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Anytime Neural Network: a Versatile Trade-off Between Computation and Accuracy

We present an approach for anytime predictions in deep neural networks.For each test sample, an anytime predictor produces a coarse result quickly, and then continues to refine it until the test-time computational budget is depleted.Such predictors can address the growing computational problem of DNNs by automatically adjusting to varying test-time budgets.In this work, we study a augmentation to feed-forward networks to form anytime neural networks via auxiliary predictions and losses.Specifically, we point out a blind-spot in recent studies in such ANNs: the importance of high final accuracy.In fact, we show on multiple recognition data-sets and architectures that by having near-optimal final predictions in small anytime models, we can effectively double the speed of large ones to reach corresponding accuracy level.We achieve such speed-up with simple weighting of anytime losses that oscillate during training.We also assemble a sequence of exponentially deepening ANNs, to achieve both theoretically and practically near-optimal anytime results at any budget, at the cost of a constant fraction of additional consumed budget.

By focusing more on the final predictions in anytime predictors (such as the very recent Multi-Scale-DenseNets), we make small anytime models to outperform large ones that don't have such focus.

Memory-efficient Learning for Large-scale Computational Imaging

Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems.Recently, critical aspects such as experimental design and image priors are optimized through deep neural networks formed by the unrolled iterations of classical physics-based reconstructions.However, for real-world large-scale systems, computing gradients via backpropagation restricts learning due to memory limitations of graphical processing units.In this work, we propose a memory-efficient learning procedure that exploits the reversibility of the network’s layers to enable data-driven design for large-scale computational imaging.We demonstrate our methods practicality on two large-scale systems: super-resolution optical microscopy and multi-channel magnetic resonance imaging.

We propose a memory-efficient learning procedure that exploits the reversibility of the network’s layers to enable data-driven design for large-scale computational imaging.

Neuron as an Agent

Existing multi-agent reinforcement learning communication methods have relied on a trusted third party to distribute reward to agents, leaving them inapplicable in peer-to-peer environments.This paper proposes reward distribution using in MARL without a TTP with two key ideas: inter-agent reward distribution and auction theory.Auction theory is introduced because inter-agent reward distribution is insufficient for optimization.Agents in NaaA maximize their profits and, as a theoretical result, the auction mechanism is shown to have agents autonomously evaluate counterfactual returns as the values of other agents.NaaA enables representation trades in peer-to-peer environments, ultimately regarding unit in neural networks as agents.Finally, numerical experiments confirm that NaaA framework optimization leads to better performance in reinforcement learning.

Neuron as an Agent (NaaA) enable us to train multi-agent communication without a trusted third party.

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.However, at some point further model increases become harder due to GPU/TPU memory limitations, longer training times, and unexpected model degradation.To address these problems, we present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT.Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT.We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs.As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and SQuAD benchmarks while having fewer parameters compared to BERT-large.

A new pretraining method that establishes new state-of-the-art results on the GLUE, RACE, and SQuAD benchmarks while having fewer parameters compared to BERT-large.

SuperTML: Domain Transfer from Computer Vision to Structured Tabular Data through Two-Dimensional Word Embedding

Structured tabular data is the most commonly used form of data in industry according to a Kaggle ML and DS Survey.Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data.The recent work of Super Characters method using two-dimensional word embeddings achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach.In this paper, we propose the SuperTML method, which borrows the idea of Super Characters method and two-dimensional embeddings to address the problem of classification on tabular data.For each input of tabular data, the features are first projected into two-dimensional embeddings like an image, and then this image is fed into fine-tuned ImageNet CNN models for classification.Experimental results have shown that the proposed SuperTML method have achieved state-of-the-art results on both large and small datasets.

Deep learning on structured tabular data using two-dimensional word embedding with fine-tuned ImageNet pre-trained CNN model.

Adaptive Gradient Methods with Dynamic Bound of Learning Rate

Adaptive optimization methods such as AdaGrad, RMSprop and Adam have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates.Though prevailing, they are observed to generalize poorly compared with SGD or even fail to converge due to unstable and extreme learning rates.Recent work has put forward some algorithms such as AMSGrad to tackle this issue but they failed to achieve considerable improvement over existing methods.In our paper, we demonstrate that extreme learning rates can lead to poor performance.We provide new variants of Adam and AMSGrad, called AdaBound and AMSBound respectively, which employ dynamic bounds on learning rates to achieve a gradual and smooth transition from adaptive methods to SGD and give a theoretical proof of convergence.We further conduct experiments on various popular tasks and models, which is often insufficient in previous work.Experimental results show that new variants can eliminate the generalization gap between adaptive methods and SGD and maintain higher learning speed early in training at the same time.Moreover, they can bring significant improvement over their prototypes, especially on complex deep networks.The implementation of the algorithm can be found at https://github.com/Luolc/AdaBound .

Novel variants of optimization methods that combine the benefits of both adaptive and non-adaptive methods.

GenDICE: Generalized Offline Estimation of Stationary Values

An important problem that arises in reinforcement learning and Monte Carlo methods is estimating quantities defined by the stationary distribution of a Markov chain.In many real-world applications, access to the underlying transition operator is limited to a fixed set of data that has already been collected, without additional interaction with the environment being available.We show that consistent estimation remains possible in this scenario, and that effective estimation can still be achieved in important applications.Our approach is based on estimating a ratio that corrects for the discrepancy between the stationary and empirical distributions, derived from fundamental properties of the stationary distribution, and exploiting constraint reformulations based on variational divergence minimization.The resulting algorithm, GenDICE, is straightforward and effective.We prove the consistency of the method under general conditions, provide a detailed error analysis, and demonstrate strong empirical performance on benchmark tasks, including off-line PageRank and off-policy policy evaluation.

In this paper, we proposed a novel algorithm, GenDICE, for general stationary distribution correction estimation, which can handle both discounted and average off-policy evaluation on multiple behavior-agnostic samples.

The Blessing of Dimensionality: An Empirical Study of Generalization

The power of neural networks lies in their ability to generalize to unseen data, yet the underlying reasons for this phenomenon remain elusive.Numerous rigorous attempts have been made to explain generalization, but available bounds are still quite loose, and analysis does not always lead to true understanding.The goal of this work is to make generalization more intuitive.Using visualization methods, we discuss the mystery of generalization, the geometry of loss landscapes, and how the curse of dimensionality causes optimizers to settle into minima that generalize well.

An intuitive empirical and visual exploration of the generalization properties of deep neural networks.

Symmetry and Systematicity

We argue that symmetry is an important consideration in addressing the problemof systematicity and investigate two forms of symmetry relevant to symbolic processes.We implement this approach in terms of convolution and show that it canbe used to achieve effective generalisation in three toy problems: rule learning,composition and grammar learning.

We use convolution to make neural networks behave more like symbolic systems.

Nonlinear Differential Equations with external forcing

Key equatorial climate phenomena such as QBO and ENSO have never been adequately explained as deterministic processes.This in spite of recent research showing growing evidence of predictable behavior.This study applies the fundamental Laplace tidal equations with simplifying assumptions along the equator — i.e. no Coriolis force and a small angle approximation.The solutions to the partial differential equations are highly non-linear related to Navier-Stokes and only search approaches can be used to fit to the data.

Analytical Formulation of Equatorial Standing Wave Phenomena: Application to QBO and ENSO

OvA-INN: Continual Learning with Invertible Neural Networks

In the field of Continual Learning, the objective is to learn several tasks one after the other without access to the data from previous tasks.Several solutions have been proposed to tackle this problem but they usually assume that the user knows which of the tasks to perform at test time on a particular sample, or rely on small samples from previous data and most of them suffer of a substantial drop in accuracy when updated with batches of only one class at a time.In this article, we propose a new method, OvA-INN, which is able to learn one class at a time and without storing any of the previous data.To achieve this, for each class, we train a specific Invertible Neural Network to output the zero vector for its class.At test time, we can predict the class of a sample by identifying which network outputs the vector with the smallest norm.With this method, we show that we can take advantage of pretrained models by stacking an invertible network on top of a features extractor.This way, we are able to outperform state-of-the-art approaches that rely on features learning for the Continual Learning of MNIST and CIFAR-100 datasets.In our experiments, we are reaching 72% accuracy on CIFAR-100 after training our model one class at a time.

We propose to train an Invertible Neural Network for each class to perform class-by-class Continual Learning.

An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems.

Human-computer conversation systems have attracted much attention in Natural Language Processing.Conversation systems can be roughly divided into two categories: retrieval-based and generation-based systems.Retrieval systems search a user-issued utterance in a large conversational repository and return a reply that best matches the query.Generative approaches synthesize new replies.Both ways have certain advantages but suffer from their own disadvantages.We propose a novel ensemble of retrieval-based and generation-based conversation system.The retrieved candidates, in addition to the original query, are fed to a reply generator via a neural network, so that the model is aware of more information.The generated reply together with the retrieved ones then participates in a re-ranking process to find the final reply to output.Experimental results show that such an ensemble system outperforms each single module by a large margin.

A novel ensemble of retrieval-based and generation-based for open-domain conversation systems.

Towards Understanding the Transferability of Deep Representations

Deep neural networks trained on a wide range of datasets demonstrate impressive transferability.Deep features appear general in that they are applicable to many datasets and tasks.Such property is in prevalent use in real-world applications.A neural network pretrained on large datasets, such as ImageNet, can significantly boost generalization and accelerate training if fine-tuned to a smaller target dataset.Despite its pervasiveness, few effort has been devoted to uncovering the reason of transferability in deep feature representations.This paper tries to understand transferability from the perspectives of improved generalization, optimization and the feasibility of transferability.We demonstrate that1) Transferred models tend to find flatter minima, since their weight matrices stay close to the original flat region of pretrained parameters when transferred to a similar target dataset;2) Transferred representations make the loss landscape more favorable with improved Lipschitzness, which accelerates and stabilizes training substantially.The improvement largely attributes to the fact that the principal component of gradient is suppressed in the pretrained parameters, thus stabilizing the magnitude of gradient in back-propagation.3) The feasibility of transferability is related to the similarity of both input and label.And a surprising discovery is that the feasibility is also impacted by the training stages in that the transferability first increases during training, and then declines.We further provide a theoretical analysis to verify our observations.

Understand transferability from the perspectives of improved generalization, optimization and the feasibility of transferability.

Functional vs. parametric equivalence of ReLU networks

We address the following question: How redundant is the parameterisation of ReLU networks?Specifically, we consider transformations of the weight space which leave the function implemented by the network intact.Two such transformations are known for feed-forward architectures: permutation of neurons within a layer, and positive scaling of all incoming weights of a neuron coupled with inverse scaling of its outgoing weights.In this work, we show for architectures with non-increasing widths that permutation and scaling are in fact the only function-preserving weight transformations.For any eligible architecture we give an explicit construction of a neural network such that any other network that implements the same function can be obtained from the original one by the application of permutations and rescaling.The proof relies on a geometric understanding of boundaries between linear regions of ReLU networks, and we hope the developed mathematical tools are of independent interest.

We prove that there exist ReLU networks whose parameters are almost uniquely determined by the function they implement.

Task-Based Top-Down Modulation Network for Multi-Task-Learning Applications

A general problem that received considerable recent attention is how to perform multiple tasks in the same network, maximizing both efficiency and prediction accuracy.A popular approach consists of a multi-branch architecture on top of ashared backbone, jointly trained on a weighted sum of losses.However, in many cases, the shared representation results in non-optimal performance, mainly due to an interference between conflicting gradients of uncorrelated tasks.Recent approaches address this problem by a channel-wise modulation of the feature-maps along the shared backbone, with task specific vectors, manually or dynamically tuned.Taking this approach a step further, we propose a novel architecture whichmodulate the recognition network channel-wise, as well as spatial-wise, with an efficient top-down image-dependent computation scheme.Our architecture uses no task-specific branches, nor task specific modules.Instead, it uses a top-down modulation network that is shared between all of the tasks.We show the effectiveness of our scheme by achieving on par or better results than alternative approaches on both correlated and uncorrelated sets of tasks.We also demonstrate our advantages in terms of model size, the addition of novel tasks and interpretability.Code will be released.

We propose a top-down modulation network for multi-task learning applications with several advantages over current schemes.

Dynamical Clustering of Time Series Data Using Multi-Decoder RNN Autoencoder

Clustering algorithms have wide applications and play an important role in data analysis fields including time series data analysis.The performance of a clustering algorithm depends on the features extracted from the data.However, in time series analysis, there has been a problem that the conventional methods based on the signal shape are unstable for phase shift, amplitude and signal length variations.In this paper, we propose a new clustering algorithm focused on the dynamical system aspect of the signal using recurrent neural network and variational Bayes method.Our experiments show that our proposed algorithm has a robustness against above variations and boost the classification performance.

Novel time series data clustring algorithm based on dynamical system features.

Learning Explainable Models Using Attribution Priors

Two important topics in deep learning both involve incorporating humans into the modeling process: Model priors transfer information from humans to a model by regularizing the model's parameters; Model attributions transfer information from a model to humans by explaining the model's behavior.", 'Previous work has taken important steps to connect these topics through various forms of gradient regularization."We find, however, that existing methods that use attributions to align a model's behavior with human intuition are ineffective.", "We develop an efficient and theoretically grounded feature attribution method, expected gradients, and a novel framework, attribution priors, to enforce prior expectations about a model's behavior during training.", 'We demonstrate that attribution priors are broadly applicable by instantiating them on three different types of data: image data, gene expression data, and health care data.Our experiments show that models trained with attribution priors are more intuitive and achieve better generalization performance than both equivalent baselines and existing methods to regularize model behavior.

A method for encouraging axiomatic feature attributions of a deep model to match human intuition.

Learning Recurrent Binary/Ternary Weights

Recurrent neural networks have shown excellent performance in processing sequence data.However, they are both complex and memory intensive due to their recursive nature.These limitations make RNNs difficult to embed on mobile devices requiring real-time processes with limited hardware resources.To address the above issues, we introduce a method that can learn binary and ternary weights during the training phase to facilitate hardware implementations of RNNs.As a result, using this approach replaces all multiply-accumulate operations by simple accumulations, bringing significant benefits to custom hardware in terms of silicon area and power consumption.On the software side, we evaluate the performance of our method using long short-term memories and gated recurrent units on various sequential models including sequence classification and language modeling.We demonstrate that our method achieves competitive results on the aforementioned tasks while using binary/ternary weights during the runtime.On the hardware side, we present custom hardware for accelerating the recurrent computations of LSTMs with binary/ternary weights.Ultimately, we show that LSTMs with binary/ternary weights can achieve up to 12x memory saving and 10x inference speedup compared to the full-precision hardware implementation design.

We propose high-performance LSTMs with binary/ternary weights, that can greatly reduce implementation complexity

Unsupervised Few-shot Object Recognition by Integrating Adversarial, Self-supervision, and Deep Metric Learning of Latent Parts

This paper addresses unsupervised few-shot object recognition, where all training images are unlabeled and do not share classes with labeled support images for few-shot recognition in testing.We use a new GAN-like deep architecture aimed at unsupervised learning of an image representation which will encode latent object parts and thus generalize well to unseen classes in our few-shot recognition task.Our unsupervised training integrates adversarial, self-supervision, and deep metric learning.We make two contributions.First, we extend the vanilla GAN with reconstruction loss to enforce the discriminator capture the most relevant characteristics of "fake" images generated from randomly sampled codes.Second, we compile a training set of triplet image examples for estimating the triplet loss in metric learning by using an image masking procedure suitably designed to identify latent object parts.Hence, metric learning ensures that the deep representation of images showing similar object classes which share some parts are closer than the representations of images which do not have common parts.Our results show that we significantly outperform the state of the art, as well as get similar performance to the common episodic training for fully-supervised few-shot learning on the Mini-Imagenet and Tiered-Imagenet datasets.

We address the problem of unsupervised few-shot object recognition, where all training images are unlabeled and do not share classes with test images.

Exploring the Space of Black-box Attacks on Deep Neural Networks

Existing black-box attacks on deep neural networks so far have largely focused on transferability, where an adversarial instance generated for a locally trained model can “transfer” to attack other learning models.In this paper, we propose novel Gradient Estimation black-box attacks for adversaries with query access to the target model’s class probabilities, which do not rely on transferability.We also propose strategies to decouple the number of queries required to generate each adversarial sample from the dimensionality of the input.An iterative variant of our attack achieves close to 100% adversarial success rates for both targeted and untargeted attacks on DNNs.We carry out extensive experiments for a thorough comparative evaluation of black-box attacks and show that the proposed Gradient Estimation attacks outperform all transferability based black-box attacks we tested on both MNIST and CIFAR-10 datasets, achieving adversarial success rates similar to well known, state-of-the-art white-box attacks.We also apply the Gradient Estimation attacks successfully against a real-world content moderation classifier hosted by Clarifai.Furthermore, we evaluate black-box attacks against state-of-the-art defenses.We show that the Gradient Estimation attacks are very effective even against these defenses.

Query-based black-box attacks on deep neural networks with adversarial success rates matching white-box attacks

Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification

We propose a novel subgraph image representation for classification of network fragments with the target being their parent networks.The graph image representation is based on 2D image embeddings of adjacency matrices.We use this image representation in two modes.First, as the input to a machine learning algorithm.Second, as the input to a pure transfer learner.Our conclusions from multiple datasets are that1. deep learning using structured image features performs the best compared to graph kernel and classical features based methods; and,2. pure transfer learning works effectively with minimum interference from the user and is robust against small data.

We convert subgraphs into structured images and classify them using 1. deep learning and 2. transfer learning (Caffe) and achieve stunning results.

Self-ensembling for visual domain adaptation

This paper explores the use of self-ensembling for visual domain adaptation problems.Our technique is derived from the mean teacher variant of temporal ensembling, a technique that achieved state of the art results in the area of semi-supervised learning.We introduce a number of modifications to their approach for challenging domain adaptation scenarios and evaluate its effectiveness.Our approach achieves state of the art results in a variety of benchmarks, including our winning entry in the VISDA-2017 visual domain adaptation challenge.In small image benchmarks, our algorithm not only outperforms prior art, but can also achieve accuracy that is close to that of a classifier trained in a supervised fashion.

Self-ensembling based algorithm for visual domain adaptation, state of the art results, won VisDA-2017 image classification domain adaptation challenge.

Generative Models of Visually Grounded Imagination

It is easy for people to imagine what a man with pink hair looks like, even if they have never seen such a person before.We call the ability to create images of novel semantic concepts visually grounded imagination.In this paper, we show how we can modify variational auto-encoders to perform this task.Our method uses a novel training objective, and a novel product-of-experts inference network, which can handle partially specified concepts in a principled and efficient way.We also propose a set of easy-to-compute evaluation metrics that capture our intuitive notions of what it means to have good visual imagination, namely correctness, coverage, and compositionality.Finally, we perform a detailed comparison of our method with two existing joint image-attribute VAE methods by applying them to two datasets: the MNIST-with-attributes dataset, and the CelebA dataset.

A VAE-variant which can create diverse images corresponding to novel concrete or abstract "concepts" described using attribute vectors.

Combining Q-Learning and Search with Amortized Value Estimates

We introduce "Search with Amortized Value Estimates", an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search.In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values.The new Q-estimates are then used in combination with real experience to update the prior.This effectively amortizes the value computation performed by MCTS, resulting in a cooperative relationship between model-free learning and model-based search.SAVE can be implemented on top of any Q-learning agent with access to a model, which we demonstrate by incorporating it into agents that perform challenging physical reasoning tasks and Atari.SAVE consistently achieves higher rewards with fewer training steps, and---in contrast to typical model-based search approaches---yields strong performance with very small search budgets.By combining real experience with information computed during search, SAVE demonstrates that it is possible to improve on both the performance of model-free learning and the computational cost of planning.

We propose a model-based method called "Search with Amortized Value Estimates" (SAVE) which leverages both real and planned experience by combining Q-learning with Monte-Carlo Tree Search, achieving strong performance with very small search budgets.

Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients

Two main families of reinforcement learning algorithms, Q-learning and policy gradients, have recently been proven to be equivalent when using a softmax relaxation on one part, and an entropic regularization on the other.We relate this result to the well-known convex duality of Shannon entropy and the softmax function.Such a result is also known as the Donsker-Varadhan formula.This provides a short proof of the equivalence.We then interpret this duality further, and use ideas of convex analysis to prove a new policy inequality relative to soft Q-learning.

A short proof of the equivalence of soft Q-learning and policy gradients.

Policy Transfer with Strategy Optimization

Computer simulation provides an automatic and safe way for training robotic controlpolicies to achieve complex tasks such as locomotion.However, a policytrained in simulation usually does not transfer directly to the real hardware dueto the differences between the two environments.Transfer learning using domainrandomization is a promising approach, but it usually assumes that the target environmentis close to the distribution of the training environments, thus relyingheavily on accurate system identification.In this paper, we present a differentapproach that leverages domain randomization for transferring control policies tounknown environments.The key idea that, instead of learning a single policy inthe simulation, we simultaneously learn a family of policies that exhibit differentbehaviors.When tested in the target environment, we directly search for the bestpolicy in the family based on the task performance, without the need to identifythe dynamic parameters.We evaluate our method on five simulated robotic controlproblems with different discrepancies in the training and testing environmentand demonstrate that our method can overcome larger modeling errors comparedto training a robust policy or an adaptive policy.

We propose a policy transfer algorithm that can overcome large and challenging discrepancies in the system dynamics such as latency, actuator modeling error, etc.

vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations

We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task.The algorithm uses either a gumbel softmax or online k-means clustering to quantize the dense representations.Discretization enables the direct application of algorithms from the NLP community which require discrete inputs.Experiments show that BERT pre-training achieves a new state of the art on TIMIT phoneme classification and WSJ speech recognition.

Learn how to quantize speech signal and apply algorithms requiring discrete inputs to audio data such as BERT.

Hierarchical Deep Reinforcement Learning Agent with Counter Self-play on Competitive Games

Deep Reinforcement Learning algorithms lead to agents that can solve difficult decision making problems in complex environments.However, many difficult multi-agent competitive games, especially real-time strategy games are still considered beyond the capability of current deep reinforcement learning algorithms, although there has been a recent effort to change this p.Moreover, when the opponents in a competitive game are suboptimal, the current seeking, self-play algorithms are often unable to generalize their strategies to opponents that play strategies vastly different from their own.This suggests that a learning algorithm that is beyond conventional self-play is necessary.We develop Hierarchical Agent with Self-play, a learning approach for obtaining hierarchically structured policies that can achieve higher performance than conventional self-play on competitive games through the use of a diverse pool of sub-policies we get from Counter Self-Play.We demonstrate that the ensemble policy generated by HASP can achieve better performance while facing unseen opponents that use sub-optimal policies.On a motivating iterated Rock-Paper-Scissor game and a partially observable real-time strategic game, we are led to the conclusion that HASP can perform better than conventional self-play as well as achieve 77% win rate against FloBot, an open-source agent which has ranked at position number 2 on the online leaderboards.

We develop Hierarchical Agent with Self-play (HASP), a learning approach for obtaining hierarchically structured policies that can achieve high performance than conventional self-play on competitive real-time strategic games.

Adaptive Input Representations for Neural Language Modeling

We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. to input representations of variable capacity.There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units.We perform a systematic comparison of popular choices for a self-attentional architecture.Our experiments show that models equipped with adaptive embeddings are more than twice as fast to train than the popular character input CNN while having a lower number of parameters.On the WikiText-103 benchmark we achieve 18.7 perplexity, an improvement of 10.5 perplexity compared to the previously best published result and on the Billion Word benchmark, we achieve 23.02 perplexity.

Variable capacity input word embeddings and SOTA on WikiText-103, Billion Word benchmarks.

Deep Multivariate Mixture of Gaussians for Object Detection under Occlusion

In this paper, we consider the problem of detecting object under occlusion.Most object detectors formulate bounding box regression as a unimodal task.However, we observe that the bounding box borders of an occluded object can have multiple plausible configurations.Also, the occluded bounding box borders have correlations with visible ones.Motivated by these two observations, we propose a deep multivariate mixture of Gaussians model for bounding box regression under occlusion.The mixture components potentially learn different configurations of an occluded part, and the covariances between variates help to learn the relationship between the occluded parts and the visible ones.Quantitatively, our model improves the AP of the baselines by 3.9% and 1.2% on CrowdHuman and MS-COCO respectively with almost no computational or memory overhead.Qualitatively, our model enjoys explainability since we can interpret the resulting bounding boxes via the covariance matrices and the mixture components.

a deep multivariate mixture of Gaussians model for bounding box regression under occlusion

Adversarial Training with Voronoi Constraints

Adversarial examples are a pervasive phenomenon of machine learning models where seemingly imperceptible perturbations to the input lead to misclassifications for otherwise statistically accurate models. Adversarial training, one of the most successful empirical defenses to adversarial examples, refers to training on adversarial examples generated within a geometric constraint set.The most commonly used geometric constraint is an-ball of radius in some norm.We introduce adversarial training with Voronoi constraints, which replaces the-ball constraint with the Voronoi cell for each point in the training set.We show that adversarial training with Voronoi constraints produces robust models which significantly improve over the state-of-the-art on MNIST and are competitive on CIFAR-10.

We replace the Lp ball constraint with the Voronoi cells of the training data to produce more robust models.

Generalized Natural Language Grounded Navigation via Environment-agnostic Multitask Learning

Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e.g., following natural language instructions or dialog.However, existing methods tend to overfit training data in seen environments and fail to generalize well in previously unseen environments.In order to close the gap between seen and unseen environments, we aim at learning a generalizable navigation model from two novel perspectives: we introduce a multitask navigation model that can be seamlessly trained on both Vision-Language Navigation and Navigation from Dialog History tasks, which benefits from richer natural language guidance and effectively transfers knowledge across tasks; we propose to learn environment-agnostic representations for navigation policy that are invariant among environments, thus generalizing better on unseen environments.Extensive experiments show that our environment-agnostic multitask navigation model significantly reduces the performance gap between seen and unseen environments and outperforms the baselines on unseen environments by 16% on VLN and 120% on NDH, establishing the new state of the art for NDH task.

We propose to learn a more generalized policy for natural language grounded navigation tasks via environment-agnostic multitask learning.

Wasserstein Barycenter Model Ensembling

In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein barycenters.Optimal transport metrics, such as the Wasserstein distance, allow incorporating semantic side information such as word embeddings.Using W. barycenters to find the consensus between models allows us to balance confidence and semantics in finding the agreement between the models.We show applications of Wasserstein ensembling in attribute-based classification, multilabel learning and image captioning generation.These results show that the W. ensembling is a viable alternative to the basic geometric or arithmetic mean ensembling.

we propose to use Wasserstein barycenters for semantic model ensembling

Unbiased scalable softmax optimization

Recent neural network and language models have begun to rely on softmax distributions with an extremely large number of categories.In this context calculating the softmax normalizing constant is prohibitively expensive.This has spurred a growing literature of efficiently computable but biased estimates of the softmax.In this paper we present the first two unbiased algorithms for maximizing the softmax likelihood whose work per iteration is independent of the number of classes and datapoints."We compare our unbiased methods' empirical performance to the state-of-the-art on seven real world datasets, where they comprehensively outperform all competitors.

Propose first methods for exactly optimizing the softmax distribution using stochastic gradient with runtime independent on the number of classes or datapoints.

MCTSBug: Generating Adversarial Text Sequences via Monte Carlo Tree Search and Homoglyph Attack

Crafting adversarial examples on discrete inputs like text sequences is fundamentally different from generating such examples for continuous inputs like images.This paper tries to answer the question: under a black-box setting, can we create adversarial examples automatically to effectively fool deep learning classifiers on texts by making imperceptible changes?Our answer is a firm yes.Previous efforts mostly replied on using gradient evidence, and they are less effective either due to finding the nearest neighbor word automatically is difficult or relying heavily on hand-crafted linguistic rules.We, instead, use Monte Carlo tree search for finding the most important few words to perturb and perform homoglyph attack by replacing one character in each selected word with a symbol of identical shape. Our novel algorithm, we call MCTSBug, is black-box and extremely effective at the same time.Our experimental results indicate that MCTSBug can fool deep learning classifiers at the success rates of 95% on seven large-scale benchmark datasets, by perturbing only a few characters. Surprisingly, MCTSBug, without relying on gradient information at all, is more effective than the gradient-based white-box baseline.Thanks to the nature of homoglyph attack, the generated adversarial perturbations are almost imperceptible to human eyes.

Use Monte carlo Tree Search and Homoglyphs to generate indistinguishable adversarial samples on text data

Learning to Infer Graphics Programs from Hand-Drawn Images

We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of \\LaTeX.~The model combines techniques from deep learning and program synthesis. We learn a convolutional neural network that proposes plausible drawing primitives that explain an image.These drawing primitives are like a trace of the set of primitive commands issued by a graphics program.We learn a model that uses program synthesis techniques to recover a graphics program from that trace.These programs have constructs like variable bindings, iterative loops, or simple kinds of conditionals.With a graphics program in hand, we can correct errors made by the deep network and extrapolate drawings. Taken together these results are a step towards agents that induce useful, human-readable programs from perceptual input.

Learn to convert a hand drawn sketch into a high-level program

Calibration of neural network logit vectors to combat adversarial attacks

Adversarial examples remain an issue for contemporary neural networks.This paper draws on Background Check, a technique in model calibration, to assist two-class neural networks in detecting adversarial examples, using the one dimensional difference between logit values as the underlying measure.This method interestingly tends to achieve the highest average recall on image sets that are generated with large perturbation vectors, which is unlike the existing literature on adversarial attacks.The proposed method does not need knowledge of the attack parameters or methods at training time, unlike a great deal of the literature that uses deep learning based methods to detect adversarial examples, such as Metzen et al., imbuing the proposed method with additional flexibility.

This paper uses principles from the field of calibration in machine learning on the logits of a neural network to defend against adversarial attacks

Multitask learning of Multilingual Sentence Representations

We present a novel multi-task training approach to learning multilingual distributed representations of text.Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together with a cross-lingual sentence similarity model.We construct sentence embeddings by processing word embeddings with an LSTM and by taking an average of the outputs.Our architecture can transparently use both monolingual and sentence aligned bilingual corpora to learn multilingual embeddings, thus covering a vocabulary significantly larger than the vocabulary of the bilingual corpora alone.Our model shows competitive performance in a standard cross-lingual document classification task.We also show the effectiveness of our method in a low-resource scenario.

We jointly train a multilingual skip-gram model and a cross-lingual sentence similarity model to learn high quality multilingual text embeddings that perform well in the low resource scenario.

Generative model based on minimizing exact empirical Wasserstein distance

Generative Adversarial Networks are a very powerful framework for generative modeling.However, they are often hard to train, and learning of GANs often becomes unstable.Wasserstein GAN is a promising framework to deal with the instability problem as it has a good convergence property.One drawback of the WGAN is that it evaluates the Wasserstein distance in the dual domain, which requires some approximation, so that it may fail to optimize the true Wasserstein distance.In this paper, we propose evaluating the exact empirical optimal transport cost efficiently in the primal domain and performing gradient descent with respect to its derivative to train the generator network.Experiments on the MNIST dataset show that our method is significantly stable to converge, and achieves the lowest Wasserstein distance among the WGAN variants at the cost of some sharpness of generated images.Experiments on the 8-Gaussian toy dataset show that better gradients for the generator are obtained in our method.In addition, the proposed method enables more flexible generative modeling than WGAN.

We have proposed a flexible generative model that learns stably by directly minimizing exact empirical Wasserstein distance.

NAS evaluation is frustratingly hard

Neural Architecture Search is an exciting new field which promises to be as much as a game-changer as Convolutional Neural Networks were in 2012.Despite many great works leading to substantial improvements on a variety of tasks, comparison between different methods is still very much an open issue.While most algorithms are tested on the same datasets, there is no shared experimental protocol followed by all.As such, and due to the under-use of ablation studies, there is a lack of clarity regarding why certain methods are more effective than others.Our first contribution is a benchmark of 8 NAS methods on 5 datasets.To overcome the hurdle of comparing methods with different search spaces, we propose using a method’s relative improvement over the randomly sampled average architecture, which effectively removes advantages arising from expertly engineered search spaces or training protocols.Surprisingly, we find that many NAS techniques struggle to significantly beat the average architecture baseline.We perform further experiments with the commonly used DARTS search space in order to understand the contribution of each component in the NAS pipeline.These experiments highlight that: the use of tricks in the evaluation protocol has a predominant impact on the reported performance of architectures; the cell-based search space has a very narrow accuracy range, such that the seed has a considerable impact on architecture rankings; the hand-designed macrostructure is more important than the searched micro-structure; and the depth-gap is a real phenomenon, evidenced by the change in rankings between 8 and 20 cell architectures.To conclude, we suggest best practices, that we hope will prove useful for the community and help mitigate current NAS pitfalls, e.g. difficulties in reproducibility and comparison of search methods.Thecode used is available at https://github.com/antoyang/NAS-Benchmark.

A study of how different components in the NAS pipeline contribute to the final accuracy. Also, a benchmark of 8 methods on 5 datasets.

NLProlog: Reasoning with Weak Unification for Natural Language Question Answering

Symbolic logic allows practitioners to build systems that perform rule-based reasoning which is interpretable and which can easily be augmented with prior knowledge.However, such systems are traditionally difficult to apply to problems involving natural language due to the large linguistic variability of language.Currently, most work in natural language processing focuses on neural networks which learn distributed representations of words and their composition, thereby performing well in the presence of large linguistic variability.We propose to reap the benefits of both approaches by applying a combination of neural networks and logic programming to natural language question answering.We propose to employ an external, non-differentiable Prolog prover which utilizes a similarity function over pretrained sentence encoders.We fine-tune these representations via Evolution Strategies with the goal of multi-hop reasoning on natural language. This allows us to create a system that can apply rule-based reasoning to natural language and induce domain-specific natural language rules from training data.We evaluate the proposed system on two different question answering tasks, showing that it complements two very strong baselines – BIDAF and FASTQA – and outperforms both when used in an ensemble.

We introduce NLProlog, a system that performs rule-based reasoning on natural language by leveraging pretrained sentence embeddings and fine-tuning with Evolution Strategies, and apply it to two multi-hop Question Answering tasks.

Learning from Samples of Variable Quality

Training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing.This creates a fundamental quality-versus-quantity trade-off in the learning process. Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data?We argue that if the learner could somehow know and take the label-quality into account, we could get the best of both worlds. To this end, we introduce “fidelity-weighted learning”, a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data.FWL modulates the parameter updates to a student network, trained on the task we care about on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher, who has access to limited samples with high-quality labels.

We propose Fidelity-weighted Learning, a semi-supervised teacher-student approach for training neural networks using weakly-labeled data.

Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks

This paper is focused on investigating and demystifying an intriguing robustness phenomena in over-parameterized neural network training.In particular we provide empirical and theoretical evidence that first order methods such as gradient descent are provably robust to noise/corruption on a constant fraction of the labels despite over-parameterization under a rich dataset model.In particular:i) First, we show that in the first few iterations where the updates are still in the vicinity of the initialization these algorithms only fit to the correct labels essentially ignoring the noisy labels.ii) Secondly, we prove that to start to overfit to the noisy labels these algorithms must stray rather far from from the initial model which can only occur after many more iterations.Together, these show that gradient descent with early stopping is provably robust to label noise and shed light on empirical robustness of deep networks as well as commonly adopted early-stopping heuristics.

We prove that gradient descent is robust to label corruption despite over-parameterization under a rich dataset model.

Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network

Weight pruning has been introduced as an efficient model compression technique.Even though pruning removes significant amount of weights in a network, memory requirement reduction was limited since conventional sparse matrix formats require significant amount of memory to store index-related information.Moreover, computations associated with such sparse matrix formats are slow because sequential sparse matrix decoding process does not utilize highly parallel computing systems efficiently.As an attempt to compress index information while keeping the decoding process parallelizable, Viterbi-based pruning was suggested.Decoding non-zero weights, however, is still sequential in Viterbi-based pruning.In this paper, we propose a new sparse matrix format in order to enable a highly parallel decoding process of the entire sparse matrix.The proposed sparse matrix is constructed by combining pruning and weight quantization.For the latest RNN models on PTB and WikiText-2 corpus, LSTM parameter storage requirement is compressed 19x using the proposed sparse matrix format compared to the baseline model.Compressed weight and indices can be reconstructed into a dense matrix fast using Viterbi encoders.Simulation results show that the proposed scheme can feed parameters to processing elements 20 % to 106 % faster than the case where the dense matrix values directly come from DRAM.

We present a new weight encoding scheme which enables high compression ratio and fast sparse-to-dense matrix conversion.

Generative Inpainting Network Applications on Seismic Image Compression

The use of deep learning models as priors for compressive sensing tasks presents new potential for inexpensive seismic data acquisition.An appropriately designed Wasserstein generative adversarial network is designed based on a generative adversarial network architecture trained on several historical surveys, capable of learning the statistical properties of the seismic wavelets.The usage of validating and performance testing of compressive sensing are three steps.First, the existence of a sparse representation with different compression rates for seismic surveys is studied.Then, non-uniform samplings are studied, using the proposed methodology.Finally, recommendations for non-uniform seismic survey grid, based on the evaluation of reconstructed seismic images and metrics, is proposed.The primary goal of the proposed deep learning model is to provide the foundations of an optimal design for seismic acquisition, with less loss in imaging quality.Along these lines, a compressive sensing design of a non-uniform grid over an asset in Gulf of Mexico, versus a traditional seismic survey grid which collects data uniformly at every few feet, is suggested, leveraging the proposed method.

Improved a GAN based pixel inpainting network for compressed seismic image recovery andproposed\xa0a non-uniform sampling survey recommendatio, which can be easily applied to medical and other domains for compressive sensing technique.

Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning

In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota.One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum.In contrast to these settings, success in the real world commonly requires humans to collaborate and communicate with others, in settings that are, at least partially, cooperative.In the last year, the card game Hanabi has been established as a new benchmark environment for AI to fill this gap.In particular, Hanabi is interesting to humans since it is entirely focused on theory of mind, i.e. the ability to effectively reason over the intentions, beliefs and point of view of other agents when observing their actions.Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning: Fundamentally, RL requires agents to explore in order to discover good policies.However, when done naively, this randomness will inherently make their actions less informative to others during training.We present a new deep multi-agent RL method, the Simplified Action Decoder, which resolves this contradiction exploiting the centralized training phase.During training SAD allows other agents to not only observe the action chosen, but agents instead also observe the greedy action of their team mates.By combining this simple intuition with an auxiliary task for state prediction and best practices for multi-agent learning, SAD establishes a new state of the art for 2-5 players on the self-play part of the Hanabi challenge.

We develop Simplified Action Decoder, a simple MARL algorithm that beats previous SOTA on Hanabi by a big margin across 2- to 5-player games.

Chargrid-OCR: End-to-end trainable Optical Character Recognition through Semantic Segmentation and Object Detection

We present an end-to-end trainable approach for optical character recognition on printed documents."It is based on predicting a two-dimensional character grid representation of a document image as a semantic segmentation task.", 'To identify individual character instances from the chargrid, we regard characters as objects and use object detection techniques from computer vision.We demonstrate experimentally that our method outperforms previous state-of-the-art approaches in accuracy while being easily parallelizable on GPU, as well as easier to train.

End-to-end trainable Optical Character Recognition on printed documents; we achieve state-of-the-art results, beating Tesseract4 on benchmark datasets both in terms of accuracy and runtime, using a purely computer vision based approach.

Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments

We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay.In our experiments on neural networks for image classification, speech recognition, machine translation, and language modeling, it performs on par or better than well tuned SGD with momentum and Adam/AdamW.Additionally, NovoGrad is robust to the choice of learning rate and weight initialization, works well in a large batch setting, and has two times smaller memory footprint than Adam.

NovoGrad - an adaptive SGD method with layer-wise gradient normalization and decoupled weight decay.

DDSP: Differentiable Digital Signal Processing

Most generative models of audio directly generate samples in one of two domains: time or frequency.While sufficient to express any signal, these representations are inefficient, as they do not utilize existing knowledge of how sound is generated and perceived.A third approach successfully incorporates strong domain knowledge of signal processing and perception, but has been less actively researched due to limited expressivity and difficulty integrating with modern auto-differentiation-based machine learning methods.In this paper, we introduce the Differentiable Digital Signal Processing library, which enables direct integration of classic signal processing elements with deep learning methods.Focusing on audio synthesis, we achieve high-fidelity generation without the need for large autoregressive models or adversarial losses, demonstrating that DDSP enables utilizing strong inductive biases without losing the expressive power of neural networks.Further, we show that combining interpretable modules permits manipulation of each separate model component, with applications such as independent control of pitch and loudness, realistic extrapolation to pitches not seen during training, blind dereverberation of room acoustics, transfer of extracted room acoustics to new environments, and transformation of timbre between disparate sources.In short, DDSP enables an interpretable and modular approach to generative modeling, without sacrificing the benefits of deep learning.The library will is available at https://github.com/magenta/ddsp and we encourage further contributions from the community and domain experts.

Better audio synthesis by combining interpretable DSP with end-to-end learning.

Spectral Convolutional Networks on Hierarchical Multigraphs

Spectral Graph Convolutional Networks are a generalization of convolutional networks to learning on graph-structured data.Applications of spectral GCNs have been successful, but limited to a few problems where the graph is fixed, such as shape correspondence and node classification.In this work, we address this limitation by revisiting a particular family of spectral graph networks, Chebyshev GCNs, showing its efficacy in solving graph classification tasks with a variable graph structure and size.Current GCNs also restrict graphs to have at most one edge between any pair of nodes.To this end, we propose a novel multigraph network that learns from multi-relational graphs.We explicitly model different types of edges: annotated edges, learned edges with abstract meaning, and hierarchical edges.We also experiment with different ways to fuse the representations extracted from different edge types.This restriction is sometimes implied from a dataset, however, we relax this restriction for all kinds of datasets.We achieve state-of-the-art results on a variety of chemical, social, and vision graph classification benchmarks.

A novel approach to graph classification based on spectral graph convolutional networks and its extension to multigraphs with learnable relations and hierarchical structure. We show state-of-the art results on chemical, social and image datasets.

Clean-Label Backdoor Attacks

Deep neural networks have been recently demonstrated to be vulnerable to backdoor attacks.Specifically, by altering a small set of training examples, an adversary is able to install a backdoor that can be used during inference to fully control the model’s behavior.While the attack is very powerful, it crucially relies on the adversary being able to introduce arbitrary, often clearly mislabeled, inputs to the training set and can thus be detected even by fairly rudimentary data filtering.In this paper, we introduce a new approach to executing backdoor attacks, utilizing adversarial examples and GAN-generated data.The key feature is that the resulting poisoned inputs appear to be consistent with their label and thus seem benign even upon human inspection.

We show how to successfully perform backdoor attacks without changing training labels.

On the importance of single directions for generalization

Despite their ability to memorize large datasets, deep neural networks often achieve good generalization performance.However, the differences between the learned solutions of networks which generalize and those which do not remain unclear.Additionally, the tuning properties of single directions have been highlighted, but their importance has not been evaluated.Here, we connect these lines of inquiry to demonstrate that a network’s reliance on single directions is a good predictor of its generalization performance, across networks trained on datasets with different fractions of corrupted labels, across ensembles of networks trained on datasets with unmodified labels, across different hyper- parameters, and over the course of training.While dropout only regularizes this quantity up to a point, batch normalization implicitly discourages single direction reliance, in part by decreasing the class selectivity of individual units.Finally, we find that class selectivity is a poor predictor of task importance, suggesting not only that networks which generalize well minimize their dependence on individual units by reducing their selectivity, but also that individually selective units may not be necessary for strong network performance.

We find that deep networks which generalize poorly are more reliant on single directions than those that generalize well, and evaluate the impact of dropout and batch normalization, as well as class selectivity on single direction reliance.

Learning undirected models via query training

Typical amortized inference in variational autoencoders is specialized for a single probabilistic query.Here we propose an inference network architecture that generalizes to unseen probabilistic queries.Instead of an encoder-decoder pair, we can train a single inference network directly from data, using a cost function that is stochastic not only over samples, but also over queries.We can use this network to perform the same inference tasks as we would in an undirected graphical model with hidden variables, without having to deal with the intractable partition function.The results can be mapped to the learning of an actual undirected model, which is a notoriously hard problem.Our network also marginalizes nuisance variables as required. We show that our approach generalizes to unseen probabilistic queries on also unseen test data, providing fast and flexible inference.Experiments show that this approach outperforms or matches PCD and AdVIL on 9 benchmark datasets.

Instead of learning the parameters of a graphical model from data, learn an inference network that can answer the same probabilistic queries.

Newton Residual Learning

A plethora of computer vision tasks, such as optical flow and image alignment, can be formulated as non-linear optimization problems.Before the resurgence of deep learning, the dominant family for solving such optimization problems was numerical optimization, e.g, Gauss-Newton.More recently, several attempts were made to formulate learnable GN steps as cascade regression architectures.In this paper, we investigate recent machine learning architectures, such as deep neural networks with residual connections, under the above perspective.To this end, we first demonstrate how residual blocks can be viewed as GN steps."Then, we go a step further and propose a new residual block, that is reminiscent of Newton's method in numerical optimization and exhibits faster convergence.", 'We thoroughly evaluate the proposed Newton-ResNet by conducting experiments on image and speech classification and image generation, using 4 datasets.All the experiments demonstrate that Newton-ResNet requires less parameters to achieve the same performance with the original ResNet.

We demonstrate how residual blocks can be viewed as Gauss-Newton steps; we propose a new residual block that exploits second order information.

Domain-independent Plan Intervention When Users Unwittingly Facilitate Attacks

In competitive situations, agents may take actions to achieve their goals that unwittingly facilitate an opponent’s goals.Weconsider a domain where three agents operate: a user, an attacker agent and an observer agent.The user and the attacker compete to achieve different goals.When there is a disparity in the domain knowledge the user and the attacker possess, the attacker may use the user’s unfamiliarity with the domain toits advantage and further its own goal.In this situation, the observer, whose goal is to support the user may need to intervene, and this intervention needs to occur online, on-time and be accurate.We formalize the online plan intervention problem and propose a solution that uses a decision tree classifier to identify intervention points in situations where agents unwittingly facilitate an opponent’s goal.We trained a classifier using domain-independent features extracted from the observer’s decision space to evaluate the “criticality” of the current state.The trained model is then used in an online setting on IPC benchmarks to identify observations that warrant intervention.Our contributions lay a foundation for further work in the area of deciding when to intervene.

We introduce a machine learning model that uses domain-independent features to estimate the criticality of the current state to cause a known undesirable state.

Counterfactuals uncover the modular structure of deep generative models

Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data.However, manipulating such representation to perform meaningful and controllable transformations in the data space remains challenging without some form of supervision.While previous work has focused on exploiting statistical independence to latent factors, we argue that such requirement can be advantageously relaxed and propose instead a non-statistical framework that relies on identifying a modular organization of the network, based on counterfactual manipulations.Our experiments support that modularity between groups of channels is achieved to a certain degree on a variety of generative models.This allowed the design of targeted interventions on complex image datasets, opening the way to applications such as computationally efficient style transfer and the automated assessment of robustness to contextual changes in pattern recognition systems.

We develop a framework to find modular internal representations in generative models and manipulate then to generate counterfactual examples.

Differentiable Hebbian Plasticity for Continual Learning

Catastrophic forgetting poses a grand challenge for continual learning systems, which prevents neural networks from protecting old knowledge while learning new tasks sequentially.We propose a Differentiable Hebbian Plasticity Softmax layer which adds a fast learning plastic component to the slow weights of the softmax output layer.The DHP Softmax behaves as a compressed episodic memory that reactivates existing memory traces, while creating new ones.We demonstrate the flexibility of our model by combining it with existing well-known consolidation methods to prevent catastrophic forgetting.We evaluate our approach on the Permuted MNIST and Split MNIST benchmarks, and introduce Imbalanced Permuted MNIST — a dataset that combines the challenges of class imbalance and concept drift.Our model requires no additional hyperparameters and outperforms comparable baselines by reducing forgetting.

Hebbian plastic weights can behave as a compressed episodic memory storage in neural networks; improving their ability to alleviate catastrophic forgetting in continual learning.

Checking Functional Modularity in DNN By Biclustering Task-specific Hidden Neurons

While real brain networks exhibit functional modularity, we investigate whether functional mod- ularity also exists in Deep Neural Networks trained through back-propagation.Under the hypothesis that DNN are also organized in task-specific modules, in this paper we seek to dissect a hidden layer into disjoint groups of task-specific hidden neurons with the help of relatively well- studied neuron attribution methods.By saying task-specific, we mean the hidden neurons in the same group are functionally related for predicting a set of similar data samples, i.e. samples with similar feature patterns.We argue that such groups of neurons which we call Functional Modules can serve as the basic functional unit in DNN.We propose a preliminary method to identify Functional Modules via bi- clustering attribution scores of hidden neurons.We find that first, unsurprisingly, the functional neurons are highly sparse, i.e., only a small sub- set of neurons are important for predicting a small subset of data samples and, while we do not use any label supervision, samples corresponding to the same group show surprisingly coherent feature patterns.We also show that these Functional Modules perform a critical role in discriminating data samples through ablation experiment.

We develop an approach to parcellate a hidden layer in DNN into functionally related groups, by applying spectral coclustering on the attribution scores of hidden neurons.

System Demo for Transfer Learning from Vision to Language using Domain Specific CNN Accelerator for On-Device NLP Applications

Power-efficient CNN Domain Specific Accelerator chips are currently available for wide use in mobile devices.These chips are mainly used in computer vision applications.However, the recent work of Super Characters method for text classification and sentiment analysis tasks using two-dimensional CNN models has also achieved state-of-the-art results through the method of transfer learning from vision to text.In this paper, we implemented the text classification and sentiment analysis applications on mobile devices using CNN-DSA chips.Compact network representations using one-bit and three-bits precision for coefficients and five-bits for activations are used in the CNN-DSA chip with power consumption less than 300mW.For edge devices under memory and compute constraints, the network is further compressed by approximating the external Fully Connected layers within the CNN-DSA chip.At the workshop, we have two system demonstrations for NLP tasks.The first demo classifies the input English Wikipedia sentence into one of the 14 classes.The second demo classifies the Chinese online-shopping review into positive or negative.

Deploy text classification and sentiment analysis applications for English and Chinese on a 300mW CNN accelerator chip for on-device application scenarios.

Rapid Model Comparison by Amortizing Across Models

Comparing the inferences of diverse candidate models is an essential part of model checking and escaping local optima.To enable efficient comparison, we introduce an amortized variational inference framework that can perform fast and reliable posterior estimation across models of the same architecture.Our Any Parameter Encoder extends the encoder neural network common in amortized inference to take both a data feature vector and a model parameter vector as input.APE thus reduces posterior inference across unseen data and models to a single forward pass.In experiments comparing candidate topic models for synthetic data and product reviews, our Any Parameter Encoder yields comparable posteriors to more expensive methods in far less time, especially when the encoder architecture is designed in model-aware fashion.

We develop VAEs where the encoder takes a model parameter vector as input, so we can do rapid inference for many models

Self-Attentional Credit Assignment for Transfer in Reinforcement Learning

The ability to transfer knowledge to novel environments and tasks is a sensible desiderata for general learning agents.Despite the apparent promises, transfer in RL is still an open and little exploited research area.In this paper, we take a brand-new perspective about transfer: we suggest that the ability to assign credit unveils structural invariants in the tasks that can be transferred to make RL more sample efficient.Our main contribution is Secret, a novel approach to transfer learning for RL that uses a backward-view credit assignment mechanism based on a self-attentive architecture.Two aspects are key to its generality: it learns to assign credit as a separate offline supervised process and exclusively modifies the reward function.Consequently, it can be supplemented by transfer methods that do not modify the reward function and it can be plugged on top of any RL algorithm.

Secret is a transfer method for RL based on the transfer of credit assignment.

Neural Variational Inference For Embedding Knowledge Graphs

Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data.In this paper, we introduce two generic Variational Inference frameworks for generative models of Knowledge Graphs; Latent Fact Model and Latent Information Model. While traditional variational methods derive an analytical approximation for the intractable distribution over the latent variables, here we construct an inference network conditioned on the symbolic representation of entities and relation types in the Knowledge Graph, to provide the variational distributions.The new framework can create models able to discover underlying probabilistic semantics for the symbolic representation by utilising parameterisable distributions which permit training by back-propagation in the context of neural variational inference, resulting in a highly-scalable method.Under a Bernoulli sampling framework, we provide an alternative justification for commonly used techniques in large-scale stochastic variational inference, which drastically reduces training time at a cost of an additional approximation to the variational lower bound. The generative frameworks are flexible enough to allow training under any prior distribution that permits a re-parametrisation trick, as well as under any scoring function that permits maximum likelihood estimation of the parameters.Experiment results display the potential and efficiency of this framework by improving upon multiple benchmarks with Gaussian prior representations.Code publicly available on Github.

Working toward generative knowledge graph models to better estimate predictive uncertainty in knowledge inference.

Monge-Amp\`ere Flow for Generative Modeling

We present a deep generative model, named Monge-Amp\\`ere flow, which builds on continuous-time gradient flow arising from the Monge-Amp\\`ere equation in optimal transport theory.The generative map from the latent space to the data space follows a dynamical system, where a learnable potential function guides a compressible fluid to flow towards the target density distribution.Training of the model amounts to solving an optimal control problem.The Monge-Amp\\`ere flow has tractable likelihoods and supports efficient sampling and inference.One can easily impose symmetry constraints in the generative model by designing suitable scalar potential functions.We apply the approach to unsupervised density estimation of the MNIST dataset and variational calculation of the two-dimensional Ising model at the critical point.This approach brings insights and techniques from Monge-Amp\\`ere equation, optimal transport, and fluid dynamics into reversible flow-based generative models.

A gradient flow based dynamical system for invertible generative modeling

Memory-Based Graph Networks

Graph Neural Networks are a class of deep models that operates on data with arbitrary topology and order-invariant structure represented as graphs.We introduce an efficient memory layer for GNNs that can learn to jointly perform graph representation learning and graph pooling.We also introduce two new networks based on our memory layer: Memory-Based Graph Neural Network and Graph Memory Network that can learn hierarchical graph representations by coarsening the graph throughout the layers of memory.The experimental results demonstrate that the proposed models achieve state-of-the-art results in six out of seven graph classification and regression benchmarks.We also show that the learned representations could correspond to chemical features in the molecule data.

We introduce an efficient memory layer that can learn representation and coarsen input graphs simultaneously without relying on message passing.

Efficient Computation of Quantized Neural Networks by {−1, +1} Encoding Decomposition

Deep neural networks require extensive computing resources, and can not be efficiently applied to embedded devices such as mobile phones, which seriously limits their applicability.To address this problem, we propose a novel encoding scheme by using to decompose quantized neural networks into multi-branch binary networks, which can be efficiently implemented by bitwise operations to achieve model compression, computational acceleration and resource saving.Our method can achieve at most ~59 speedup and ~32 memory saving over its full-precision counterparts.Therefore, users can easily achieve different encoding precisions arbitrarily according to their requirements and hardware resources.Our mechanism is very suitable for the use of FPGA and ASIC in terms of data storage and computation, which provides a feasible idea for smart chips.We validate the effectiveness of our method on both large-scale image classification and object detection tasks.

A novel encoding scheme of using to decompose QNNs into multi-branch binary networks, in which we used bitwise operations (xnor and bitcount) to achieve model compression, computational acceleration and resource saving.

Diversity is All You Need: Learning Skills without a Reward Function

Intelligent creatures can explore their environments and learn useful skills without supervision."In this paper, we propose Diversity is All You Need, a method for learning useful skills without a reward function.", 'Our proposed method learns skills by maximizing an information theoretic objective using a maximum entropy policy.On a variety of simulated robotic tasks, we show that this simple objective results in the unsupervised emergence of diverse skills, such as walking and jumping.In a number of reinforcement learning benchmark environments, our method is able to learn a skill that solves the benchmark task despite never receiving the true task reward.We show how pretrained skills can provide a good parameter initialization for downstream tasks, and can be composed hierarchically to solve complex, sparse reward tasks.Our results suggest that unsupervised discovery of skills can serve as an effective pretraining mechanism for overcoming challenges of exploration and data efficiency in reinforcement learning.

We propose an algorithm for learning useful skills without a reward function, and show how these skills can be used to solve downstream tasks.

Resolving Lexical Ambiguity in English–Japanese Neural Machine Translation

Lexical ambiguity, i.e., the presence of two or more meanings for a single word, is an inherent and challenging problem for machine translation systems.Even though the use of recurrent neural networks and attention mechanisms are expected to solve this problem, machine translation systems are not always able to correctly translate lexically ambiguous sentences.In this work, I attempt to resolve the problem of lexical ambiguity in English--Japanese neural machine translation systems by combining a pretrained Bidirectional Encoder Representations from Transformer language model that can produce contextualized word embeddings and a Transformer translation model, which is a state-of-the-art architecture for the machine translation task.These two proposed architectures have been shown to be more effective in translating ambiguous sentences than a vanilla Transformer model and the Google Translate system.Furthermore, one of the proposed models, the Transformer_BERT-WE, achieves a higher BLEU score compared to the vanilla Transformer model in terms of general translation, which is concrete proof that the use of contextualized word embeddings from BERT can not only solve the problem of lexical ambiguity, but also boost the translation quality in general.

The paper solves a lexical ambiguity problem caused from homonym in neural translation by BERT.

Using GANs for Generation of Realistic City-Scale Ride Sharing/Hailing Data Sets

This paper focuses on the synthetic generation of human mobility data in urban areas.We present a novel and scalable application of Generative Adversarial Networks for modeling and generating human mobility data.We leverage actual ride requests from ride sharing/hailing services from four major cities in the US to train our GANs model.Our model captures the spatial and temporal variability of the ride-request patterns observed for all four cities on any typical day and over any typical week.Previous works have succinctly characterized the spatial and temporal properties of human mobility data sets using the fractal dimensionality and the densification power law, respectively, which we utilize to validate our GANs-generated synthetic data sets.Such synthetic data sets can avoid privacy concerns and be extremely useful for researchers and policy makers on urban mobility and intelligent transportation.

This paper focuses on the synthetic generation of human mobility data in urban areas using GANs.

Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior

Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to recover a near noise-free image from a noisy image.The underlying principle is that neural networks trained on large datasets have empirically been shown to be able to generate natural images well from a low-dimensional latent representation of the image.Given such a generator network, or prior, a noisy image can be denoised by finding the closest image in the range of the prior.However, there is little theory to justify this success, let alone to predict the denoising performance as a function of the networks parameters.In this paper we consider the problem of denoising an image from additive Gaussian noise, assuming the image is well described by a deep neural network with ReLu activations functions, mapping a k-dimensional latent space to an n-dimensional image.We state and analyze a simple gradient-descent-like iterative algorithm that minimizes a non-convex loss function, and provably removes a fraction of) of the noise energy.We also demonstrate in numerical experiments that this denoising performance is, indeed, achieved by generative priors learned from data.

By analyzing an algorithms minimizing a non-convex loss, we show that all but a small fraction of noise can be removed from an image using a deep neural network based generative prior.

Large Scale Multi-Domain Multi-Task Learning with MultiModel

Deep learning yields great results across many fields,from speech recognition, image classification, to translation.But for each problem, getting a deep model to work well involvesresearch into the architecture and a long period of tuning.We present a single model that yields good results on a numberof problems spanning multiple domains.In particular, this single modelis trained concurrently on ImageNet, multiple translation tasks,image captioning, a speech recognition corpus,and an English parsing task.Our model architecture incorporates building blocks from multipledomains.It contains convolutional layers, an attention mechanism,and sparsely-gated layers.Each of these computational blocks is crucial for a subset ofthe tasks we train on.Interestingly, even if a block is notcrucial for a task, we observe that adding it never hurts performanceand in most cases improves it on all tasks.We also show that tasks with less data benefit largely from jointtraining with other tasks, while performance on large tasks degradesonly slightly if at all.

Large scale multi-task architecture solves ImageNet and translation together and shows transfer learning.

Domain Adaptation Through Label Propagation: Learning Clustered and Aligned Features

The difficulty of obtaining sufficient labeled data for supervised learning has motivated domain adaptation, in which a classifier is trained in one domain, source domain, but operates in another, target domain.Reducing domain discrepancy has improved the performance, but it is hampered by the embedded features that do not form clearly separable and aligned clusters.We address this issue by propagating labels using a manifold structure, and by enforcing cycle consistency to align the clusters of features in each domain more closely.Specifically, we prove that cycle consistency leads the embedded features distant from all but one clusters if the source domain is ideally clustered.We additionally utilize more information from approximated local manifold and pursue local manifold consistency for more improvement.Results for various domain adaptation scenarios show tighter clustering and an improvement in classification accuracy.

A novel domain adaptation method to align manifolds from source and target domains using label propagation for better accuracy.

Neural Network Bandit Learning by Last Layer Marginalization

We propose a new method for training neural networks online in a bandit setting.Similar to prior work, we model the uncertainty only in the last layer of the network, treating the rest of the network as a feature extractor.This allows us to successfully balance between exploration and exploitation due to the efficient, closed-form uncertainty estimates available for linear models.To train the rest of the network, we take advantage of the posterior we have over the last layer, optimizing over all values in the last layer distribution weighted by probability.We derive a closed form, differential approximation to this objective and show empirically over various models and datasets that training the rest of the network in this fashion leads to both better online and offline performance when compared to other methods.

This paper proposes a new method for neural network learning in online bandit settings by marginalizing over the last layer

Aggregating explanation methods for neural networks stabilizes explanations

Despite a growing literature on explaining neural networks, no consensus has been reached on how to explain a neural network decision or how to evaluate an explanation.Our contributions in this paper are twofold.First, we investigate schemes to combine explanation methods and reduce model uncertainty to obtain a single aggregated explanation.The aggregation is more robust and aligns better with the neural network than any single explanation method..Second, we propose a new approach to evaluating explanation methods that circumvents the need for manual evaluation and is not reliant on the alignment of neural networks and humans decision processes.

We show in theory and in practice that combining multiple explanation methods for DNN benefits the explanation.

Training Neural Machines with Partial Traces

We present a novel approach for training neural abstract architectures which in- corporates supervision over the machine’s interpretable components.To cleanly capture the set of neural architectures to which our method applies, we introduce the concept of a differential neural computational machine and show that several existing architectures can be instantiated as a ∂NCM and can thus benefit from any amount of additional supervision over their interpretable components.Based on our method, we performed a detailed experimental evaluation with both, the NTM and NRAM architectures, and showed that the approach leads to significantly better convergence and generalization capabilities of the learning phase than when training using only input-output examples.

We increase the amount of trace supervision possible to utilize when training fully differentiable neural machine architectures.

Sampling-Free Learning of Bayesian Quantized Neural Networks

Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important.In this paper, we propose Bayesian quantized networks, quantized neural networks for which we learn a posterior distribution over their discrete parameters.We provide a set of efficient algorithms for learning and prediction in BQNs without the need to sample from their parameters or activations, which not only allows for differentiable learning in quantized models but also reduces the variance in gradients estimation.We evaluate BQNs on MNIST, Fashion-MNIST and KMNIST classification datasets compared against bootstrap ensemble of QNNs.We demonstrate BQNs achieve both lower predictive errors and better-calibrated uncertainties than E-QNN.

We propose Bayesian quantized networks, for which we learn a posterior distribution over their quantized parameters.

Cascade Adversarial Machine Learning Regularized with a Unified Embedding

Injecting adversarial examples during training, known as adversarial training, can improve robustness against one-step attacks, but not for unknown iterative attacks.To address this challenge, we first show iteratively generated adversarial images easily transfer between networks trained with the same strategy.Inspired by this observation, we propose cascade adversarial training, which transfers the knowledge of the end results of adversarial training.We train a network from scratch by injecting iteratively generated adversarial images crafted from already defended networks in addition to one-step adversarial images from the network being trained.We also propose to utilize embedding space for both classification and low-level similarity learning to ignore unknown pixel level perturbation.During training, we inject adversarial images without replacing their corresponding clean images and penalize the distance between the two embeddings.Experimental results show that cascade adversarial training together with our proposed low-level similarity learning efficiently enhances the robustness against iterative attacks, but at the expense of decreased robustness against one-step attacks.We show that combining those two techniques can also improve robustness under the worst case black box attack scenario.

Cascade adversarial training + low level similarity learning improve robustness against both white box and black box attacks.

Gradients explode - Deep Networks are shallow - ResNet explained

Whereas it is believed that techniques such as Adam, batch normalization and, more recently, SeLU nonlinearities solve the exploding gradient problem, we show that this is not the case and that in a range of popular MLP architectures, exploding gradients exist and that they limit the depth to which networks can be effectively trained, both in theory and in practice.", 'We explain why exploding gradients occur and highlight the, which can arise in architectures that avoid exploding gradients.ResNets have significantly lower gradients and thus can circumvent the exploding gradient problem, enabling the effective training of much deeper networks, which we show is a consequence of a surprising mathematical property.By noticing that, we devise the, which reveals that introducing skip connections simplifies the network mathematically, and that this simplicity may be the major cause for their success.

We show that in contras to popular wisdom, the exploding gradient problem has not been solved and that it limits the depth to which MLPs can be effectively trained. We show why gradients explode and how ResNet handles them.

From Adversarial Training to Generative Adversarial Networks

In this paper, we are interested in two seemingly different concepts: and .Particularly, how these techniques work to improve each other.To this end, we analyze the limitation of adversarial training as a defense method, starting from questioning how well the robustness of a model can generalize."Then, we successfully improve the generalizability via data augmentation by the fake images sampled from generative adversarial network.", 'After that, we are surprised to see that the resulting robust classifier leads to a better generator, for free.We intuitively explain this interesting phenomenon and leave the theoretical analysis for future work.Motivated by these observations, we propose a system that combines generator, discriminator, and adversarial attacker together in a single network.After end-to-end training and fine tuning, our method can simultaneously improve the robustness of classifiers, measured by accuracy under strong adversarial attacks, and the quality of generators, evaluated both aesthetically and quantitatively.In terms of the classifier, we achieve better robustness than the state-of-the-art adversarial training algorithm proposed in, while our generator achieves competitive performance compared with SN-GAN.

We found adversarial training not only speeds up the GAN training but also increases the image quality

Self-Binarizing Networks

We present a method to train self-binarizing neural networks, that is, networks that evolve their weights and activations during training to become binary.To obtain similar binary networks, existing methods rely on the sign activation function.This function, however, has no gradients for non-zero values, which makes standard backpropagation impossible.To circumvent the difficulty of training a network relying on the sign activation function, these methods alternate between floating-point and binary representations of the network during training, which is sub-optimal and inefficient.We approach the binarization task by training on a unique representation involving a smooth activation function, which is iteratively sharpened during training until it becomes a binary representation equivalent to the sign activation function.Additionally, we introduce a new technique to perform binary batch normalization that simplifies the conventional batch normalization by transforming it into a simple comparison operation.This is unlike existing methods, which are forced to the retain the conventional floating-point-based batch normalization.Our binary networks, apart from displaying advantages of lower memory and computation as compared to conventional floating-point and binary networks, also show higher classification accuracy than existing state-of-the-art methods on multiple benchmark datasets.

A method to binarize both weights and activations of a deep neural network that is efficient in computation and memory usage and performs better than the state-of-the-art.

Model Aggregation via Good-Enough Model Spaces

In many applications, the training data for a machine learning task is partitioned across multiple nodes, and aggregating this data may be infeasible due to storage, communication, or privacy constraints.In this work, we present Good-Enough Model Spaces, a novel framework for learning a global satisficing model within a few communication rounds by carefully combining the space of local nodes\' satisficing models.In experiments on benchmark and medical datasets, our approach outperforms other baseline aggregation techniques such as ensembling or model averaging, and performs comparably to the ideal non-distributed models.

We present Good-Enough Model Spaces (GEMS), a framework for learning an aggregate model over distributed nodes within a small number of communication rounds.

Wasserstein Auto-Encoders

We propose the Wasserstein Auto-Encoder---a new algorithm for building a generative model of the data distribution.WAE minimizes a penalized form of the Wasserstein distance between the model distribution and the target distribution, which leads to a different regularizer than the one used by the Variational Auto-Encoder.This regularizer encourages the encoded training distribution to match the prior.We compare our algorithm with several other techniques and show that it is a generalization of adversarial auto-encoders.Our experiments show that WAE shares many of the properties of VAEs while generating samples of better quality.

We propose a new auto-encoder based on the Wasserstein distance, which improves on the sampling properties of VAE.

Differential Privacy in Adversarial Learning with Provable Robustness

In this paper, we aim to develop a novel mechanism to preserve differential privacy in adversarial learning for deep neural networks, with provable robustness to adversarial examples.We leverage the sequential composition theory in DP, to establish a new connection between DP preservation and provable robustness.To address the trade-off among model utility, privacy loss, and robustness, we design an original, differentially private, adversarial objective function, based on the post-processing property in DP, to tighten the sensitivity of our model.An end-to-end theoretical analysis and thorough evaluations show that our mechanism notably improves the robustness of DP deep neural networks.

Preserving Differential Privacy in Adversarial Learning with Provable Robustness to Adversarial Examples

Learning Abstract Models for Long-Horizon Exploration

In high-dimensional reinforcement learning settings with sparse rewards, performingeffective exploration to even obtain any reward signal is an open challenge.While model-based approaches hold promise of better exploration via planning, itis extremely difficult to learn a reliable enough Markov Decision Processin high dimensions.In this paper, we propose learningan abstract MDP over a much smaller number of states, which we canplan over for effective exploration.We assume we have an abstraction functionthat maps concrete states to abstract states.In our approach, a manager maintains an abstractMDP over a subset of the abstract states, which grows monotonically through targetedexploration.Concurrently, we learn aworker policy to travel between abstract states; the worker deals with the messinessof concrete states and presents a clean abstraction to the manager.On three of', "the hardest games from the Arcade Learning Environment, our approach outperforms the previousstate-of-the-art by over a factor of 2 in each game.In Pitfall!, our approach isthe first to achieve superhuman performance without demonstrations.

We automatically construct and explore a small abstract Markov Decision Process, enabling us to achieve state-of-the-art results on Montezuma's Revenge, Pitfall!, and Private Eye by a significant margin.

Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog

Most deep reinforcement learning systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment.This is a critical shortcoming for applying RL to real-world problems where collecting data is expensive, and models must be tested offline before being deployed to interact with the environment -- e.g. systems that learn from human interaction.Thus, we develop a novel class of off-policy batch RL algorithms which use KL-control to penalize divergence from a pre-trained prior model of probable actions.This KL-constraint reduces extrapolation error, enabling effective offline learning, without exploration, from a fixed batch of data.We also use dropout-based uncertainty estimates to lower bound the target Q-values as a more efficient alternative to Double Q-Learning.This Way Off-Policy algorithm is tested on both traditional RL tasks from OpenAI Gym, and on the problem of open-domain dialog generation; a challenging reinforcement learning problem with a 20,000 dimensional action space.WOP allows for the extraction of multiple different reward functions post-hoc from collected human interaction data, and can learn effectively from all of these.We test real-world generalization by deploying dialog models live to converse with humans in an open-domain setting, and demonstrate that WOP achieves significant improvements over state-of-the-art prior methods in batch deep RL.

We show that KL-control from a pre-trained prior can allow RL models to learn from a static batch of collected data, without the ability to explore online in the environment.

Single Episode Policy Transfer in Reinforcement Learning

Transfer and adaptation to new unknown environmental dynamics is a key challenge for reinforcement learning.An even greater challenge is performing near-optimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience rollouts for adaptation.To achieve single episode transfer in a family of environments with related dynamics, we propose a general algorithm that optimizes a probe and an inference model to rapidly estimate underlying latent variables of test dynamics, which are then immediately used as input to a universal control policy.This modular approach enables integration of state-of-the-art algorithms for variational inference or RL.Moreover, our approach does not require access to rewards at test time, allowing it to perform in settings where existing adaptive approaches cannot.In diverse experimental domains with a single episode test constraint, our method significantly outperforms existing adaptive approaches and shows favorable performance against baselines for robust transfer.

Single episode policy transfer in a family of environments with related dynamics, via optimized probing for rapid inference of latent variables and immediate execution of a universal policy.

Graph Convolutional Network with Sequential Attention For Goal-Oriented Dialogue Systems

Domain specific goal-oriented dialogue systems typically require modeling three types of inputs, viz., the knowledge-base associated with the domain, the history of the conversation, which is a sequence of utterances and the current utterance for which the response needs to be generated.While modeling these inputs, current state-of-the-art models such as Mem2Seq typically ignore the rich structure inherent in the knowledge graph and the sentences in the conversation context.Inspired by the recent success of structure-aware Graph Convolutional Networks for various NLP tasks such as machine translation, semantic role labeling and document dating, we propose a memory augmented GCN for goal-oriented dialogues.Our model exploits the entity relation graph in a knowledge-base and the dependency graph associated with an utterance to compute richer representations for words and entities.Further, we take cognizance of the fact that in certain situations, such as, when the conversation is in a code-mixed language, dependency parsers may not be available.We show that in such situations we could use the global word co-occurrence graph and use it to enrich the representations of utterances.We experiment with the modified DSTC2 dataset and its recently released code-mixed versions in four languages and show that our method outperforms existing state-of-the-art methods, using a wide range of evaluation metrics.

We propose a Graph Convolutional Network based encoder-decoder model with sequential attention for goal-oriented dialogue systems.

SENSE: SEMANTICALLY ENHANCED NODE SEQUENCE EMBEDDING

Effectively capturing graph node sequences in the form of vector embeddings is critical to many applications.We achieve this by first learning vector embeddings of single graph nodes and then composing them to compactly represent node sequences.Specifically, we propose SENSE-S, a skip-gram based novel embedding mechanism, for single graph nodes that co-learns graph structure as well as their textual descriptions.We demonstrate that SENSE-S vectors increase the accuracy of multi-label classification tasks by up to 50% and link-prediction tasks by up to 78% under a variety of scenarios using real datasets.Based on SENSE-S, we next propose generic SENSE to compute composite vectors that represent a sequence of nodes, where preserving the node order is important.We prove that this approach is efficient in embedding node sequences, and our experiments on real data confirm its high accuracy in node order decoding.

Node sequence embedding mechanism that captures both graph and text properties.

Extreme Values are Accurate and Robust in Deep Networks

Recent evidence shows that convolutional neural networks are biased towards textures so that CNNs are non-robust to adversarial perturbations over textures, while traditional robust visual features like SIFT are designed to be robust across a substantial range of affine distortion, addition of noise, etc with the mimic of human perception nature.This paper aims to leverage good properties of SIFT to renovate CNN architectures towards better accuracy and robustness.We borrow the scale-space extreme value idea from SIFT, and propose EVPNet which contains three novel components to model the extreme values: parametric differences of Gaussian to extract extrema, truncated ReLU to suppress non-stable extrema and projected normalization layer to mimic PCA-SIFT like feature normalization.Experiments demonstrate that EVPNets can achieve similar or better accuracy than conventional CNNs, while achieving much better robustness on a set of adversarial attacks even without adversarial training.

This paper aims to leverage good properties of robust visual features like SIFT to renovate CNN architectures towards better accuracy and robustness.

Learnability for the Information Bottleneck

Compressed representations generalize better, which may be crucial when learning from limited or noisy labeled data.The Information Bottleneck method) provides an insightful and principled approach for balancing compression and prediction in representation learning.The IB objective I − βI employs a Lagrange multiplier β to tune this trade-off.However, there is little theoretical guidance for how to select β.There is also a lack of theoretical understanding about the relationship between β, the dataset, model capacity, and learnability.In this work, we show that if β is improperly chosen, learning cannot happen: the trivial representation P = P becomes the global minimum of the IB objective.We show how this can be avoided, by identifying a sharp phase transition between the unlearnable and the learnable which arises as β varies.This phase transition defines the concept of IB-Learnability.We prove several sufficient conditions for IB-Learnability, providing theoretical guidance for selecting β.We further show that IB-learnability is determined by the largest confident, typical, and imbalanced subset of the training examples.We give a practical algorithm to estimate the minimum β for a given dataset.We test our theoretical results on synthetic datasets, MNIST, and CIFAR10 with noisy labels, and make the surprising observation that accuracy may be non-monotonic in β.

Theory predicts the phase transition between unlearnable and learnable values of beta for the Information Bottleneck objective

MetaPoison: Learning to craft adversarial poisoning examples via meta-learning

We consider a new class of attacks on neural networks, in which the attacker takes control of a model by making small perturbations to a subset of its training data. We formulate the task of finding poisons as a bi-level optimization problem, which can be solved using methods borrowed from the meta-learning community. Unlike previous poisoning strategies, the meta-poisoning can poison networks that are trained from scratch using an initialization unknown to the attacker and transfer across hyperparameters.Further we show that our attacks are more versatile: they can cause misclassification of the target image into an arbitrarily chosen class.Our results show above 50% attack success rate when poisoning just 3-10% of the training dataset.

Generate corrupted training images that are imperceptible yet change CNN behavior on a target during any new training.

Learning Two-layer Neural Networks with Symmetric Inputs

We give a new algorithm for learning a two-layer neural network under a very general class of input distributions.Assuming there is a ground-truth two-layer networky = A \\sigma + \\xi,where A, W are weight matrices, \\xi represents noise, and the number of neurons in the hidden layer is no larger than the input or output, our algorithm is guaranteed to recover the parameters A, W of the ground-truth network.The only requirement on the input x is that it is symmetric, which still allows highly complicated and structured input.Our algorithm is based on the method-of-moments framework and extends several results in tensor decompositions.We use spectral algorithms to avoid the complicated non-convex optimization in learning neural networks.Experiments show that our algorithm can robustly learn the ground-truth neural network with a small number of samples for many symmetric input distributions.

We give an algorithm for learning a two-layer neural network with symmetric input distribution.

Interpretable and Pedagogical Examples

Teachers intentionally pick the most informative examples to show their students.However, if the teacher and student are neural networks, the examples that the teacher network learns to give, although effective at teaching the student, are typically uninterpretable.We show that training the student and teacher iteratively, rather than jointly, can produce interpretable teaching strategies."We evaluate interpretability by measuring the similarity of the teacher's emergent strategies to intuitive strategies in each domain and conducting human experiments to evaluate how effective the teacher's strategies are at teaching humans.", 'We show that the teacher network learns to select or generate interpretable, pedagogical examples to teach rule-based, probabilistic, boolean, and hierarchical concepts.

We show that training a student and teacher network iteratively, rather than jointly, can produce emergent, interpretable teaching strategies.

A Non-asymptotic comparison of SVRG and SGD: tradeoffs between compute and speed

Stochastic gradient descent, which trades off noisy gradient updates for computational efficiency, is the de-facto optimization algorithm to solve large-scale machine learning problems.SGD can make rapid learning progress by performing updates using subsampled training data, but the noisy updates also lead to slow asymptotic convergence. Several variance reduction algorithms, such as SVRG, introduce control variates to obtain a lower variance gradient estimate and faster convergence. Despite their appealing asymptotic guarantees, SVRG-like algorithms have not been widely adopted in deep learning.The traditional asymptotic analysis in stochastic optimization provides limited insight into training deep learning models under a fixed number of epochs.In this paper, we present a non-asymptotic analysis of SVRG under a noisy least squares regression problem.Our primary focus is to compare the exact loss of SVRG to that of SGD at each iteration t.We show that the learning dynamics of our regression model closely matches with that of neural networks on MNIST and CIFAR-10 for both the underparameterized and the overparameterized models.Our analysis and experimental results suggest there is a trade-off between the computational cost and the convergence speed in underparametrized neural networks.SVRG outperforms SGD after a few epochs in this regime.However, SGD is shown to always outperform SVRG in the overparameterized regime.

Non-asymptotic analysis of SGD and SVRG, showing the strength of each algorithm in convergence speed and computational cost, in both under-parametrized and over-parametrized settings.

Understanding Knowledge Distillation in Non-autoregressive Machine Translation

Non-autoregressive machine translation systems predict a sequence of output tokens in parallel, achieving substantial improvements in generation speed compared to autoregressive models.Existing NAT models usually rely on the technique of knowledge distillation, which creates the training data from a pretrained autoregressive model for better performance.Knowledge distillation is empirically useful, leading to large gains in accuracy for NAT models, but the reason for this success has, as of yet, been unclear.In this paper, we first design systematic experiments to investigate why knowledge distillation is crucial to NAT training.We find that knowledge distillation can reduce the complexity of data sets and help NAT to model the variations in the output data.Furthermore, a strong correlation is observed between the capacity of an NAT model and the optimal complexity of the distilled data for the best translation quality.Based on these findings, we further propose several approaches that can alter the complexity of data sets to improve the performance of NAT models.We achieve the state-of-the-art performance for the NAT-based models, and close the gap with the autoregressive baseline on WMT14 En-De benchmark.

We systematically examine why knowledge distillation is crucial to the training of non-autoregressive translation (NAT) models, and propose methods to further improve the distilled data to best match the capacity of an NAT model.

Stabilizing Transformers for Reinforcement Learning

Owing to their ability to both effectively integrate information over long time horizons and scale to massive amounts of data, self-attention architectures have recently shown breakthrough success in natural language processing, achieving state-of-the-art results in domains such as language modeling and machine translation."Harnessing the transformer's ability to process long time horizons of information could provide a similar performance boost in partially-observable reinforcement learning domains, but the large-scale transformers used in NLP have yet to be successfully applied to the RL setting.", 'In this work we demonstrate that the standard transformer architecture is difficult to optimize, which was previously observed in the supervised learning setting but becomes especially pronounced with RL objectives.We propose architectural modifications that substantially improve the stability and learning speed of the original Transformer and XL variant.The proposed architecture, the Gated Transformer-XL, surpasses LSTMs on challenging memory environments and achieves state-of-the-art results on the multi-task DMLab-30 benchmark suite, exceeding the performance of an external memory architecture.We show that the GTrXL, trained using the same losses, has stability and performance that consistently matches or exceeds a competitive LSTM baseline, including on more reactive tasks where memory is less critical.GTrXL offers an easy-to-train, simple-to-implement but substantially more expressive architectural alternative to the standard multi-layer LSTM ubiquitously used for RL agents in partially-observable environments.

We succeed in stabilizing transformers for training in the RL setting and demonstrate a large improvement over LSTMs on DMLab-30, matching an external memory architecture.

Noisy Collaboration in Knowledge Distillation

Knowledge distillation is an effective model compression technique in which a smaller model is trained to mimic a larger pretrained model.However in order to make these compact models suitable for real world deployment, not only dowe need to reduce the performance gap but also we need to make them more robust to commonly occurring and adversarial perturbations.Noise permeates every level of the nervous system, from the perception of sensory signals to thegeneration of motor responses.We therefore believe that noise could be a crucial element in improving neural networks training and addressing the apparently contradictory goals of improving both the generalization and robustness of themodel.Inspired by trial-to-trial variability in the brain that can result from multiple noise sources, we introduce variability through noise at either the input level or the supervision signals.Our results show that noise can improve both the generalization and robustness of the model.”Fickle Teacher” which uses dropout in teacher model as a source of response variation leads to significant generalization improvement.”Soft Randomization”, which matches the output distribution ofthe student model on the image with Gaussian noise to the output of the teacher on original image, improves the adversarial robustness manifolds compared to the student model trained with Gaussian noise.We further show the surprising effect of random label corruption on a model’s adversarial robustness.The study highlights the benefits of adding constructive noise in the knowledge distillation framework and hopes to inspire further work in the area.

Inspired by trial-to-trial variability in the brain that can result from multiple noise sources, we introduce variability through noise in the knowledge distillation framework and studied their effect on generalization and robustness.

Generalized Clustering by Learning to Optimize Expected Normalized Cuts

We introduce a novel end-to-end approach for learning to cluster in the absence of labeled examples.Our clustering objective is based on optimizing normalized cuts, a criterion which measures both intra-cluster similarity as well as inter-cluster dissimilarity.We define a differentiable loss function equivalent to the expected normalized cuts.Unlike much of the work in unsupervised deep learning, our trained model directly outputs final cluster assignments, rather than embeddings that need further processing to be usable.Our approach generalizes to unseen datasets across a wide variety of domains, including text, and image.Specifically, we achieve state-of-the-art results on popular unsupervised clustering benchmarks, outperforming the strongest baselines by up to 10.9%.Our generalization results are superior to the recent top-performing clustering approach with the ability to generalize.

We introduce a novel end-to-end approach for learning to cluster in the absence of labeled examples. We define a differentiable loss function equivalent to the expected normalized cuts.

On recognition of Cyrillic Text

We introduce the largest dataset for Cyrillic Handwritten Text Recognition and the first dataset for Cyrillic Text in the Wild Recognition, as well as suggest a method for recognizing Cyrillic Handwritten Text and Text in the Wild.Based on this approach, we develop a system that can reduce the document processing time for one of the largest mathematical competitions in Ukraine by 12 days and the amount of used paper by 0.5 ton.

We introduce several datasets for Cyrillic OCR and a method for its recognition

Learning Multi-facet Embeddings of Phrases and Sentences using Sparse Coding for Unsupervised Semantic Applications

Most deep learning for NLP represents each word with a single point or single-mode region in semantic space, while the existing multi-mode word embeddings cannot represent longer word sequences like phrases or sentences."We introduce a phrase representation where each phrase has a distinct set of multi-mode codebook embeddings to capture different semantic facets of the phrase's meaning.", 'The codebook embeddings can be viewed as the cluster centers which summarize the distribution of possibly co-occurring words in a pre-trained word embedding space.We propose an end-to-end trainable neural model that directly predicts the set of cluster centers from the input text sequence during test time.We find that the per-phrase/sentence codebook embeddings not only provide a more interpretable semantic representation but also outperform strong baselines on benchmark datasets for unsupervised phrase similarity, sentence similarity, hypernym detection, and extractive summarization.

We propose an unsupervised way to learn multiple embeddings for sentences and phrases

Sample Efficient Adaptive Text-to-Speech

We present a meta-learning approach for adaptive text-to-speech with few data.During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker.The aim of training is not to produce a neural network with fixed weights, which is then deployed as a TTS system.Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers.We introduce and benchmark three strategies: learning the speaker embedding while keeping the WaveNet core fixed, fine-tuning the entire architecture with stochastic gradient descent, and predicting the speaker embedding with a trained neural network encoder.The experiments show that these approaches are successful at adapting the multi-speaker neural network to new speakers, obtaining state-of-the-art results in both sample naturalness and voice similarity with merely a few minutes of audio data from new speakers.

Sample efficient algorithms to adapt a text-to-speech model to a new voice style with the state-of-the-art performance.