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Normalising Flows (NFs) are a class of likelihood-based generative models that have recently gained popularity. They are based on the idea of transforming a simple density into that of the data. We seek to better understand this class of models, and how they compare to previously proposed techniques for generative mode... | We explore the relationship between Normalising Flows and Variational- and Denoising Autoencoders, and propose a novel model that generalises them. | 200 | scitldr |
We investigate methods to efficiently learn diverse strategies in reinforcement learning for a generative structured prediction problem: query reformulation. In the proposed framework an agent consists of multiple specialized sub-agents and a meta-agent that learns to aggregate the answers from sub-agents to produce a ... | We use reinforcement learning for query reformulation on two tasks and surprisingly find that when training multiple agents diversity of the reformulations is more important than specialisation. | 201 | scitldr |
We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through safe policies, i.e.,~policies that keep the agent in desirable situations, both during training and at convergence. We formulate these problems as {\em constrained} Markov decisio... | A general framework for incorporating long-term safety constraints in policy-based reinforcement learning | 202 | scitldr |
Generative networks are known to be difficult to assess. Recent works on generative models, especially on generative adversarial networks, produce nice samples of varied categories of images. But the validation of their quality is highly dependent on the method used. A good generator should generate data which contain ... | Evaluating generative networks through their data augmentation capacity on discrimative models. | 203 | scitldr |
We propose Automating Science Journalism (ASJ), the process of producing a press release from a scientific paper, as a novel task that can serve as a new benchmark for neural abstractive summarization. ASJ is a challenging task as it requires long source texts to be summarized to long target texts, while also paraphras... | New: application of seq2seq modelling to automating sciene journalism; highly abstractive dataset; transfer learning tricks; automatic evaluation measure. | 204 | scitldr |
The interpretability of an AI agent's behavior is of utmost importance for effective human-AI interaction. To this end, there has been increasing interest in characterizing and generating interpretable behavior of the agent. An alternative approach to guarantee that the agent generates interpretable behavior would be t... | We present an approach to redesign the environment such that uninterpretable agent behaviors are minimized or eliminated. | 205 | scitldr |
Inference models, which replace an optimization-based inference procedure with a learned model, have been fundamental in advancing Bayesian deep learning, the most notable example being variational auto-encoders (VAEs). In this paper, we propose iterative inference models, which learn how to optimize a variational lowe... | We propose a new class of inference models that iteratively encode gradients to estimate approximate posterior distributions. | 206 | scitldr |
In artificial neural networks trained with gradient descent, the weights used for processing stimuli are also used during backward passes to calculate gradients. For the real brain to approximate gradients, gradient information would have to be propagated separately, such that one set of synaptic weights is used for pr... | We present a learning rule for feedback weights in a spiking neural network that addresses the weight transport problem. | 207 | scitldr |
Variational inference (VI) methods and especially variational autoencoders (VAEs) specify scalable generative models that enjoy an intuitive connection to manifold learning --- with many default priors the posterior/likelihood pair $q(z|x)$/$p(x|z)$ can be viewed as an approximate homeomorphism (and its inverse) betwee... | We combine variational inference and manifold learning (specifically VAEs and diffusion maps) to build a generative model based on a diffusion random walk on a data manifold; we generate samples by drawing from the walk's stationary distribution. | 208 | scitldr |
While deep learning and deep reinforcement learning systems have demonstrated impressive in domains such as image classification, game playing, and robotic control, data efficiency remains a major challenge, particularly as these algorithms learn individual tasks from scratch. Multi-task learning has emerged as a promi... | We develop a simple and general approach for avoiding interference between gradients from different tasks, which improves the performance of multi-task learning in both the supervised and reinforcement learning domains. | 209 | scitldr |
In this paper we propose to view the acceptance rate of the Metropolis-Hastings algorithm as a universal objective for learning to sample from target distribution -- given either as a set of samples or in the form of unnormalized density. This point of view unifies the goals of such approaches as Markov Chain Monte Car... | Learning to sample via lower bounding the acceptance rate of the Metropolis-Hastings algorithm | 210 | scitldr |
This paper proposes a self-supervised learning approach for video features that in significantly improved performance on downstream tasks (such as video classification, captioning and segmentation) compared to existing methods. Our method extends the BERT model for text sequences to the case of sequences of real-valued... | Generalized BERT for continuous and cross-modal inputs; state-of-the-art self-supervised video representations. | 211 | scitldr |
We present a generic dynamic architecture that employs a problem specific differentiable forking mechanism to leverage discrete logical information about the problem data structure. We adapt and apply our model to CLEVR Visual Question Answering, giving rise to the DDRprog architecture; compared to previous approaches,... | A generic dynamic architecture that employs a problem specific differentiable forking mechanism to encode hard data structure assumptions. Applied to CLEVR VQA and expression evaluation. | 212 | scitldr |
We propose Support-guided Adversarial Imitation Learning (SAIL), a generic imitation learning framework that unifies support estimation of the expert policy with the family of Adversarial Imitation Learning (AIL) algorithms. SAIL addresses two important challenges of AIL, including the implicit reward bias and potentia... | We unify support estimation with the family of Adversarial Imitation Learning algorithms into Support-guided Adversarial Imitation Learning, a more robust and stable imitation learning framework. | 213 | scitldr |
We consider the task of few shot link prediction, where the goal is to predict missing edges across multiple graphs using only a small sample of known edges. We show that current link prediction methods are generally ill-equipped to handle this task---as they cannot effectively transfer knowledge between graphs in a mu... | We apply gradient based meta-learning to the graph domain and introduce a new graph specific transfer function to further bootstrap the process. | 214 | scitldr |
Generative neural networks map a standard, possibly distribution to a complex high-dimensional distribution, which represents the real world data set. However, a determinate input distribution as well as a specific architecture of neural networks may impose limitations on capturing the diversity in the high dimensional... | We propose a new method to incrementally train a mixture generative model to approximate the information projection of the real data distribution. | 215 | scitldr |
Generative priors have become highly effective in solving inverse problems including denoising, inpainting, and reconstruction from few and noisy measurements. With a generative model we can represent an image with a much lower dimensional latent codes. In the context of compressive sensing, if the unknown image belong... | Recover videos from compressive measurements by learning a low-dimensional (low-rank) representation directly from measurements while training a deep generator. | 216 | scitldr |
Magnitude-based pruning is one of the simplest methods for pruning neural networks. Despite its simplicity, magnitude-based pruning and its variants demonstrated remarkable performances for pruning modern architectures. Based on the observation that the magnitude-based pruning indeed minimizes the Frobenius distortion ... | We study a multi-layer generalization of the magnitude-based pruning. | 217 | scitldr |
Recent literature has demonstrated promising on the training of Generative Adversarial Networks by employing a set of discriminators, as opposed to the traditional game involving one generator against a single adversary. Those methods perform single-objective optimization on some simple consolidation of the losses, e.g... | We introduce hypervolume maximization for training GANs with multiple discriminators, showing performance improvements in terms of sample quality and diversity. | 218 | scitldr |
Designing of search space is a critical problem for neural architecture search (NAS) algorithms. We propose a fine-grained search space comprised of atomic blocks, a minimal search unit much smaller than the ones used in recent NAS algorithms. This search space facilitates direct selection of channel numbers and kernel... | A new state-of-the-art on Imagenet for mobile setting | 219 | scitldr |
We introduce Lyceum, a high-performance computational ecosystem for robotlearning. Lyceum is built on top of the Julia programming language and theMuJoCo physics simulator, combining the ease-of-use of a high-level program-ming language with the performance of native C. Lyceum is up to 10-20Xfaster compared to other po... | A high performance robotics simulation and algorithm development framework. | 220 | scitldr |
There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domain in the presence of a domain-shift. Existing domain adaptation (DA) approaches are not equipped for practical DA scenarios as a of their re... | A novel unsupervised domain adaptation paradigm - performing adaptation without accessing the source data ('source-free') and without any assumption about the source-target category-gap ('universal'). | 221 | scitldr |
One of the long-standing challenges in Artificial Intelligence for learning goal-directed behavior is to build a single agent which can solve multiple tasks. Recent progress in multi-task learning for goal-directed sequential problems has been in the form of distillation based learning wherein a student network learns ... | Letting a meta-learner decide the task to train on for an agent in a multi-task setting improves multi-tasking ability substantially | 222 | scitldr |
Numerous machine reading comprehension (MRC) datasets often involve manual annotation, requiring enormous human effort, and hence the size of the dataset remains significantly smaller than the size of the data available for unsupervised learning. Recently, researchers proposed a model for generating synthetic question-... | We propose Answer-containing Sentence Generation (ASGen), a novel pre-training method for generating synthetic data for machine reading comprehension. | 223 | scitldr |
Deep neural networks (DNNs) dominate current research in machine learning. Due to massive GPU parallelization DNN training is no longer a bottleneck, and large models with many parameters and high computational effort lead common benchmark tables. In contrast, embedded devices have a very limited capability. As a , bot... | Soft quantization approach to learn pure fixed-point representations of deep neural networks | 224 | scitldr |
We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks. Existing approaches conventionally learn full model parameters independently and then compress them via \emph{ad hoc} processing such as model pruning or filter factorization. Alternatively, WSNet ... | We present a novel network architecture for learning compact and efficient deep neural networks | 225 | scitldr |
Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models. However, in such visual perception pipeline the detected objects must also be tracked, in a process called Multiple Object Tracking (MOT), to bu... | We study the adversarial machine learning attacks against the Multiple Object Tracking mechanisms for the first time. | 226 | scitldr |
Self-supervised learning (SlfSL), aiming at learning feature representations through ingeniously designed pretext tasks without human annotation, has achieved compelling progress in the past few years. Very recently, SlfSL has also been identified as a promising solution for semi-supervised learning (SemSL) since it of... | Coupling semi-supervised learning with self-supervised learning and explicitly modeling the self-supervised task conditioned on the semi-supervised one | 227 | scitldr |
Ranking is a central task in machine learning and information retrieval. In this task, it is especially important to present the user with a slate of items that is appealing as a whole. This in turn requires taking into account interactions between items, since intuitively, placing an item on the slate affects the deci... | A pointer network architecture for re-ranking items, learned from click-through logs. | 228 | scitldr |
While momentum-based methods, in conjunction with the stochastic gradient descent, are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In practice, the momentum parameter is often chosen in a heuristic fashion with little theoreti... | Stochastic gradient method with momentum generalizes. | 229 | scitldr |
Imitation learning from demonstrations usually relies on learning a policy from trajectories of optimal states and actions. However, in real life expert demonstrations, often the action information is missing and only state trajectories are available. We present a model-based imitation learning method that can learn en... | Learning to imitate an expert in the absence of optimal actions learning a dynamics model while exploring the environment. | 230 | scitldr |
Recent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the original model with commensurate training steps. While this discovery is insightful, finding proper sub-networks requires iterative training and... | We show the possibility of pruning to find a small sub-network with significantly higher convergence rate than the full model. | 231 | scitldr |
Disentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant representations, transferability to other tasks, interpretability, etc. We consider the problem of unsupervised learning of disentangled rep... | We propose a variational inference based approach for encouraging the inference of disentangled latents. We also propose a new metric for quantifying disentanglement. | 232 | scitldr |
In multiagent systems (MASs), each agent makes individual decisions but all of them contribute globally to the system evolution. Learning in MASs is difficult since each agent's selection of actions must take place in the presence of other co-learning agents. Moreover, the environmental stochasticity and uncertainties ... | Our proposed ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them. | 233 | scitldr |
Spiking neural networks are being investigated both as biologically plausible models of neural computation and also as a potentially more efficient type of neural network. While convolutional spiking neural networks have been demonstrated to achieve near state-of-the-art performance, only one solution has been proposed... | We demonstrate a gated recurrent asynchronous spiking neural network that corresponds to an LSTM unit. | 234 | scitldr |
CNNs are widely successful in recognizing human actions in videos, albeit with a great cost of computation. This cost is significantly higher in the case of long-range actions, where a video can span up to a few minutes, on average. The goal of this paper is to reduce the computational cost of these CNNs, without sacri... | Efficient video classification using frame-based conditional gating module for selecting most-dominant frames, followed by temporal modeling and classifier. | 235 | scitldr |
Human annotation for syntactic parsing is expensive, and large resources are available only for a fraction of languages. A question we ask is whether one can leverage abundant unlabeled texts to improve syntactic parsers, beyond just using the texts to obtain more generalisable lexical features (i.e. beyond word embedd... | Differentiable dynamic programming over perturbed input weights with application to semi-supervised VAE | 236 | scitldr |
DeConvNet, Guided BackProp, LRP, were invented to better understand deep neural networks. We show that these methods do not produce the theoretically correct explanation for a linear model. Yet they are used on multi-layer networks with millions of parameters. This is a cause for concern since linear models are simple ... | Without learning, it is impossible to explain a machine learning model's decisions. | 237 | scitldr |
Graph neural networks have shown promising on representing and analyzing diverse graph-structured data such as social, citation, and protein interaction networks. Existing approaches commonly suffer from the oversmoothing issue, regardless of whether policies are edge-based or node-based for neighborhood aggregation. M... | Simple and effective graph neural network with mixture of random walk steps and attention | 238 | scitldr |
Basis pursuit is a compressed sensing optimization in which the l1-norm is minimized subject to model error constraints. Here we use a deep neural network prior instead of l1-regularization. Using known noise statistics, we jointly learn the prior and reconstruct images without access to ground-truth data. During train... | We present an unsupervised deep learning reconstruction for imaging inverse problems that combines neural networks with model-based constraints. | 239 | scitldr |
Deep learning approaches usually require a large amount of labeled data to generalize. However, humans can learn a new concept only by a few samples. One of the high cogntition human capablities is to learn several concepts at the same time. In this paper, we address the task of classifying multiple objects by seeing o... | We introduce a diagnostic task which is a variation of few-shot learning and introduce a dataset for it. | 240 | scitldr |
We present a new approach to defining a sequence loss function to train a summarizer by using a secondary encoder-decoder as a loss function, alleviating a shortcoming of word level training for sequence outputs. The technique is based on the intuition that if a summary is a good one, it should contain the most essenti... | We present the use of a secondary encoder-decoder as a loss function to help train a summarizer. | 241 | scitldr |
Existing unsupervised video-to-video translation methods fail to produce translated videos which are frame-wise realistic, semantic information preserving and video-level consistent. In this work, we propose a novel unsupervised video-to-video translation model. Our model decomposes the style and the content, uses spec... | A temporally consistent and modality flexible unsupervised video-to-video translation framework trained in a self-supervised manner. | 242 | scitldr |
Providing transparency of AI planning systems is crucial for their success in practical applications. In order to create a transparent system, a user must be able to query it for explanations about its outputs. We argue that a key underlying principle for this is the use of causality within a planning model, and that a... | Argumentation frameworks are used to represent causality of plans/models to be utilized for explanations. | 243 | scitldr |
Point clouds, as a form of Lagrangian representation, allow for powerful and flexible applications in a large number of computational disciplines. We propose a novel deep-learning method to learn stable and temporally coherent feature spaces for points clouds that change over time. We identify a set of inherent problem... | We propose a generative neural network approach for temporally coherent point clouds. | 244 | scitldr |
We study the problem of generating adversarial examples in a black-box setting in which only loss-oracle access to a model is available. We introduce a framework that conceptually unifies much of the existing work on black-box attacks, and demonstrate that the current state-of-the-art methods are optimal in a natural s... | We present a unifying view on black-box adversarial attacks as a gradient estimation problem, and then present a framework (based on bandits optimization) to integrate priors into gradient estimation, leading to significantly increased performance. | 245 | scitldr |
Collecting high-quality, large scale datasets typically requires significant resources. The aim of the present work is to improve the label efficiency of large neural networks operating on audio data through multitask learning with self-supervised tasks on unlabeled data. To this end, we trained an end-to-end audio fea... | Improving label efficiency through multi-task learning on auditory data | 246 | scitldr |
Information need of humans is essentially multimodal in nature, enabling maximum exploitation of situated context. We introduce a dataset for sequential procedural (how-to) text generation from images in cooking domain. The dataset consists of 16,441 cooking recipes with 160,479 photos associated with different steps. ... | The paper presents two techniques to incorporate high level structure in generating procedural text from a sequence of images. | 247 | scitldr |
Implicit models, which allow for the generation of samples but not for point-wise evaluation of probabilities, are omnipresent in real-world problems tackled by machine learning and a hot topic of current research. Some examples include data simulators that are widely used in engineering and scientific research, genera... | We introduced a novel gradient estimator using Stein's method, and compared with other methods on learning implicit models for approximate inference and image generation. | 248 | scitldr |
Noise injection is a fundamental tool for data augmentation, and yet there is no widely accepted procedure to incorporate it with learning frameworks. This study analyzes the effects of adding or applying different noise models of varying magnitudes to Convolutional Neural Network (CNN) architectures. Noise models that... | Ideal methodology to inject noise to input data during CNN training | 249 | scitldr |
State-of-the-art Unsupervised Domain Adaptation (UDA) methods learn transferable features by minimizing the feature distribution discrepancy between the source and target domains. Different from these methods which do not model the feature distributions explicitly, in this paper, we explore explicit feature distributio... | We propose to explicitly model deep feature distributions of source and target data as Gaussian mixture distributions for Unsupervised Domain Adaptation (UDA) and achieve superior results in multiple UDA tasks than state-of-the-art methods. | 250 | scitldr |
Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents. We propose to decompose a complex environment using a task-agnostic world graphs, an abstraction that accelerates learning by enabling agents to focus exploration on a subspace of the environment. The ... | We learn a task-agnostic world graph abstraction of the environment and show how using it for structured exploration can significantly accelerate downstream task-specific RL. | 251 | scitldr |
We introduce the notion of property signatures, a representation for programs and program specifications meant for consumption by machine learning algorithms. Given a function with input type τ_in and output type τ_out, a property is a function of type: (τ_in, τ_out) → Bool that (informally) describes some simple prope... | We represent a computer program using a set of simpler programs and use this representation to improve program synthesis techniques. | 252 | scitldr |
Social dilemmas are situations where individuals face a temptation to increase their payoffs at a cost to total welfare. Building artificially intelligent agents that achieve good outcomes in these situations is important because many real world interactions include a tension between selfish interests and the welfare o... | How can we build artificial agents that solve social dilemmas (situations where individuals face a temptation to increase their payoffs at a cost to total welfare)? | 253 | scitldr |
The reparameterization trick has become one of the most useful tools in the field of variational inference. However, the reparameterization trick is based on the standardization transformation which restricts the scope of application of this method to distributions that have tractable inverse cumulative distribution fu... | We propose a novel generalized transformation-based gradient model and propose a polynomial-based gradient estimator based upon the model. | 254 | scitldr |
The fault diagnosis in a modern communication system is traditionally supposed to be difficult, or even impractical for a purely data-driven machine learning approach, for it is a humanmade system of intensive knowledge. A few labeled raw packet streams extracted from fault archive can hardly be sufficient to deduce th... | semi-supervised and transfer learning on packet flow classification, via a system of cooperative or adversarial neural blocks | 255 | scitldr |
Our work addresses two important issues with recurrent neural networks: they are over-parameterized, and the recurrent weight matrix is ill-conditioned. The former increases the sample complexity of learning and the training time. The latter causes the vanishing and exploding gradient problem. We present a flexible rec... | Out work presents a Kronecker factorization of recurrent weight matrices for parameter efficient and well conditioned recurrent neural networks. | 256 | scitldr |
This paper studies the undesired phenomena of over-sensitivity of representations learned by deep networks to semantically-irrelevant changes in data. We identify a cause for this shortcoming in the classical Variational Auto-encoder (VAE) objective, the evidence lower bound (ELBO). We show that the ELBO fails to contr... | We propose a method for computing adversarially robust representations in an entirely unsupervised way. | 257 | scitldr |
We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data. We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables using neural networks. This extension allows to model complex interactions while... | We are proposing a new score-based approach to structure/causal learning leveraging neural networks and a recent continuous constrained formulation to this problem | 258 | scitldr |
We study the problem of designing provably optimal adversarial noise algorithms that induce misclassification in settings where a learner aggregates decisions from multiple classifiers. Given the demonstrated vulnerability of state-of-the-art models to adversarial examples, recent efforts within the field of robust mac... | Paper analyzes the problem of designing adversarial attacks against multiple classifiers, introducing algorithms that are optimal for linear classifiers and which provide state-of-the-art results for deep learning. | 259 | scitldr |
Multiagent systems where the agents interact among themselves and with an stochastic environment can be formalized as stochastic games. We study a subclass of these games, named Markov potential games (MPGs), that appear often in economic and engineering applications when the agents share some common resource. We consi... | We present general closed loop analysis for Markov potential games and show that deep reinforcement learning can be used for learning approximate closed-loop Nash equilibrium. | 260 | scitldr |
We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model. We use this property, applying fully convolutional models to lossless compression, demonstrating a method to scale... | We scale up lossless compression with latent variables, beating existing approaches on full-size ImageNet images. | 261 | scitldr |
State of the art computer vision models have been shown to be vulnerable to small adversarial perturbations of the input. In other words, most images in the data distribution are both correctly classified by the model and are very close to a visually similar misclassified image. Despite substantial research interest, t... | We hypothesize that the vulnerability of image models to small adversarial perturbation is a naturally occurring result of the high dimensional geometry of the data manifold. We explore and theoretically prove this hypothesis for a simple synthetic dataset. | 262 | scitldr |
It has been established that diverse behaviors spanning the controllable subspace of a Markov decision process can be trained by rewarding a policy for being distinguishable from other policies. However, one limitation of this formulation is the difficulty to generalize beyond the finite set of behaviors being explicit... | We introduce Variational Intrinsic Successor FeatuRes (VISR), a novel algorithm which learns controllable features that can be leveraged to provide fast task inference through the successor features framework. | 263 | scitldr |
Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn strong supervised models like convolutional neural networks. However, these models trained on one data domain may not generalize well to other domains unequipped with annotations for model finetuning. To avoid... | A domain adaptation method for structured output via learning patch-level discriminative feature representations | 264 | scitldr |
Many machine learning algorithms are vulnerable to almost imperceptible perturbations of their inputs. So far it was unclear how much risk adversarial perturbations carry for the safety of real-world machine learning applications because most methods used to generate such perturbations rely either on detailed model inf... | A novel adversarial attack that can directly attack real-world black-box machine learning models without transfer. | 265 | scitldr |
We demonstrate that it is possible to train large recurrent language models with user-level differential privacy guarantees with only a negligible cost in predictive accuracy. Our work builds on recent advances in the training of deep networks on user-partitioned data and privacy accounting for stochastic gradient desc... | User-level differential privacy for recurrent neural network language models is possible with a sufficiently large dataset. | 266 | scitldr |
Convolutional neural networks (CNNs) are commonly trained using a fixed spatial image size predetermined for a given model. Although trained on images of a specific size, it is well established that CNNs can be used to evaluate a wide range of image sizes at test time, by adjusting the size of intermediate feature maps... | Training convnets with mixed image size can improve results across multiple sizes at evaluation | 267 | scitldr |
We propose a simple technique for encouraging generative RNNs to plan ahead. We train a ``backward'' recurrent network to generate a given sequence in reverse order, and we encourage states of the forward model to predict cotemporal states of the backward model. The backward network is used only during training, and pl... | The paper introduces a method of training generative recurrent networks that helps to plan ahead. We run a second RNN in a reverse direction and make a soft constraint between cotemporal forward and backward states. | 268 | scitldr |
Deep generative models seek to recover the process with which the observed data was generated. They may be used to synthesize new samples or to subsequently extract representations. Successful approaches in the domain of images are driven by several core inductive biases. However, a bias to account for the compositiona... | We propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition | 269 | scitldr |
Current literature in machine learning holds that unaligned, self-interested agents do not learn to use an emergent communication channel. We introduce a new sender-receiver game to study emergent communication for this spectrum of partially-competitive scenarios and put special care into evaluation. We find that commu... | We manage to emerge communication with selfish agents, contrary to the current view in ML | 270 | scitldr |
Deep neural networks (DNNs) typically have enough capacity to fit random data by brute force even when conventional data-dependent regularizations focusing on the geometry of the features are imposed. We find out that the reason for this is the inconsistency between the enforced geometry and the standard softmax cross ... | we propose a new framework for data-dependent DNN regularization that can prevent DNNs from overfitting random data or random labels. | 271 | scitldr |
End-to-end automatic speech recognition (ASR) commonly transcribes audio signals into sequences of characters while its performance is evaluated by measuring the word-error rate (WER). This suggests that predicting sequences of words directly may be helpful instead. However, training with word-level supervision can be ... | Multi-task learning improves word-and-character-level speech recognition by interpolating the preference biases of its components: frequency- and word length-preference. | 272 | scitldr |
Discretizing floating-point vectors is a fundamental step of modern indexing methods. State-of-the-art techniques learn parameters of the quantizers on training data for optimal performance, thus adapting quantizers to the data. In this work, we propose to reverse this paradigm and adapt the data to the quantizer: we t... | We learn a neural network that uniformizes the input distribution, which leads to competitive indexing performance in high-dimensional space | 273 | scitldr |
Temporal difference (TD) learning is a popular algorithm for policy evaluation in reinforcement learning, but the vanilla TD can substantially suffer from the inherent optimization variance. A variance reduced TD (VRTD) algorithm was proposed by , which applies the variance reduction technique directly to the online TD... | This paper provides a rigorous study of the variance reduced TD learning and characterizes its advantage over vanilla TD learning | 274 | scitldr |
We tackle unsupervised domain adaptation by accounting for the fact that different domains may need to be processed differently to arrive to a common feature representation effective for recognition. To this end, we introduce a deep learning framework where each domain undergoes a different sequence of operations, allo... | A Multiflow Network is a dynamic architecture for domain adaptation that learns potentially different computational graphs per domain, so as to map them to a common representation where inference can be performed in a domain-agnostic fashion. | 275 | scitldr |
The practical usage of reinforcement learning agents is often bottlenecked by the duration of training time. To accelerate training, practitioners often turn to distributed reinforcement learning architectures to parallelize and accelerate the training process. However, modern methods for scalable reinforcement learnin... | IMPACT helps RL agents train faster by decreasing training wall-clock time and increasing sample efficiency simultaneously. | 276 | scitldr |
In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks (MPNNs). More specifically, we introduce a graph neural network called Colored Local Iterative Procedure (CLIP) that uses colors to disambiguate identical node at... | This paper introduces a coloring scheme for node disambiguation in graph neural networks based on separability, proven to be a universal MPNN extension. | 277 | scitldr |
In this paper we present a method for algorithmic melody generation using a generative adversarial network without recurrent components. Music generation has been successfully done using recurrent neural networks, where the model learns sequence information that can help create authentic sounding melodies. Here, we use... | Representing melodies as images with semantic units aligned we can generate them using a DCGAN without any recurrent components. | 278 | scitldr |
Neural machine translation (NMT) systems have reached state of the art performance in translating text and widely deployed. Yet little is understood about how these systems function or break. Here we show that NMT systems are susceptible to producing highly pathological translations that are completely untethered from ... | We introduce and analyze the phenomenon of "hallucinations" in NMT, or spurious translations unrelated to source text, and propose methods to reduce its frequency. | 279 | scitldr |
For AI systems to garner widespread public acceptance, we must develop methods capable of explaining the decisions of black-box models such as neural networks. In this work, we identify two issues of current explanatory methods. First, we show that two prevalent perspectives on explanations—feature-additivity and featu... | An evaluation framework based on a real-world neural network for post-hoc explanatory methods | 280 | scitldr |
Planning in high-dimensional space remains a challenging problem, even with recent advances in algorithms and computational power. We are inspired by efference copy and sensory reafference theory from neuroscience. Our aim is to allow agents to form mental models of their environments for planning. The cerebellum is em... | We present a neuroscience-inspired method based on neural networks for latent space search | 281 | scitldr |
Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks. Introducing the concept of an optimal representation space, we provide a simple theoretical resolution to this apparent paradox. In addition, we present a straightforward procedure that, without an... | By introducing the notion of an optimal representation space, we provide a theoretical argument and experimental validation that an unsupervised model for sentences can perform well on both supervised similarity and unsupervised transfer tasks. | 282 | scitldr |
Cloze test is widely adopted in language exams to evaluate students' language proficiency. In this paper, we propose the first large-scale human-designed cloze test dataset CLOTH in which the questions were used in middle-school and high-school language exams. With the missing blanks carefully created by teachers and c... | A cloze test dataset designed by teachers to assess language proficiency | 283 | scitldr |
Recent work suggests goal-driven training of neural networks can be used to model neural activity in the brain. While response properties of neurons in artificial neural networks bear similarities to those in the brain, the network architectures are often constrained to be different. Here we ask if a neural network can... | Artificial neural networks trained with gradient descent are capable of recapitulating both realistic neural activity and the anatomical organization of a biological circuit. | 284 | scitldr |
Convolutional Neural Networks (CNNs) are computationally intensive, which limits their application on mobile devices. Their energy is dominated by the number of multiplies needed to perform the convolutions. Winograd’s minimal filtering algorithm and network pruning can reduce the operation count, but these two methods... | Prune and ReLU in Winograd domain for efficient convolutional neural network | 285 | scitldr |
In this paper we present a novel optimization algorithm called Advanced Neuroevolution. The aim for this algorithm is to train deep neural networks, and eventually act as an alternative to Stochastic Gradient Descent (SGD) and its variants as needed. We evaluated our algorithm on the MNIST dataset, as well as on severa... | A new algorithm to train deep neural networks. Tested on optimization functions and MNIST. | 286 | scitldr |
Stochastic neural net weights are used in a variety of contexts, including regularization, Bayesian neural nets, exploration in reinforcement learning, and evolution strategies. Unfortunately, due to the large number of weights, all the examples in a mini-batch typically share the same weight perturbation, thereby limi... | We introduce flipout, an efficient method for decorrelating the gradients computed by stochastic neural net weights within a mini-batch by implicitly sampling pseudo-independent weight perturbations for each example. | 287 | scitldr |
Deep generative models such as Variational AutoEncoder (VAE) and Generative Adversarial Network (GAN) play an increasingly important role in machine learning and computer vision. However, there are two fundamental issues hindering their real-world applications: the difficulty of conducting variational inference in VAE ... | A new model Latently Invertible Autoencoder is proposed to solve the problem of variational inference in VAE using the invertible network and two-stage adversarial training. | 288 | scitldr |
Neural programs are highly accurate and structured policies that perform algorithmic tasks by controlling the behavior of a computation mechanism. Despite the potential to increase the interpretability and the compositionality of the behavior of artificial agents, it remains difficult to learn from demonstrations neura... | We introduce the PHP model for hierarchical representation of neural programs, and an algorithm for learning PHPs from a mixture of strong and weak supervision. | 289 | scitldr |
Deep Reinforcement Learning has managed to achieve state-of-the-art in learning control policies directly from raw pixels. However, despite its remarkable success, it fails to generalize, a fundamental component required in a stable Artificial Intelligence system. Using the Atari game Breakout, we demonstrate the diffi... | We propose a method of transferring knowledge between related RL tasks using visual mappings, and demonstrate its effectiveness on visual variants of the Atari Breakout game and different levels of Road Fighter, a Nintendo car driving game. | 290 | scitldr |
A commonplace belief in the machine learning community is that using adaptive gradient methods hurts generalization. We re-examine this belief both theoretically and experimentally, in light of insights and trends from recent years. We revisit some previous oft-cited experiments and theoretical accounts in more depth, ... | Adaptive gradient methods, when done right, do not incur a generalization penalty. | 291 | scitldr |
The ability to generalize quickly from few observations is crucial for intelligent systems. In this paper we introduce APL, an algorithm that approximates probability distributions by remembering the most surprising observations it has encountered. These past observations are recalled from an external memory module and... | We introduce a model which generalizes quickly from few observations by storing surprising information and attending over the most relevant data at each time point. | 292 | scitldr |
Recent advances in recurrent neural nets (RNNs) have shown much promise in many applications in natural language processing. For most of these tasks, such as sentiment analysis of customer reviews, a recurrent neural net model parses the entire review before forming a decision. We argue that reading the entire input is... | We develop an end-to-end trainable approach for skimming, rereading and early stopping applicable to classification tasks. | 293 | scitldr |
Reinforcement learning agents need to explore their unknown environments to solve the tasks given to them. The Bayes optimal solution to exploration is intractable for complex environments, and while several exploration methods have been proposed as approximations, it remains unclear what underlying objective is being ... | We view exploration in RL as a problem of matching a marginal distribution over states. | 294 | scitldr |
The effectiveness of Convolutional Neural Networks stems in large part from their ability to exploit the translation invariance that is inherent in many learning problems. Recently, it was shown that CNNs can exploit other invariances, such as rotation invariance, by using group convolutions instead of planar convoluti... | We introduce G-HexaConv, a group equivariant convolutional neural network on hexagonal lattices. | 295 | scitldr |
Deep Convolutional Neural Networks (CNNs) have been repeatedly shown to perform well on image classification tasks, successfully recognizing a broad array of objects when given sufficient training data. Methods for object localization, however, are still in need of substantial improvement. Common approaches to this pro... | Proposing a novel object localization(detection) approach based on interpreting the deep CNN using internal representation and network's thoughts | 296 | scitldr |
We present trellis networks, a new architecture for sequence modeling. On the one hand, a trellis network is a temporal convolutional network with special structure, characterized by weight tying across depth and direct injection of the input into deep layers. On the other hand, we show that truncated recurrent network... | Trellis networks are a new sequence modeling architecture that bridges recurrent and convolutional models and sets a new state of the art on word- and character-level language modeling. | 297 | scitldr |
We propose an end-to-end framework for training domain specific models (DSMs) to obtain both high accuracy and computational efficiency for object detection tasks. DSMs are trained with distillation and focus on achieving high accuracy at a limited domain (e.g. fixed view of an intersection). We argue that DSMs can cap... | High object-detection accuracy can be obtained by training domain specific compact models and the training can be very short. | 298 | scitldr |
We compare the model-free reinforcement learning with the model-based approaches through the lens of the expressive power of neural networks for policies, $Q$-functions, and dynamics. We show, theoretically and empirically, that even for one-dimensional continuous state space, there are many MDPs whose optimal $Q$-func... | We compare deep model-based and model-free RL algorithms by studying the approximability of $Q$-functions, policies, and dynamics by neural networks. | 299 | scitldr |
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