch (CMS) matrix and then learns to predict this matrix to recover the original prediction probabilities.", "Thereby, the model memory scales O(logK) for K classes.", "MACH is a simple algorithm which works exceptionally well in practice.", "Despite this simplicity of MACH, there is a big gap between the theoretical understanding of the trade-offs with MACH.", "In this paper we fill this gap.", "Leveraging the theory of count-min sketch we provide precise quantification of the memory-identifiablity tradeoffs.", "We extend the theory to the case of multi-label classification, where the dependencies make the estimators hard to calculate in closed forms.", "To mitigate this issue, we propose novel quadratic approximation using the Inclusion-Exclusion Principle.", "Our estimator has significantly lower reconstruction error than the typical CMS estimator across various values of number of classes K, label sparsity and compression ratio." ]

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[ "How to estimate original probability vector for millions of classes from count-min sketch measurements - a theoretical and practical setup." ]

[ "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" ]

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[ 0.07999999821186066, 0.06666666269302368, 0.1818181723356247, 0.1428571343421936, 0.20689654350280762, 0.17391303181648254 ]

[ "You can fix the classifier in neural networks without losing accuracy" ]

[ "This paper introduces NEMO, an approach to unsupervised object detection that uses motion---instead of image labels---as a cue to learn object detection.", "To discriminate between motion of the target object and other changes in the image, it relies on negative examples that show the scene without the object.", "The required data can be collected very easily by recording two short videos, a positive one showing the object in motion and a negative one showing the scene without the object.", "Without any additional form of pretraining or supervision and despite of occlusions, distractions, camera motion, and adverse lighting, those videos are sufficient to learn object detectors that can be applied to new videos and even generalize to unseen scenes and camera angles.", "In a baseline comparison, unsupervised object detection outperforms off-the shelf template matching and tracking approaches that are given an initial bounding box of the object.", "The learned object representations are also shown to be accurate enough to capture the relevant information from manipulation task demonstrations, which makes them applicable to learning from demonstration in robotics.", "An example of object detection that was learned from 3 minutes of video can be found here: http://y2u.be/u_jyz9_ETz4" ]

[ 0, 0, 0, 0, 0, 0, 1 ]

[ 0.19354838132858276, 0.11764705181121826, 0.05405404791235924, 0.08695651590824127, 0.0555555522441864, 0.10256409645080566, 0.3333333432674408 ]

[ "Learning to detect objects without image labels from 3 minutes of video" ]

[ "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." ]

[ 0, 0, 0, 0, 0, 0, 1 ]

[ 0.22727271914482117, 0.13636362552642822, 0.260869562625885, 0.03999999538064003, 0.2181818187236786, 0.20408162474632263, 0.4444444477558136 ]

[ "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." ]