query stringlengths 273 149k | pos stringlengths 18 667 | idx int64 0 1.99k | task_name stringclasses 1
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We present a data driven approach to construct a library of feedback motion primitives for non-holonomic vehicles that guarantees bounded error in following arbitrarily long trajectories. This ensures that motion re-planning can be avoided as long as disturbances to the vehicle remain within a certain bound and also po... | We show that under some assumptions on vehicle dynamics and environment uncertainty it is possible to automatically synthesize motion primitives that do not accumulate error over time. | 1,200 | scitldr |
Deep Convolutional Networks (DCNs) have been shown to be sensitive to Universal Adversarial Perturbations (UAPs): input-agnostic perturbations that fool a model on large portions of a dataset. These UAPs exhibit interesting visual patterns, but this phenomena is, as yet, poorly understood. Our work shows that visually ... | Existing Deep Convolutional Networks in image classification tasks are sensitive to Gabor noise patterns, i.e. small structured changes to the input cause large changes to the output. | 1,201 | scitldr |
Deep neural networks (DNNs) perform well on a variety of tasks despite the fact that most used in practice are vastly overparametrized and even capable of perfectly fitting randomly labeled data. Recent evidence suggests that developing "compressible" representations is key for adjusting the complexity of overparametri... | Probing robustness and redundancy in deep neural networks reveals capacity-constraining features which help to explain non-overfitting. | 1,202 | scitldr |
Deep networks realize complex mappings that are often understood by their locally linear behavior at or around points of interest. For example, we use the derivative of the mapping with respect to its inputs for sensitivity analysis, or to explain (obtain coordinate relevance for) a prediction. One key challenge is tha... | A scalable algorithm to establish robust derivatives of deep networks w.r.t. the inputs. | 1,203 | scitldr |
Recent years have witnessed two seemingly opposite developments of deep convolutional neural networks (CNNs). On one hand, increasing the density of CNNs by adding cross-layer connections achieve higher accuracy. On the other hand, creating sparsity structures through regularization and pruning methods enjoys lower com... | In this paper, we explore an internal dense yet external sparse network structure of deep neural networks and analyze its key properties. | 1,204 | scitldr |
Statistical inference methods are fundamentally important in machine learning. Most state-of-the-art inference algorithms are variants of Markov chain Monte Carlo (MCMC) or variational inference (VI). However, both methods struggle with limitations in practice: MCMC methods can be computationally demanding; VI methods ... | In this work, we aim to improve upon MCMC and VI by a novel hybrid method based on the idea of reducing simulation bias of finite-length MCMC chains using gradient-based optimisation. | 1,205 | scitldr |
We show that information about whether a neural network's output will be correct or incorrect is present in the outputs of the network's intermediate layers. To demonstrate this effect, we train a new "meta" network to predict from either the final output of the underlying "base" network or the output of one of the bas... | Information about whether a neural network's output will be correct or incorrect is somewhat present in the outputs of the network's intermediate layers. | 1,206 | scitldr |
We develop a new algorithm for imitation learning from a single expert demonstration. In contrast to many previous one-shot imitation learning approaches, our algorithm does not assume access to more than one expert demonstration during the training phase. Instead, we leverage an exploration policy to acquire unsupervi... | Unsupervised self-imitation algorithm capable of inference from a single expert demonstration. | 1,207 | scitldr |
Significant work has been dedicated to developing methods for communicating reasons for decision-making within au- tomated scheduling systems to human users. However, much less focus has been placed on communicating reasons for why scheduling systems are unable to arrive at a feasible solution when over-constrained. We... | We develop a framework for generating human-understandable explanations for why infeasibility is occurring in over-constrained instances of a class of resource-constrained scheduling problems. | 1,208 | scitldr |
Adversarial perturbations cause a shift in the salient features of an image, which may in a misclassification. We demonstrate that gradient-based saliency approaches are unable to capture this shift, and develop a new defense which detects adversarial examples based on learnt saliency models instead. We study two appro... | We show that gradients are unable to capture shifts in saliency due to adversarial perturbations and present an alternative adversarial defense using learnt saliency models that is effective against both black-box and white-box attacks. | 1,209 | scitldr |
Disentangled encoding is an important step towards a better representation learning. However, despite the numerous efforts, there still is no clear winner that captures the independent features of the data in an unsupervised fashion. In this work we empirically evaluate the performance of six unsupervised disentangleme... | Inadequacy of Disentanglement Metrics | 1,210 | scitldr |
Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly learning a policy for each agent to maximize a common reward. In this paper, we aim to address this from a different angle. In particular, we consider scenarios where there are self-interested agents (i.e., wo... | We propose Mind-aware Multi-agent Management Reinforcement Learning (M^3RL) for training a manager to motivate self-interested workers to achieve optimal collaboration by assigning suitable contracts to them. | 1,211 | scitldr |
Inferring temporally coherent data features is crucial for a large variety of learning tasks. We propose a network architecture that introduces temporal recurrent connections for the internal state of the widely used residual blocks. We demonstrate that, with these connections, convolutional neural networks can more ro... | A method for persistent latent states in ResBlocks demonstrated for super-resolution of alised image sequences. | 1,212 | scitldr |
The backpropagation algorithm is the most popular algorithm training neural networks nowadays. However, it suffers from the forward locking, backward locking and update locking problems, especially when a neural network is so large that its layers are distributed across multiple devices. Existing solutions either can o... | We propose Diversely Stale Parameters to break lockings of the backpropoagation algorithm and train a CNN in parallel. | 1,213 | scitldr |
The emergence of language in multi-agent settings is a promising research direction to ground natural language in simulated agents. If AI would be able to understand the meaning of language through its using it, it could also transfer it to other situations flexibly. That is seen as an important step towards achieving ... | An auxiliary prediction task can speed up learning in language emergence setups. | 1,214 | scitldr |
Image paragraph captioning is the task of automatically generating multiple sentences for describing images in grain-fined and coherent text. Existing typical deep learning-based models for image captioning consist of an image encoder to extract visual features and a language model decoder, which has shown promising in... | TEB Module for IPC | 1,215 | scitldr |
Learning Mahalanobis metric spaces is an important problem that has found numerous applications. Several algorithms have been designed for this problem, including Information Theoretic Metric Learning (ITML) [Davis et al. 2007] and Large Margin Nearest Neighbor (LMNN) classification [Weinberger and Saul 2009]. We consi... | Fully parallelizable and adversarial-noise resistant metric learning algorithm with theoretical guarantees. | 1,216 | scitldr |
Standard image captioning tasks such as COCO and Flickr30k are factual, neutral in tone and (to a human) state the obvious (e.g., “a man playing a guitar”). While such tasks are useful to verify that a machine understands the content of an image, they are not engaging to humans as captions. With this in mind we define ... | We develop engaging image captioning models conditioned on personality that are also state of the art on regular captioning tasks. | 1,217 | scitldr |
Machine learning (ML) models trained by differentially private stochastic gradient descent (DP-SGD) have much lower utility than the non-private ones. To mitigate this degradation, we propose a DP Laplacian smoothing SGD (DP-LSSGD) to train ML models with differential privacy (DP) guarantees. At the core of DP-LSSGD is... | We propose a differentially private Laplacian smoothing stochastic gradient descent to train machine learning models with better utility and maintain differential privacy guarantees. | 1,218 | scitldr |
We study the robust one-bit compressed sensing problem whose goal is to design an algorithm that faithfully recovers any sparse target vector $\theta_0\in\mathbb{R}^d$ \emph{uniformly} from $m$ quantized noisy measurements. Under the assumption that the measurements are sub-Gaussian, to recover any $k$-sparse $\theta_0... | We provide statistical and computational analysis of one-bit compressed sensing problem with a generative prior. | 1,219 | scitldr |
We introduce an unsupervised structure learning algorithm for deep, feed-forward, neural networks. We propose a new interpretation for depth and inter-layer connectivity where a hierarchy of independencies in the input distribution is encoded in the network structure. This in structures allowing neurons to connect to n... | A principled approach for structure learning of deep neural networks with a new interpretation for depth and inter-layer connectivity. | 1,220 | scitldr |
L1 and L2 regularizers are critical tools in machine learning due to their ability to simplify solutions. However, imposing strong L1 or L2 regularization with gradient descent method easily fails, and this limits the generalization ability of the underlying neural networks. To understand this phenomenon, we investigat... | We investigate how and why strong L1/L2 regularization fails and propose a method than can achieve strong regularization. | 1,221 | scitldr |
Despite neural network’s high performance, the lack of interpretability has been the main bottleneck for its safe usage in practice. In domains with high stakes (e.g., medical diagnosis), gaining insights into the network is critical for gaining trust and being adopted. One of the ways to improve interpretability of a ... | This work aims to provide quantitative answers to the relative importance of concepts of interest via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interest and test hypotheses using examples (e.g., a set of pictures that illustrate the conc... | 1,222 | scitldr |
We present a new family of objective functions, which we term the Conditional Entropy Bottleneck (CEB). These objectives are motivated by the Minimum Necessary Information (MNI) criterion. We demonstrate the application of CEB to classification tasks. We show that CEB gives: well-calibrated predictions; strong detectio... | The Conditional Entropy Bottleneck is an information-theoretic objective function for learning optimal representations. | 1,223 | scitldr |
Deep representation learning has become one of the most widely adopted approaches for visual search, recommendation, and identification. Retrieval of such representations from a large database is however computationally challenging. Approximate methods based on learning compact representations, have been widely explore... | We propose an approach to learn sparse high dimensional representations that are fast to search, by incorporating a surrogate of the number of operations directly into the loss function. | 1,224 | scitldr |
Model interpretability and systematic, targeted model adaptation present central challenges in deep learning. In the domain of intuitive physics, we study the task of visually predicting stability of block towers with the goal of understanding and influencing the model's reasoning. Our contributions are two-fold. First... | Combining auxiliary and adversarial training to interrogate and help physical understanding. | 1,225 | scitldr |
Flow based models such as Real NVP are an extremely powerful approach to density estimation. However, existing flow based models are restricted to transforming continuous densities over a continuous input space into similarly continuous distributions over continuous latent variables. This makes them poorly suited for m... | Flow based models, but non-invertible, to also learn discrete variables | 1,226 | scitldr |
We investigate the loss surface of neural networks. We prove that even for one-hidden-layer networks with "slightest" nonlinearity, the empirical risks have spurious local minima in most cases. Our thus indicate that in general "no spurious local minim" is a property limited to deep linear networks, and insights obtain... | We constructively prove that even the slightest nonlinear activation functions introduce spurious local minima, for general datasets and activation functions. | 1,227 | scitldr |
The tasks that an agent will need to solve often aren’t known during training. However, if the agent knows which properties of the environment we consider im- portant, then after learning how its actions affect those properties the agent may be able to use this knowledge to solve complex tasks without training specifi-... | Compositional attribute-based planning that generalizes to long test tasks, despite being trained on short & simple tasks. | 1,228 | scitldr |
The ability to learn new concepts with small amounts of data is a critical aspect of intelligence that has proven challenging for deep learning methods. Meta-learning has emerged as a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks. However, most meta-learning algor... | We identify and formalize the memorization problem in meta-learning and solve this problem with novel meta-regularization method, which greatly expand the domain that meta-learning can be applicable to and effective on. | 1,229 | scitldr |
Learning an efficient update rule from data that promotes rapid learning of new tasks from the same distribution remains an open problem in meta-learning. Typically, previous works have approached this issue either by attempting to train a neural network that directly produces updates or by attempting to learn better i... | We propose a novel framework for meta-learning a gradient-based update rule that scales to beyond few-shot learning and is applicable to any form of learning, including continual learning. | 1,230 | scitldr |
In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images. We propose Defense-GAN, a new fra... | Defense-GAN uses a Generative Adversarial Network to defend against white-box and black-box attacks in classification models. | 1,231 | scitldr |
We study the problem of learning similarity functions over very large corpora using neural network embedding models. These models are typically trained using SGD with random sampling of unobserved pairs, with a sample size that grows quadratically with the corpus size, making it expensive to scale. We propose new effic... | We develop efficient methods to train neural embedding models with a dot-product structure, by reformulating the objective function in terms of generalized Gram matrices, and maintaining estimates of those matrices. | 1,232 | scitldr |
For natural language understanding (NLU) technology to be maximally useful, it must be able to process language in a way that is not exclusive to a single task, genre, or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation (GLUE) benchmark, a collection of tools for evaluat... | We present a multi-task benchmark and analysis platform for evaluating generalization in natural language understanding systems. | 1,233 | scitldr |
A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient explo... | A modular method for fully cooperative multi-goal multi-agent reinforcement learning, based on curriculum learning for efficient exploration and credit assignment for action-goal interactions. | 1,234 | scitldr |
We identify two issues with the family of algorithms based on the Adversarial Imitation Learning framework. The first problem is implicit bias present in the reward functions used in these algorithms. While these biases might work well for some environments, they can also lead to sub-optimal behavior in others. Secondl... | We address sample inefficiency and reward bias in adversarial imitation learning algorithms such as GAIL and AIRL. | 1,235 | scitldr |
Capsule Networks have shown encouraging on defacto benchmark computer vision datasets such as MNIST, CIFAR and smallNORB. Although, they are yet to be tested on tasks where the entities detected inherently have more complex internal representations and there are very few instances per class to learn from and where poin... | A variant of capsule networks that can be used for pairwise learning tasks. Results shows that Siamese Capsule Networks work well in the few shot learning setting. | 1,236 | scitldr |
Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack. Such adaptation property strongly relies on cellular neuromodulation, t... | This paper introduces neuromodulation in artificial neural networks. | 1,237 | scitldr |
Convolution operator is the core of convolutional neural networks (CNNs) and occupies the most computation cost. To make CNNs more efficient, many methods have been proposed to either design lightweight networks or compress models. Although some efficient network structures have been proposed, such as MobileNet or Shuf... | We propose a dynamic convolution method to significantly accelerate inference time of CNNs while maintaining the accuracy. | 1,238 | scitldr |
Operating deep neural networks on devices with limited resources requires the reduction of their memory footprints and computational requirements. In this paper we introduce a training method, called look-up table quantization (LUT-Q), which learns a dictionary and assigns each weight to one of the dictionary's values.... | In this paper we introduce a training method, called look-up table quantization (LUT-Q), which learns a dictionary and assigns each weight to one of the dictionary's values | 1,239 | scitldr |
An unintended consequence of feature sharing is the model fitting to correlated tasks within the dataset, termed negative transfer. In this paper, we revisit the problem of negative transfer in multitask setting and find that its corrosive effects are applicable to a wide range of linear and non-linear models, includin... | We look at negative transfer from a domain adaptation point of view to derive an adversarial learning algorithm. | 1,240 | scitldr |
Recent theoretical and experimental suggest the possibility of using current and near-future quantum hardware in challenging sampling tasks. In this paper, we introduce free-energy-based reinforcement learning (FERL) as an application of quantum hardware. We propose a method for processing a quantum annealer’s measured... | We train Quantum Boltzmann Machines using a replica stacking method and a quantum annealer to perform a reinforcement learning task. | 1,241 | scitldr |
Deep learning models are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on benign inputs. However, under the black-box setting, most existing adversaries often have a poor transferability to attack other defense models. In this work, from the perspective of regarding the advers... | We proposed a Nesterov Iterative Fast Gradient Sign Method (NI-FGSM) and a Scale-Invariant attack Method (SIM) that can boost the transferability of adversarial examples for image classification. | 1,242 | scitldr |
Low bit-width weights and activations are an effective way of combating the increasing need for both memory and compute power of Deep Neural Networks. In this work, we present a probabilistic training method for Neural Network with both binary weights and activations, called PBNet. By embracing stochasticity during tra... | We introduce a stochastic training method for training Binary Neural Network with both binary weights and activations. | 1,243 | scitldr |
The goal of generative models is to model the underlying data distribution of a sample based dataset. Our intuition is that an accurate model should in principle also include the sample based dataset as part of its induced probability distribution. To investigate this, we look at fully trained generative models using t... | We analyze the impact of the latent space of fully trained generators by pseudo inverting them. | 1,244 | scitldr |
We address the problem of open-set authorship verification, a classification task that consists of attributing texts of unknown authorship to a given author when the unknown documents in the test set are excluded from the training set. We present an end-to-end model-building process that is universally applicable to a ... | We propose and end-to-end model-building process that is universally applicable to a wide variety of authorship verification corpora and outperforms state-of-the-art with little to no modification or fine-tuning. | 1,245 | scitldr |
We consider tackling a single-agent RL problem by distributing it to $n$ learners. These learners, called advisors, endeavour to solve the problem from a different focus. Their advice, taking the form of action values, is then communicated to an aggregator, which is in control of the system. We show that the local plan... | We consider tackling a single-agent RL problem by distributing it to $n$ learners. | 1,246 | scitldr |
We present a hybrid framework that leverages the trade-off between temporal and frequency precision in audio representations to improve the performance of speech enhancement task. We first show that conventional approaches using specific representations such as raw-audio and spectrograms are each effective at targeting... | A hybrid model utilizing both raw-audio and spectrogram information for speech enhancement tasks. | 1,247 | scitldr |
Consistently checking the statistical significance of experimental is the first mandatory step towards reproducible science. This paper presents a hitchhiker's guide to rigorous comparisons of reinforcement learning algorithms. After introducing the concepts of statistical testing, we review the relevant statistical te... | This paper compares statistical tests for RL comparisons (false positive, statistical power), checks robustness to assumptions using simulated distributions and empirical distributions (SAC, TD3), provides guidelines for RL students and researchers. | 1,248 | scitldr |
In the Information Bottleneck (IB), when tuning the relative strength between compression and prediction terms, how do the two terms behave, and what's their relationship with the dataset and the learned representation? In this paper, we set out to answer these questions by studying multiple phase transitions in the IB... | We give a theoretical analysis of the Information Bottleneck objective to understand and predict observed phase transitions in the prediction vs. compression tradeoff. | 1,249 | scitldr |
We propose two approaches of locally adaptive activation functions namely, layer-wise and neuron-wise locally adaptive activation functions, which improve the performance of deep and physics-informed neural networks. The local adaptation of activation function is achieved by introducing scalable hyper-parameters in eac... | Proposing locally adaptive activation functions in deep and physics-informed neural networks for faster convergence | 1,250 | scitldr |
Learning in Gaussian Process models occurs through the adaptation of hyperparameters of the mean and the covariance function. The classical approach entails maximizing the marginal likelihood yielding fixed point estimates (an approach called Type II maximum likelihood or ML-II). An alternative learning procedure is to... | Analysis of Bayesian Hyperparameter Inference in Gaussian Process Regression | 1,251 | scitldr |
We consider the problem of using variational latent-variable models for data compression. For such models to produce a compressed binary sequence, which is the universal data representation in a digital world, the latent representation needs to be subjected to entropy coding. Range coding as an entropy coding technique... | We train variational models with quantized networks for computational determinism. This enables using them for cross-platform data compression. | 1,252 | scitldr |
Neural networks powered with external memory simulate computer behaviors. These models, which use the memory to store data for a neural controller, can learn algorithms and other complex tasks. In this paper, we introduce a new memory to store weights for the controller, analogous to the stored-program memory in modern... | A neural simulation of Universal Turing Machine | 1,253 | scitldr |
It is common practice to decay the learning rate. Here we show one can usually obtain the same learning curve on both training and test sets by instead increasing the batch size during training. This procedure is successful for stochastic gradient descent (SGD), SGD with momentum, Nesterov momentum, and Adam. It reache... | Decaying the learning rate and increasing the batch size during training are equivalent. | 1,254 | scitldr |
Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate. We propose a mixed objective that combines cross entropy loss with self-critical policy learning, using rewards derived... | We introduce the DCN+ with deep residual coattention and mixed-objective RL, which achieves state of the art performance on the Stanford Question Answering Dataset. | 1,255 | scitldr |
Neural architecture search (NAS) has achieved breakthrough success in a great number of applications in the past few years. It could be time to take a step back and analyze the good and bad aspects in the field of NAS. A variety of algorithms search architectures under different search space. These searched architectur... | A NAS benchmark applicable to almost any NAS algorithms. | 1,256 | scitldr |
Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions. However, generalization properties of GANs have not been well understood. In this paper, we analyze the generalization of GANs in practical settings. We show that discriminators trained on discr... | We propose a zero-centered gradient penalty for improving generalization and stability of GANs | 1,257 | scitldr |
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant amount of hyperparameter tuning, neural arc... | A sober view on the current state of GANs from a practical perspective | 1,258 | scitldr |
As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches ... | We propose a model-agnostic approach to explain the behaviour of black-box deep RL agents, trained to play Atari and board games, by highlighting relevant features of an input state. | 1,259 | scitldr |
To understand the inner work of deep neural networks and provide possible theoretical explanations, we study the deep representations through the untrained, random weight CNN-DCN architecture. As a convolutional AutoEncoder, CNN indicates the portion of a convolutional neural network from the input to an intermediate c... | We investigate the deep representation of untrained, random weight CNN-DCN architectures, and show their image reconstruction quality and possible applications. | 1,260 | scitldr |
The current trade-off between depth and computational cost makes it difficult to adopt deep neural networks for many industrial applications, especially when computing power is limited. Here, we are inspired by the idea that, while deeper embeddings are needed to discriminate difficult samples, a large number of sample... | This paper introduces a new dynamic feature representation approach to provide a more efficient way to do inference on deep neural networks. | 1,261 | scitldr |
This work provides an additional step in the theoretical understanding of neural networks. We consider neural networks with one hidden layer and show that when learning symmetric functions, one can choose initial conditions so that standard SGD training efficiently produces generalization guarantees. We empirically ver... | When initialized properly, neural networks can learn the simple class of symmetric functions; when initialized randomly, they fail. | 1,262 | scitldr |
Architecture search aims at automatically finding neural architectures that are competitive with architectures designed by human experts. While recent approaches have achieved state-of-the-art predictive performance for image recognition, they are problematic under resource constraints for two reasons: the neural archi... | We propose a method for efficient Multi-Objective Neural Architecture Search based on Lamarckian inheritance and evolutionary algorithms. | 1,263 | scitldr |
We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i.e., P(wordjcontext): No Language Structure in Context: Probabilistic topic models ignore word order by summarizing a given context as a “bag-of-word” and consequently the semantics of w... | Unified neural model of topic and language modeling to introduce language structure in topic models for contextualized topic vectors | 1,264 | scitldr |
The large memory requirements of deep neural networks strain the capabilities of many devices, limiting their deployment and adoption. Model compression methods effectively reduce the memory requirements of these models, usually through applying transformations such as weight pruning or quantization. In this paper, we ... | We propose a new way to compress neural networks using probabilistic data structures. | 1,265 | scitldr |
Recent work has shown that contextualized word representations derived from neural machine translation (NMT) are a viable alternative to such from simple word predictions tasks. This is because the internal understanding that needs to be built in order to be able to translate from one language to another is much more c... | We study the impact of using different kinds of subword units on the quality of the resulting representations when used to model syntax, semantics, and morphology. | 1,266 | scitldr |
Few-shot learning is the process of learning novel classes using only a few examples and it remains a challenging task in machine learning. Many sophisticated few-shot learning algorithms have been proposed based on the notion that networks can easily overfit to novel examples if they are simply fine-tuned using only a... | An empirical study that provides a novel perspective on few-shot learning, in which a fine-tuning method shows comparable accuracy to more complex state-of-the-art methods in several classification tasks. | 1,267 | scitldr |
We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks. We model the order stream as a stochastic process with finite history dependence, and employ a conditional Wasserstein GAN to capture history dependence of orders in a stock market. We test our app... | We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks. | 1,268 | scitldr |
We present a novel black-box adversarial attack algorithm with state-of-the-art model evasion rates for query efficiency under $\ell_\infty$ and $\ell_2$ metrics. It exploits a \textit{sign-based}, rather than magnitude-based, gradient estimation approach that shifts the gradient estimation from continuous to binary bl... | We present a sign-based, rather than magnitude-based, gradient estimation approach that shifts gradient estimation from continuous to binary black-box optimization. | 1,269 | scitldr |
Recurrent Neural Networks (RNNs) are widely used models for sequence data. Just like for feedforward networks, it has become common to build "deep" RNNs, i.e., stack multiple recurrent layers to obtain higher-level abstractions of the data. However, this works only for a handful of layers. Unlike feedforward networks, ... | We analyze the gradient propagation in deep RNNs and from our analysis, we propose a new multi-layer deep RNN. | 1,270 | scitldr |
Despite its empirical success, the theoretical underpinnings of the stability, convergence and acceleration properties of batch normalization (BN) remain elusive. In this paper, we attack this problem from a modelling approach, where we perform thorough theoretical analysis on BN applied to simplified model: ordinary l... | We mathematically analyze the effect of batch normalization on a simple model and obtain key new insights that applies to general supervised learning. | 1,271 | scitldr |
Solving tasks in Reinforcement Learning is no easy feat. As the goal of the agent is to maximize the accumulated reward, it often learns to exploit loopholes and misspecifications in the reward signal ing in unwanted behavior. While constraints may solve this issue, there is no closed form solution for general constrai... | For complex constraints in which it is not easy to estimate the gradient, we use the discounted penalty as a guiding signal. We prove that under certain assumptions it converges to a feasible solution. | 1,272 | scitldr |
The Handheld Virtual Panel (HVP) is the virtual panel attached to the non-dominant hand’s controller in virtual reality (VR). The HVP is the go-to technique for enabling menus and toolboxes in VR devices. In this paper, we investigate target acquisition performance for the HVP as a function of four factors: target widt... | The paper investigates target acquisition for handheld virtual panels in VR and shows that target width, distance, direction of approach with respect to gravity, and angle of approach, all impact user performance. | 1,273 | scitldr |
Deep neural networks have demonstrated unprecedented success in various knowledge management applications. However, the networks created are often very complex, with large numbers of trainable edges which require extensive computational resources. We note that many successful networks nevertheless often contain large n... | iSparse eliminates irrelevant or insignificant network edges with minimal impact on network performance by determining edge importance w.r.t. the final network output. | 1,274 | scitldr |
Language modeling tasks, in which words are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases. Motivated by the observation that efforts to code world knowledge into machine readable knowledge bases tend to be entity-centric... | We learn entity representations that can reconstruct Wikipedia categories with just a few exemplars. | 1,275 | scitldr |
Variational autoencoders (VAEs) have been successful at learning a low-dimensional manifold from high-dimensional data with complex dependencies. At their core, they consist of a powerful Bayesian probabilistic inference model, to capture the salient features of the data. In training, they exploit the power of variatio... | Using the q-deformed logarithm, we derive tighter bounds than IWAE, to train variational autoencoders. | 1,276 | scitldr |
A belief persists long in machine learning that enlargement of margins over training data accounts for the resistance of models to overfitting by increasing the robustness. Yet Breiman shows a dilemma that a uniform improvement on margin distribution \emph{does not} necessarily reduces generalization error. In this pap... | Bregman's dilemma is shown in deep learning that improvement of margins of over-parameterized models may result in overfitting, and dynamics of normalized margin distributions are proposed to predict generalization error and identify such a dilemma. | 1,277 | scitldr |
It has been an open research challenge for developing an end-to-end multi-domain task-oriented dialogue system, in which a human can converse with the dialogue agent to complete tasks in more than one domain. First, tracking belief states of multi-domain dialogues is difficult as the dialogue agent must obtain the comp... | We proposed an end-to-end dialogue system with a novel multi-level dialogue state tracker and achieved consistent performance on MultiWOZ2.1 in state tracking, task completion, and response generation performance. | 1,278 | scitldr |
Score matching provides an effective approach to learning flexible unnormalized models, but its scalability is limited by the need to evaluate a second-order derivative. In this paper,we connect a general family of learning objectives including score matching to Wassersteingradient flows. This connection enables us to ... | We present a scalable approximation to a wide range of EBM objectives, and applications in implicit VAEs and WAEs | 1,279 | scitldr |
This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks. The framework can successfully train both deep discriminative models and deep generative models in compl... | This paper develops a principled method for continual learning in deep models. | 1,280 | scitldr |
In partially observable (PO) environments, deep reinforcement learning (RL) agents often suffer from unsatisfactory performance, since two problems need to be tackled together: how to extract information from the raw observations to solve the task, and how to improve the policy. In this study, we propose an RL algorith... | A deep RL algorithm for solving POMDPs by auto-encoding the underlying states using a variational recurrent model | 1,281 | scitldr |
This paper formalises the problem of online algorithm selection in the context of Reinforcement Learning (RL). The setup is as follows: given an episodic task and a finite number of off-policy RL algorithms, a meta-algorithm has to decide which RL algorithm is in control during the next episode so as to maximize the ex... | This paper formalises the problem of online algorithm selection in the context of Reinforcement Learning. | 1,282 | scitldr |
In this paper, we present a new generative model for learning latent embeddings. Compared to the classical generative process, where each observed data point is generated from an individual latent variable, our approach assumes a global latent variable to generate the whole set of observed data points. We then propose ... | We propose a new latent variable model to learn latent embeddings for some high-dimensional data. | 1,283 | scitldr |
We improve the robustness of deep neural nets to adversarial attacks by using an interpolating function as the output activation. This data-dependent activation function remarkably improves both classification accuracy and stability to adversarial perturbations. Together with the total variation minimization of adversa... | We proposal strategies for adversarial defense based on data dependent activation function, total variation minimization, and training data augmentation | 1,284 | scitldr |
This work presents a scalable solution to continuous visual speech recognition. To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3,886 hours of video). In tandem, we designed and trained an integrated lipreading system... | This work presents a scalable solution to continuous visual speech recognition. | 1,285 | scitldr |
Previous work has found difficulty developing generative models based on variational autoencoders (VAEs) for text. To address the problem of the decoder ignoring information from the encoder (posterior collapse), these previous models weaken the capacity of the decoder to force the model to use information from latent ... | We propose a model of variational autoencoders for text modeling without weakening the decoder, which improves the quality of text generation and interpretability of acquired representations. | 1,286 | scitldr |
Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent reports prune deep networks at the cost of only a marginal loss in accuracy and achieve a sizable reduction in model size. This hints at the possib... | We demonstrate that large, but pruned models (large-sparse) outperform their smaller, but dense (small-dense) counterparts with identical memory footprint. | 1,287 | scitldr |
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying the model architecture or fine-tuning on attribute-specific data and en... | We control the topic and sentiment of text generation (almost) without any training. | 1,288 | scitldr |
Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering. Such an organization significantly constricts the types of shared structure that can be learned. The necessity of parallel ordering for deep MTL is first tested by comparing it with permuted ordering of shared la... | Relaxing the constraint of shared hierarchies enables more effective deep multitask learning. | 1,289 | scitldr |
We propose a generic framework to calibrate accuracy and confidence (score) of a prediction through stochastic inferences in deep neural networks. We first analyze relation between variation of multiple model parameters for a single example inference and variance of the corresponding prediction scores by Bayesian model... | We propose a framework to learn confidence-calibrated networks by designing a novel loss function that incorporates predictive uncertainty estimated through stochastic inferences. | 1,290 | scitldr |
Real-life control tasks involve matters of various substances---rigid or soft bodies, liquid, gas---each with distinct physical behaviors. This poses challenges to traditional rigid-body physics engines. Particle-based simulators have been developed to model the dynamics of these complex scenes; however, relying on app... | Learning particle dynamics with dynamic interaction graphs for simulating and control rigid bodies, deformable objects, and fluids. | 1,291 | scitldr |
Generative Adversarial Networks (GANs), when trained on large datasets with diverse modes, are known to produce conflated images which do not distinctly belong to any of the modes. We hypothesize that this problem occurs due to the interaction between two facts: For datasets with large variety, it is likely that the mo... | We introduce theory to explain the failure of GANs on complex datasets and propose a solution to fix it. | 1,292 | scitldr |
To leverage crowd-sourced data to train multi-speaker text-to-speech (TTS) models that can synthesize clean speech for all speakers, it is essential to learn disentangled representations which can independently control the speaker identity and noise in generated signals. However, learning such representations can be ch... | Data augmentation and adversarial training are very effective for disentangling correlated speaker and noise, enabling independent control of each attribute for text-to-speech synthesis. | 1,293 | scitldr |
LSTM-based language models exhibit compositionality in their representations, but how this behavior emerges over the course of training has not been explored. Analyzing synthetic data experiments with contextual decomposition, we find that LSTMs learn long-range dependencies compositionally by building them from shorte... | LSTMs learn long-range dependencies compositionally by building them from shorter constituents over the course of training. | 1,294 | scitldr |
Learning a deep neural network requires solving a challenging optimization problem: it is a high-dimensional, non-convex and non-smooth minimization problem with a large number of terms. The current practice in neural network optimization is to rely on the stochastic gradient descent (SGD) algorithm or its adaptive var... | We train neural networks by locally linearizing them and using a linear SVM solver (Frank-Wolfe) at each iteration. | 1,295 | scitldr |
In this paper, we show how novel transfer reinforcement learning techniques can be applied to the complex task of target-driven navigation using the photorealisticAI2THOR simulator. Specifically, we build on the concept of Universal SuccessorFeatures with an A3C agent. We introduce the novel architectural1contribution ... | We present an improved version of Universal Successor Features based DRL method which can improve the transfer learning of agents. | 1,296 | scitldr |
A major challenge in learning image representations is the disentangling of the factors of variation underlying the image formation. This is typically achieved with an autoencoder architecture where a subset of the latent variables is constrained to correspond to specific factors, and the rest of them are considered nu... | A method for learning image representations that are good for both disentangling factors of variation and obtaining faithful reconstructions. | 1,297 | scitldr |
We propose a new notion of'non-linearity' of a network layer with respect to an input batch that is based on its proximity to a linear system, which is reflected in the non-negative rank of the activation matrix. We measure this non-linearity by applying non-negative factorization to the activation matrix. Considering ... | We use the non-negative rank of ReLU activation matrices as a complexity measure and show it (negatively) correlates with good generalization. | 1,298 | scitldr |
Deep neural networks have achieved state-of-the-art performance in various fields, but they have to be scaled down to be used for real-world applications. As a means to reduce the size of a neural network while preserving its performance, knowledge transfer has brought a lot of attention. One popular method of knowledg... | The goal of this paper is to get the effect of multiple teacher networks by exploiting stochastic blocks and skip connections. | 1,299 | scitldr |
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