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We introduce and study minimax curriculum learning (MCL), a new method for adaptively selecting a sequence of training subsets for a succession of stages in machine learning. The subsets are encouraged to be small and diverse early on, and then larger, harder, and allowably more homogeneous in later stages. At each sta... | Minimax Curriculum Learning is a machine teaching method involving increasing desirable hardness and scheduled reducing diversity. |
Progress in probabilistic generative models has accelerated, developing richer models with neural architectures, implicit densities, and with scalable algorithms for their Bayesian inference. However, there has been limited progress in models that capture causal relationships, for example, how individual genetic factor... | Implicit models applied to causality and genetics |
Few-shot learning trains image classifiers over datasets with few examples per category.
It poses challenges for the optimization algorithms, which typically require many examples to fine-tune the model parameters for new categories.
Distance-learning-based approaches avoid the optimization issue by embedding the ... | Few-shot learning by exploiting the object-level relation to learn the image-level relation (similarity) |
Word embeddings are widely used in machine learning based natural language processing systems. It is common to use pre-trained word embeddings which provide benefits such as reduced training time and improved overall performance. There has been a recent interest in applying natural language processing techniques to pro... | Researchers exploring natural language processing techniques applied to source code are not using any form of pre-trained embeddings, we show that they should be. |
Recently, Approximate Policy Iteration (API) algorithms have achieved super-human proficiency in two-player zero-sum games such as Go, Chess, and Shogi without human data. These API algorithms iterate between two policies: a slow policy (tree search), and a fast policy (a neural network). In these two-player games, a r... | We solve the Rubik's Cube with pure reinforcement learning |
Answering compositional questions requiring multi-step reasoning is challenging for current models. We introduce an end-to-end differentiable model for interpreting questions, which is inspired by formal approaches to semantics. Each span of text is represented by a denotation in a knowledge graph, together with a vect... | We describe an end-to-end differentiable model for QA that learns to represent spans of text in the question as denotations in knowledge graph, by learning both neural modules for composition and the syntactic structure of the sentence. |
Deep learning software demands reliability and performance. However, many of the existing deep learning frameworks are software libraries that act as an unsafe DSL in Python and a computation graph interpreter. We present DLVM, a design and implementation of a compiler infrastructure with a linear algebra intermediate ... | We introduce a novel compiler infrastructure that addresses shortcomings of existing deep learning frameworks. |
In this work, we focus on the problem of grounding language by training an agent
to follow a set of natural language instructions and navigate to a target object
in a 2D grid environment. The agent receives visual information through raw
pixels and a natural language instruction telling what task needs to be achieve... | Attention based architecture for language grounding via reinforcement learning in a new customizable 2D grid environment |
Current end-to-end machine reading and question answering (Q\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q\&A architecture called QANet, which... | A simple architecture consisting of convolutions and attention achieves results on par with the best documented recurrent models. |
Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, mo... | We introduce Spherical CNNs, a convolutional network for spherical signals, and apply it to 3D model recognition and molecular energy regression. |
We propose a novel method that makes use of deep neural networks and gradient decent to perform automated design on complex real world engineering tasks. Our approach works by training a neural network to mimic the fitness function of a design optimization task and then, using the differential nature of the neural netw... | A method for performing automated design on real world objects such as heat sinks and wing airfoils that makes use of neural networks and gradient descent. |
Methods that align distributions by minimizing an adversarial distance between them have recently achieved impressive results. However, these approaches are difficult to optimize with gradient descent and they often do not converge well without careful hyperparameter tuning and proper initialization. We investigate whe... | We propose a dual version of the logistic adversarial distance for feature alignment and show that it yields more stable gradient step iterations than the min-max objective. |
There are many applications scenarios for which the computational
performance and memory footprint of the prediction phase of Deep
Neural Networks (DNNs) need to be optimized. Binary Deep Neural
Networks (BDNNs) have been shown to be an effective way of achieving
this objective. In this paper, we show how Con... | state-of-the-art computational performance implementation of binary neural networks |
Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization. In this paper, we propose a subset selection algorithm that is trainable with gradient based methods yet achieves near optimal performance via submodular optimization. We focus on the task of identifying ... | We propose a subset selection algorithm that is trainable with gradient based methods yet achieves near optimal performance via submodular optimization. |
The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which oftentimes involves cohesive clustering with a focus on instance relat... | We introduce hierarchically clustered representation learning (HCRL), which simultaneously optimizes representation learning and hierarchical clustering in the embedding space. |
We introduce a novel geometric perspective and unsupervised model augmentation framework for transforming traditional deep (convolutional) neural networks into adversarially robust classifiers. Class-conditional probability densities based on Bayesian nonparametric mixtures of factor analyzers (BNP-MFA) over the input ... | We develop a statistical-geometric unsupervised learning augmentation framework for deep neural networks to make them robust to adversarial attacks. |
Reinforcement learning in environments with large state-action spaces is challenging, as exploration can be highly inefficient. Even if the dynamics are simple, the optimal policy can be combinatorially hard to discover. In this work, we propose a hierarchical approach to structured exploration to improve the sample ef... | Make deep reinforcement learning in large state-action spaces more efficient using structured exploration with deep hierarchical policies. |
Much attention has been devoted recently to the generalization puzzle in deep learning: large, deep networks can generalize well, but existing theories bounding generalization error are exceedingly loose, and thus cannot explain this striking performance. Furthermore, a major hope is that knowledge may transfer across ... | We provide many insights into neural network generalization from the theoretically tractable linear case. |
We conduct a mathematical analysis on the Batch normalization (BN) effect on gradient backpropagation in residual network training in this work, which is believed to play a critical role in addressing the gradient vanishing/explosion problem. Specifically, by analyzing the mean and variance behavior of the input and th... | Batch normalisation maintains gradient variance throughout training, thus stabilizing optimization. |
To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images representative of 1,854 object categories. These representations predicted a latent similarity structure between objects, which captured most of the... | Human behavioral judgments are used to obtain sparse and interpretable representations of objects that generalize to other tasks |
We frame Question Answering (QA) as a Reinforcement Learning task, an approach that we call Active Question Answering.
We propose an agent that sits between the user and a black box QA system and learns to reformulate questions to elicit the best possible answers. The agent probes the system with, potentially many, ... | We propose an agent that sits between the user and a black box question-answering system and which learns to reformulate questions to elicit the best possible answers |
Most deep latent factor models choose simple priors for simplicity, tractability
or not knowing what prior to use. Recent studies show that the choice of
the prior may have a profound effect on the expressiveness of the model,
especially when its generative network has limited capacity. In this paper, we propose to ... | Learning Priors for Adversarial Autoencoders |
In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer. In the field of Natural Language Processing, ... | Generating text using sentence embeddings from Skip-Thought Vectors with the help of Generative Adversarial Networks. |
The novel \emph{Unbiased Online Recurrent Optimization} (UORO) algorithm allows for online learning of general recurrent computational graphs such as recurrent network models. It works in a streaming fashion and avoids backtracking through past activations and inputs. UORO is computationally as costly as \emph{Truncate... | Introduces an online, unbiased and easily implementable gradient estimate for recurrent models. |
We present a deep learning-based method for super-resolving coarse (low-resolution) labels assigned to groups of image pixels into pixel-level (high-resolution) labels, given the joint distribution between those low- and high-resolution labels. This method involves a novel loss function that minimizes the distance betw... | Super-resolving coarse labels into pixel-level labels, applied to aerial imagery and medical scans. |
We propose a novel framework for combining datasets via alignment of their associated intrinsic dimensions. Our approach assumes that the two datasets are sampled from a common latent space, i.e., they measure equivalent systems. Thus, we expect there to exist a natural (albeit unknown) alignment of the data manifolds ... | We propose a method for aligning the latent features learned from different datasets using harmonic correlations. |
Reinforcement learning (RL) has proven to be a powerful paradigm for deriving complex behaviors from simple reward signals in a wide range of environments. When applying RL to continuous control agents in simulated physics environments, the body is usually considered to be part of the environment. However, during evolu... | Evolving the shape of the body in RL controlled agents improves their performance (and help learning) |
Many practical reinforcement learning problems contain catastrophic states that the optimal policy visits infrequently or never. Even on toy problems, deep reinforcement learners periodically revisit these states, once they are forgotten under a new policy. In this paper, we introduce intrinsic fear, a learned reward s... | Shape reward with intrinsic motivation to avoid catastrophic states and mitigate catastrophic forgetting. |
Convolution is an efficient technique to obtain abstract feature representations using hierarchical layers in deep networks. Although performing convolution in Euclidean geometries is fairly straightforward, its extension to other topological spaces---such as a sphere S^2 or a unit ball B^3---entails unique challenges.... | A novel convolution operator for automatic representation learning inside unit ball |
Learning in environments with large state and action spaces, and sparse rewards, can hinder a Reinforcement Learning (RL) agent’s learning through trial-and-error. For instance, following natural language instructions on the Web (such as booking a flight ticket) leads to RL settings where input vocabulary and number of... | We train reinforcement learning policies using reward augmentation, curriculum learning, and meta-learning to successfully navigate web pages. |
Labeled text classification datasets are typically only available in a few select languages. In order to train a model for e.g news categorization in a language $L_t$ without a suitable text classification dataset there are two options. The first option is to create a new labeled dataset by hand, and the second option ... | Cross Language Text Classification by universal encoding |
Syntax is a powerful abstraction for language understanding. Many downstream tasks require segmenting input text into meaningful constituent chunks (e.g., noun phrases or entities); more generally, models for learning semantic representations of text benefit from integrating syntax in the form of parse trees (e.g., tre... | In this work we propose deep inside-outside recursive auto-encoders(DIORA) a fully unsupervised method of discovering syntax while simultaneously learning representations for discovered constituents. |
Careful tuning of the learning rate, or even schedules thereof, can be crucial to effective neural net training. There has been much recent interest in gradient-based meta-optimization, where one tunes hyperparameters, or even learns an optimizer, in order to minimize the expected loss when the training procedure is un... | We investigate the bias in the short-horizon meta-optimization objective. |
Mainstream captioning models often follow a sequential structure to generate cap-
tions, leading to issues such as introduction of irrelevant semantics, lack of diversity
in the generated captions, and inadequate generalization performance. In this paper,
we present an alternative paradigm for image captioning, whic... | a hierarchical and compositional way to generate captions |
While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data. Measures for characterizing and monitoring structural properties, however, have not been developed. In this work, we propose neural persistence, a complexity measure for ne... | We develop a new topological complexity measure for deep neural networks and demonstrate that it captures their salient properties. |
Deep neural networks (DNNs) are vulnerable to adversarial examples, which are carefully crafted instances aiming to cause prediction errors for DNNs. Recent research on adversarial examples has examined local neighborhoods in the input space of DNN models. However, previous work has limited what regions to consider, fo... | Looking at decision boundaries around an input gives you more information than a fixed small neighborhood |
Machine learning models are usually tuned by nesting optimization of model weights inside the optimization of hyperparameters. We give a method to collapse this nested optimization into joint stochastic optimization of both weights and hyperparameters. Our method trains a neural network to output approximately opti... | We train a neural network to output approximately optimal weights as a function of hyperparameters. |
Estimating covariances between financial assets plays an important role in risk management. In practice, when the sample size is small compared to the number of variables, the empirical estimate is known to be very unstable. Here, we propose a novel covariance estimator based on the Gaussian Process Latent Variable Mod... | Covariance matrix estimation of financial assets with Gaussian Process Latent Variable Models |
We study how, in generative adversarial networks, variance in the discriminator's output affects the generator's ability to learn the data distribution. In particular, we contrast the results from various well-known techniques for training GANs when the discriminator is near-optimal and updated multiple times per updat... | We introduce meta-adversarial learning, a new technique to regularize GANs, and propose a training method by explicitly controlling the discriminator's output distribution. |
We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent’s policy can be used to aid efficient exploration. The parameters of the noise are learned with gradient descent along with the remaining network weights. NoisyNe... | A deep reinforcement learning agent with parametric noise added to its weights can be used to aid efficient exploration. |
Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. W... | "Active Neural Localizer", a fully differentiable neural network that learns to localize efficiently using deep reinforcement learning. |
Machine translation is an important real-world application, and neural network-based AutoRegressive Translation (ART) models have achieved very promising accuracy. Due to the unparallelizable nature of the autoregressive factorization, ART models have to generate tokens one by one during decoding and thus suffer from h... | We develop a training algorithm for non-autoregressive machine translation models, achieving comparable accuracy to strong autoregressive baselines, but one order of magnitude faster in inference. |
Artificial neural networks are built on the basic operation of linear combination and non-linear activation function. Theoretically this structure can approximate any continuous function with three layer architecture. But in practice learning the parameters of such network can be hard. Also the choice of activation fu... | Using mophological operation (dilation and erosion) we have defined a class of network which can approximate any continious function. |
With the rapidly scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for efficient deployment. This work aims to advance the compression beyond the weights to the activations of DNNs. We propose the Integral Pruning (IP) technique... | This work advances DNN compression beyond the weights to the activations by integrating the activation pruning with the weight pruning. |
The Variational Auto Encoder (VAE) is a popular generative
latent variable model that is often
applied for representation learning.
Standard VAEs assume continuous valued
latent variables and are trained by maximization
of the evidence lower bound (ELBO). Conventional methods obtain a
differentiable estimate of th... | We propose an easy method to train Variational Auto Encoders (VAE) with discrete latent representations, using importance sampling |
Distributed computing can significantly reduce the training time of neural networks. Despite its potential, however, distributed training has not been widely adopted: scaling the training process is difficult, and existing SGD methods require substantial tuning of hyperparameters and learning schedules to achieve suffi... | A new distributed asynchronous SGD algorithm that achieves state-of-the-art accuracy on existing architectures without any additional tuning or overhead. |
This paper proposes a novel approach to train deep neural networks by unlocking the layer-wise dependency of backpropagation training. The approach employs additional modules called local critic networks besides the main network model to be trained, which are used to obtain error gradients without complete feedforward ... | We propose a new learning algorithm of deep neural networks, which unlocks the layer-wise dependency of backpropagation. |
\emph{Truncated Backpropagation Through Time} (truncated BPTT, \cite{jaeger2002tutorial}) is a widespread method for learning recurrent computational graphs. Truncated BPTT keeps the computational benefits of \emph{Backpropagation Through Time} (BPTT \cite{werbos:bptt}) while relieving the need for a complete backtrack... | Provides an unbiased version of truncated backpropagation by sampling truncation lengths and reweighting accordingly. |
Graph convolutional networks (GCNs) have been widely used for classifying graph nodes in the semi-supervised setting.
Previous works have shown that GCNs are vulnerable to the perturbation on adjacency and feature matrices of existing nodes. However, it is unrealistic to change the connections of existing nodes in ma... | non-targeted and targeted attack on GCN by adding fake nodes |
Transfer learning aims to solve the data sparsity for a specific domain by applying information of another domain. Given a sequence (e.g. a natural language sentence), the transfer learning, usually enabled by recurrent neural network (RNN), represent the sequential information transfer. RNN uses a chain of repeating c... | Transfer learning for sequence via learning to align cell-level information across domains. |
Addressing uncertainty is critical for autonomous systems to robustly adapt to the real world. We formulate the problem of model uncertainty as a continuous Bayes-Adaptive Markov Decision Process (BAMDP), where an agent maintains a posterior distribution over latent model parameters given a history of observations and ... | We formulate model uncertainty in Reinforcement Learning as a continuous Bayes-Adaptive Markov Decision Process and present a method for practical and scalable Bayesian policy optimization. |
For many evaluation metrics commonly used as benchmarks for unconditional image generation, trivially memorizing the training set attains a better score than models which are considered state-of-the-art; we consider this problematic.
We clarify a necessary condition for an evaluation metric not to behave this way: est... | We argue that GAN benchmarks must require a large sample from the model to penalize memorization and investigate whether neural network divergences have this property. |
Conventional methods model open domain dialogue generation as a black box through end-to-end learning from large scale conversation data. In this work, we make the first step to open the black box by introducing dialogue acts into open domain dialogue generation. The dialogue acts are generally designed and reveal how ... | open domain dialogue generation with dialogue acts |
We discuss the feasibility of the following learning problem: given unmatched samples from two domains and nothing else, learn a mapping between the two, which preserves semantics. Due to the lack of paired samples and without any definition of the semantic information, the problem might seem ill-posed. Specifically, i... | Our hypothesis is that given two domains, the lowest complexity mapping that has a low discrepancy approximates the target mapping. |
We present a novel approach for the certification of neural networks against adversarial perturbations which combines scalable overapproximation methods with precise (mixed integer) linear programming. This results in significantly better precision than state-of-the-art verifiers on challenging feedforward and convolut... | We refine the over-approximation results from incomplete verifiers using MILP solvers to prove more robustness properties than state-of-the-art. |
A distinct commonality between HMMs and RNNs is that they both learn hidden representations for sequential data. In addition, it has been noted that the backward computation of the Baum-Welch algorithm for HMMs is a special case of the back-propagation algorithm used for neural networks (Eisner (2016)). Do these observ... | Are HMMs a special case of RNNs? We investigate a series of architectural transformations between HMMs and RNNs, both through theoretical derivations and empirical hybridization and provide new insights. |
Deep neural networks have been tremendously successful in a number of tasks.
One of the main reasons for this is their capability to automatically
learn representations of data in levels of abstraction,
increasingly disentangling the data as the internal transformations are applied.
In this paper we propose a novel... | We propose a novel regularization method that penalize covariance between dimensions of the hidden layers in a network. |
This report introduces a training and recognition scheme, in which classification is realized via class-wise discerning. Trained with datasets whose labels are randomly shuffled except for one class of interest, a neural network learns class-wise parameter values, and remolds itself from a feature sorter into feature f... | The proposed scheme mimics the classification process mediated by a series of one component picking. |
A long-held conventional wisdom states that larger models train more slowly when using gradient descent. This work challenges this widely-held belief, showing that larger models can potentially train faster despite the increasing computational requirements of each training step. In particular, we study the effect of ne... | Empirically shows that larger models train in fewer training steps, because all factors in weight space traversal improve. |
Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques harness neural networks to learn directly from examples of buggy programs and their fixes. In this work, we consider a recently identified class of bugs called va... | Multi-headed Pointer Networks for jointly learning to localize and repair Variable Misuse bugs |
Classification and clustering have been studied separately in machine learning and computer vision. Inspired by the recent success of deep learning models in solving various vision problems (e.g., object recognition, semantic segmentation) and the fact that humans serve as the gold standard in assessing clustering algo... | Human-like Clustering with CNNs |
Instancewise feature scoring is a method for model interpretation, which yields, for each test instance, a vector of importance scores associated with features. Methods based on the Shapley score have been proposed as a fair way of computing feature attributions, but incur an exponential complexity in the number of fea... | We develop two linear-complexity algorithms for model-agnostic model interpretation based on the Shapley value, in the settings where the contribution of features to the target is well-approximated by a graph-structured factorization. |
According to parallel distributed processing (PDP) theory in psychology, neural networks (NN) learn distributed rather than interpretable localist representations. This view has been held so strongly that few researchers have analysed single units to determine if this assumption is correct. However, recent results from... | Local codes have been found in feed-forward neural networks |
Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches however primarily focus on simple link structure between a finite set of entities, ignoring the variety of data types that are often used in relational databases, such as tex... | Extending relational modeling to support multimodal data using neural encoders. |
An ensemble of neural networks is known to be more robust and accurate than an individual network, however usually with linearly-increased cost in both training and testing.
In this work, we propose a two-stage method to learn Sparse Structured Ensembles (SSEs) for neural networks.
In the first stage, we run SG-MCMC... | Propose a novel method by integrating SG-MCMC sampling, group sparse prior and network pruning to learn Sparse Structured Ensemble (SSE) with improved performance and significantly reduced cost than traditional methods. |
This paper introduces a new framework for data efficient and versatile learning. Specifically:
1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods.
2) W... | Novel framework for meta-learning that unifies and extends a broad class of existing few-shot learning methods. Achieves strong performance on few-shot learning benchmarks without requiring iterative test-time inference. |
In recent years, softmax together with its fast approximations has become the de-facto loss function for deep neural networks with multiclass predictions. However, softmax is used in many problems that do not fully fit the multiclass framework and where the softmax assumption of mutually exclusive outcomes can lead to ... | Defining a partially mutual exclusive softmax loss for postive data and implementing a cooperative based sampling scheme |
Over the past few years, various tasks involving videos such as classification, description, summarization and question answering have received a lot of attention. Current models for these tasks compute an encoding of the video by treating it as a sequence of images and going over every image in the sequence, which bec... | Teacher-Student framework for efficient video classification using fewer frames |
Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. ... | A unified statistical view of the broad class of deep generative models |
Deep neural networks have demonstrated promising prediction and classification performance on many healthcare applications. However, the interpretability of those models are often lacking. On the other hand, classical interpretable models such as rule lists or decision trees do not lead to the same level of accuracy as... | a method combining rule list learning and prototype learning |
Generative Adversarial Networks (GANs) are powerful tools for realistic image generation. However, a major drawback of GANs is that they are especially hard to train, often requiring large amounts of data and long training time. In this paper we propose the Deli-Fisher GAN, a GAN that generates photo-realistic images b... | This paper proposes a new Generative Adversarial Network that is more stable, more efficient, and produces better images than those of status-quo |
Recent work on encoder-decoder models for sequence-to-sequence mapping has shown that integrating both temporal and spatial attentional mechanisms into neural networks increases the performance of the system substantially. We report on a new modular network architecture that applies an attentional mechanism not on temp... | We introduce a modular multi-sensor network architecture with an attentional mechanism that enables dynamic sensor selection on real-world noisy data from CHiME-3. |
Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints. Cloud-based training schemes provide beneficial services but suffer from potential privacy risks due to excessive user data collection. To enable cloud-based ... | To enable cloud-based DNN training while protecting the data privacy simultaneously, we propose to leverage the intermediate data representations, which is achieved by splitting the DNNs and deploying them separately onto local platforms and the cloud. |
Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to produce realistic real-valued multi-dimensional time series, with an emphasis on their appli... | Conditional recurrent GANs for real-valued medical sequences generation, showing novel evaluation approaches and an empirical privacy analysis. |
Emphasis effects – visual changes that make certain elements more
prominent – are commonly used in information visualization to draw
the user’s attention or to indicate importance. Although theoretical
frameworks of emphasis exist (that link visually diverse emphasis
effects through the idea of visual prominence co... | Our studies and empirical models provide valuable new information for designers who want to understand and control how emphasis effects will be perceived by users |
Memory Network based models have shown a remarkable progress on the task of relational reasoning.
Recently, a simpler yet powerful neural network module called Relation Network (RN) has been introduced.
Despite its architectural simplicity, the time complexity of relation network grows quadratically with data, hence... | A simple reasoning architecture based on the memory network (MemNN) and relation network (RN), reducing the time complexity compared to the RN and achieving state-of-the-are result on bAbI story based QA and bAbI dialog. |
We investigate in this paper the architecture of deep convolutional networks. Building on existing state of the art models, we propose a reconfiguration of the model parameters into several parallel branches at the global network level, with each branch being a standalone CNN. We show that this arrangement is an effici... | We show that splitting a neural network into parallel branches improves performance and that proper coupling of the branches improves performance even further. |
Convolutional Neural Networks (CNN) are very popular in many fields including computer vision, speech recognition, natural language processing, to name a few. Though deep learning leads to groundbreaking performance in these domains, the networks used are very demanding computationally and are far from real-time even o... | Combine noise injection, gradual quantization and activation clamping learning to achieve state-of-the-art 3,4 and 5 bit quantization |
In complex transfer learning scenarios new tasks might not be tightly linked to previous tasks. Approaches that transfer information contained only in the final parameters of a source model will therefore struggle. Instead, transfer learning at at higher level of abstraction is needed. We propose Leap, a framework that... | We propose Leap, a framework that transfers knowledge across learning processes by minimizing the expected distance the training process travels on a task's loss surface. |
Given an existing trained neural network, it is often desirable to learn new capabilities without hindering performance of those already learned. Existing approaches either learn sub-optimal solutions, require joint training, or incur a substantial increment in the number of parameters for each added task, typically as... | An alternative to transfer learning that learns faster, requires much less parameters (3-13 %), usually achieves better results and precisely preserves performance on old tasks. |
High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications. This paper presents a general technique toward 8-bit low precision inference of convolutional neural networks, including 1) channel-wise scale factors of weights, especially for depthwise con... | We present a general technique toward 8-bit low precision inference of convolutional neural networks. |
Recent approaches have successfully demonstrated the benefits of learning the parameters of shallow networks in hyperbolic space. We extend this line of work by imposing hyperbolic geometry on the embeddings used to compute the ubiquitous attention mechanisms for different neural networks architectures. By only changin... | We propose to incorporate inductive biases and operations coming from hyperbolic geometry to improve the attention mechanism of the neural networks. |
We present a method for evaluating the sensitivity of deep reinforcement learning (RL) policies. We also formulate a zero-sum dynamic game for designing robust deep reinforcement learning policies. Our approach mitigates the brittleness of policies when agents are trained in a simulated environment and are later expose... | This paper demonstrates how H-infinity control theory can help better design robust deep policies for robot motor taks |
Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers. In this paper, we provide a quantitative analysis of the robustness of classifiers to universal perturbations, a... | Analysis of vulnerability of classifiers to universal perturbations and relation to the curvature of the decision boundary. |
Behavioral skills or policies for autonomous agents are conventionally learned from reward functions, via reinforcement learning, or from demonstrations, via imitation learning. However, both modes of task specification have their disadvantages: reward functions require manual engineering, while demonstrations require ... | We propose a meta-learning method for interactively correcting policies with natural language. |
Deep generative models such as Generative Adversarial Networks (GANs) and
Variational Auto-Encoders (VAEs) are important tools to capture and investigate
the properties of complex empirical data. However, the complexity of their inner
elements makes their functionment challenging to assess and modify. In this
respe... | We investigate the modularity of deep generative models. |
Relational databases store a significant amount of the worlds data. However, accessing this data currently requires users to understand a query language such as SQL. We propose Seq2SQL, a deep neural network for translating natural language questions to corresponding SQL queries. Our model uses rewards from in the loop... | We introduce Seq2SQL, which translates questions to SQL queries using rewards from online query execution, and WikiSQL, a SQL table/question/query dataset orders of magnitude larger than existing datasets. |
We introduce Explainable Adversarial Learning, ExL, an approach for training neural networks that are intrinsically robust to adversarial attacks. We find that the implicit generative modeling of random noise with the same loss function used during posterior maximization, improves a model's understanding of the data ma... | Noise modeling at the input during discriminative training improves adversarial robustness. Propose PCA based evaluation metric for adversarial robustness |
We propose a method which can visually explain the classification decision of deep neural networks (DNNs). There are many proposed methods in machine learning and computer vision seeking to clarify the decision of machine learning black boxes, specifically DNNs. All of these methods try to gain insight into why the n... | A method to answer "why not class B?" for explaining deep networks |
We flip the usual approach to study invariance and robustness of neural networks by considering the non-uniqueness and instability of the inverse mapping. We provide theoretical and numerical results on the inverse of ReLU-layers. First, we derive a necessary and sufficient condition on the existence of invariance that... | We analyze the invertibility of deep neural networks by studying preimages of ReLU-layers and the stability of the inverse. |
While deep learning has led to remarkable results on a number of challenging problems, researchers have discovered a vulnerability of neural networks in adversarial settings, where small but carefully chosen perturbations to the input can make the models produce extremely inaccurate outputs. This makes these models par... | Adversarial training of ensembles provides robustness to adversarial examples beyond that observed in adversarially trained models and independently-trained ensembles thereof. |
Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve performance, but often suffers from task interference which reduces the benefits of transfer. To address this issue we introduce the routing network paradigm, a novel neural network and training algorithm. A routing network is a ... | routing networks: a new kind of neural network which learns to adaptively route its input for multi-task learning |
We propose a practical method for $L_0$ norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. AIC and BIC, well-known ... | We show how to optimize the expected L_0 norm of parametric models with gradient descent and introduce a new distribution that facilitates hard gating. |
Recently popularized graph neural networks achieve the state-of-the-art accuracy on a number of standard benchmark datasets for graph-based semi-supervised learning, improving significantly over existing approaches. These architectures alternate between a propagation layer that aggregates the hidden states of the local... | We propose a novel attention-based interpretable Graph Neural Network architecture which outperforms the current state-of-the-art Graph Neural Networks in standard benchmark datasets |
Modern generative models are usually designed to match target distributions directly in the data space, where the intrinsic dimensionality of data can be much lower than the ambient dimensionality. We argue that this discrepancy may contribute to the difficulties in training generative models. We therefore propose to m... | A framework for training autoencoder-based generative models, with non-adversarial losses and unrestricted neural network architectures. |
The quality of the representations achieved by embeddings is determined by how well the geometry of the embedding space matches the structure of the data.
Euclidean space has been the workhorse for embeddings; recently hyperbolic and spherical spaces have gained popularity due to their ability to better embed new type... | Product manifold embedding spaces with heterogenous curvature yield improved representations compared to traditional embedding spaces for a variety of structures. |
Synthesizing user-intended programs from a small number of input-output exam-
ples is a challenging problem with several important applications like spreadsheet
manipulation, data wrangling and code refactoring. Existing synthesis systems
either completely rely on deductive logic techniques that are extensively hand... | We integrate symbolic (deductive) and statistical (neural-based) methods to enable real-time program synthesis with almost perfect generalization from 1 input-output example. |
Variational auto-encoders
(VAEs) offer a tractable approach when performing approximate inference in otherwise intractable generative models. However, standard VAEs often produce latent codes that are disperse and lack interpretability, thus making the resulting representations unsuitable for auxiliary tasks (e.g. cla... | We explore the intersection of VAEs and sparse coding. |
A widely observed phenomenon in deep learning is the degradation problem: increasing
the depth of a network leads to a decrease in performance on both test and training data. Novel architectures such as ResNets and Highway networks have addressed this issue by introducing various flavors of skip-connections or gating ... | Phasing out skip-connections in a principled manner avoids degradation in deep feed-forward networks. |
Deep learning is becoming more widespread in its application due to its power in solving complex classification problems. However, deep learning models often require large memory and energy consumption, which may prevent them from being deployed effectively on embedded platforms, limiting their applications. This work ... | Compression of Deep neural networks deployed on embedded device. |
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