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Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is therefore heteroscedastic. Classical exploration strategies such as upper confidence bound algorithms and Thompson sampling fail to appropri... | We develop a practical extension of Information-Directed Sampling for Reinforcement Learning, which accounts for parametric uncertainty and heteroscedasticity in the return distribution for exploration. |
We address the problem of learning structured policies for continuous control. In traditional reinforcement learning, policies of agents are learned by MLPs which take the concatenation of all observations from the environment as input for predicting actions. In this work, we propose NerveNet to explicitly model the st... | using graph neural network to model structural information of the agents to improve policy and transferability |
Real-world tasks are often highly structured. Hierarchical reinforcement learning (HRL) has attracted research interest as an approach for leveraging the hierarchical structure of a given task in reinforcement learning (RL). However, identifying the hierarchical policy structure that enhances the performance of RL is n... | This paper presents a hierarchical reinforcement learning framework based on deterministic option policies and mutual information maximization. |
Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables. However, the inability to effectively visualize these relationships has led to DNNs being characterized as black boxes and consequently limited their application... | We introduce and validate hierarchical local interpretations, the first technique to automatically search for and display important interactions for individual predictions made by LSTMs and CNNs. |
Principal Filter Analysis (PFA) is an easy to implement, yet effective method for neural network compression. PFA exploits the intrinsic correlation between filter responses within network layers to recommend a smaller network footprint. We propose two compression algorithms: the first allows a user to specify the prop... | We propose an easy to implement, yet effective method for neural network compression. PFA exploits the intrinsic correlation between filter responses within network layers to recommend a smaller network footprints. |
We propose a method to efficiently learn diverse strategies in reinforcement learning for query reformulation in the tasks of document retrieval and question answering. 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... | Multiple diverse query reformulation agents trained with reinforcement learning to improve search engines. |
Network Embeddings (NEs) map the nodes of a given network into $d$-dimensional Euclidean space $\mathbb{R}^d$. Ideally, this mapping is such that 'similar' nodes are mapped onto nearby points, such that the NE can be used for purposes such as link prediction (if 'similar' means being 'more likely to be connected') or c... | We introduce a network embedding method that accounts for prior information about the network, yielding superior empirical performance. |
This paper studies a class of adaptive gradient based momentum algorithms that update the search directions and learning rates simultaneously using past gradients. This class, which we refer to as the ''``Adam-type'', includes the popular algorithms such as Adam, AMSGrad, AdaGrad. Despite their popularity in training ... | We analyze convergence of Adam-type algorithms and provide mild sufficient conditions to guarantee their convergence, we also show violating the conditions can makes an algorithm diverge. |
This research paper describes a simplistic architecture named as AANN: Absolute Artificial Neural Network, which can be used to create highly interpretable representations of the input data. These representations are generated by penalizing the learning of the network in such a way that those learned representations co... | Tied weights auto-encoder with abs function as activation function, learns to do classification in the forward direction and regression in the backward direction due to specially defined cost function. |
Current state-of-the-art relation extraction methods typically rely on a set of lexical, syntactic, and semantic features, explicitly computed in a pre-processing step. Training feature extraction models requires additional annotated language resources, which severely restricts the applicability and portability of rela... | We propose a Transformer based relation extraction model that uses pre-trained language representations instead of explicit linguistic features. |
Neural networks have recently had a lot of success for many tasks. However, neural
network architectures that perform well are still typically designed manually
by experts in a cumbersome trial-and-error process. We propose a new method
to automatically search for well-performing CNN architectures based on a simple
... | We propose a simple and efficent method for architecture search for convolutional neural networks. |
We propose GraphGAN - the first implicit generative model for graphs that enables to mimic real-world networks.
We pose the problem of graph generation as learning the distribution of biased random walks over a single input graph.
Our model is based on a stochastic neural network that generates discrete output sample... | Using GANs to generate graphs via random walks. |
The ability of a classifier to recognize unknown inputs is important for many classification-based systems. We discuss the problem of simultaneous classification and novelty detection, i.e. determining whether an input is from the known set of classes and from which specific class, or from an unknown domain and does no... | We propose to solve a problem of simultaneous classification and novelty detection within the GAN framework. |
Verifying a person's identity based on their voice is a challenging, real-world problem in biometric security. A crucial requirement of such speaker verification systems is to be domain robust. Performance should not degrade even if speakers are talking in languages not seen during training. To this end, we present a... | Speaker verificaiton performance can be significantly improved by adapting the model to in-domain data using Generative Adversarial Networks. Furthermore, the adaptation can be performed in an unsupervised way. |
Learning disentangling representations of the independent factors of variations that explain the data in an unsupervised setting is still a major challenge. In the following paper we address the task of disentanglement and introduce a new state-of-the-art approach called Non-synergistic variational Autoencoder (Non-Syn... | Minimising the synergistic mutual information within the latents and the data for the task of disentanglement using the VAE framework. |
Metric embeddings are immensely useful representations of associations between entities (images, users, search queries, words, and more). Embeddings are learned by optimizing a loss objective of the general form of a sum over example associations. Typically, the optimization uses stochastic gradient updates o... | Accelerating SGD by arranging examples differently |
Ubuntu dialogue corpus is the largest public available dialogue corpus to make it feasible to build end-to-end
deep neural network models directly from the conversation data. One challenge of Ubuntu dialogue corpus is
the large number of out-of-vocabulary words. In this paper we proposed an algorithm which combines th... | Combine information between pre-built word embedding and task-specific word representation to address out-of-vocabulary issue |
Deep learning has shown that learned functions can dramatically outperform hand-designed functions on perceptual tasks. Analogously, this suggests that learned update functions may similarly outperform current hand-designed optimizers, especially for specific tasks. However, learned optimizers are notoriously difficult... | We analyze problems when training learned optimizers, address those problems via variational optimization using two complementary gradient estimators, and train optimizers that are 5x faster in wall-clock time than baseline optimizers (e.g. Adam). |
Asynchronous distributed gradient descent algorithms for training of deep neural
networks are usually considered as inefficient, mainly because of the Gradient delay
problem. In this paper, we propose a novel asynchronous distributed algorithm
that tackles this limitation by well-thought-out averaging of model updat... | A method for an efficient asynchronous distributed training of deep learning models along with theoretical regret bounds. |
Neural network quantization is becoming an industry standard to efficiently deploy deep learning models on hardware platforms, such as CPU, GPU, TPU, and FPGAs. However, we observe that the conventional quantization approaches are vulnerable to adversarial attacks. This paper aims to raise people's awareness about the ... | We designed a novel quantization methodology to jointly optimize the efficiency and robustness of deep learning models. |
Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long term dependencies. In backpropagation through time settings, these issues are ti... | A modification for existing RNN architectures which allows them to skip state updates while preserving the performance of the original architectures. |
We propose a fast second-order method that can be used as a drop-in replacement for current deep learning solvers. Compared to stochastic gradient descent (SGD), it only requires two additional forward-mode automatic differentiation operations per iteration, which has a computational cost comparable to two standard for... | A fast second-order solver for deep learning that works on ImageNet-scale problems with no hyper-parameter tuning |
The recently presented idea to learn heuristics for combinatorial optimization problems is promising as it can save costly development. However, to push this idea towards practical implementation, we need better models and better ways of training. We contribute in both directions: we propose a model based on attention ... | Attention based model trained with REINFORCE with greedy rollout baseline to learn heuristics with competitive results on TSP and other routing problems |
We propose an efficient online hyperparameter optimization method which uses a joint dynamical system to evaluate the gradient with respect to the hyperparameters. While similar methods are usually limited to hyperparameters with a smooth impact on the model, we show how to apply it to the probability of dropout in neu... | An algorithm for optimizing regularization hyper-parameters during training |
Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modelling benchmarks. However, these have been evaluated using differing codebases and limited computational resources, which represent uncontrolled sources of experimental vari... | Show that LSTMs are as good or better than recent innovations for LM and that model evaluation is often unreliable. |
Residual and skip connections play an important role in many current
generative models. Although their theoretical and numerical advantages
are understood, their role in speech enhancement systems has not been
investigated so far. When performing spectral speech enhancement,
residual connections are very simi... | We show how using skip connections can make speech enhancement models more interpretable, as it makes them use similar mechanisms that have been explored in the DSP literature. |
Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probabilistic inference in deep neural networks, variational Bayes (VB) is theoretically grounded, generally applicable, and computationally effic... | A method for eliminating gradient variance and automatically tuning priors for effective training of bayesian neural networks |
Skills learned through (deep) reinforcement learning often generalizes poorly
across tasks and re-training is necessary when presented with a new task. We
present a framework that combines techniques in formal methods with reinforcement
learning (RL) that allows for the convenient specification of complex temporal
... | A formal method's approach to skill composition in reinforcement learning tasks |
The application of multi-modal generative models by means of a Variational Auto Encoder (VAE) is an upcoming research topic for sensor fusion and bi-directional modality exchange.
This contribution gives insights into the learned joint latent representation and shows that expressiveness and coherence are decisive prop... | Deriving a general formulation of a multi-modal VAE from the joint marginal log-likelihood. |
We build on auto-encoding sequential Monte Carlo (AESMC): a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad family of structured probabilistic models. Our approach relies on the efficiency of sequential Monte Carlo (SMC) for performing inference in st... | We build on auto-encoding sequential Monte Carlo, gain new theoretical insights and develop an improved training procedure based on those insights. |
A key component for many reinforcement learning agents is to learn a value function, either for policy evaluation or control. Many of the algorithms for learning values, however, are designed for linear function approximation---with a fixed basis or fixed representation. Though there have been a few sound extensions to... | We propose an architecture for learning value functions which allows the use of any linear policy evaluation algorithm in tandem with nonlinear feature learning. |
Large-scale Long Short-Term Memory (LSTM) cells are often the building blocks of many state-of-the-art algorithms for tasks in Natural Language Processing (NLP). However, LSTMs are known to be computationally inefficient because the memory capacity of the models depends on the number of parameters, and the inherent rec... | We propose simple, but effective, low-rank matrix factorization (MF) algorithms to speed up in running time, save memory, and improve the performance of LSTMs. |
Manipulation and re-use of images in scientific publications is a recurring problem, at present lacking a scalable solution. Existing tools for detecting image duplication are mostly manual or semi-automated, despite the fact that generating data for a learning-based approach is straightforward, as we here illustrate... | A forensic metric to determine if a given image is a copy (with possible manipulation) of another image from a given dataset. |
Training generative adversarial networks is unstable in high-dimensions as the true data distribution tends to be concentrated in a small fraction of the ambient space. The discriminator is then quickly able to classify nearly all generated samples as fake, leaving the generator without meaningful gradients and causing... | Stable GAN training in high dimensions by using an array of discriminators, each with a low dimensional view of generated samples |
We present a novel method to precisely impose tree-structured category information onto word-embeddings, resulting in ball embeddings in higher dimensional spaces (N-balls for short). Inclusion relations among N-balls implicitly encode subordinate relations among categories. The similarity measurement in terms of the c... | we show a geometric method to perfectly encode categroy tree information into pre-trained word-embeddings. |
For the challenging semantic image segmentation task the best performing models
have traditionally combined the structured modelling capabilities of Conditional
Random Fields (CRFs) with the feature extraction power of CNNs. In more recent
works however, CRF post-processing has fallen out of favour. We argue that th... | We propose Convolutional CRFs a fast, powerful and trainable alternative to Fully Connected CRFs. |
Deep Learning NLP domain lacks procedures for the analysis of model robustness. In this paper we propose a framework which validates robustness of any Question Answering model through model explainers. We propose that output of a robust model should be invariant to alterations that do not change its semantics. We test ... | We propose a model agnostic approach to validation of Q&A system robustness and demonstrate results on state-of-the-art Q&A models. |
In this paper, we propose a mix-generator generative adversarial networks (PGAN) model that works in parallel by mixing multiple disjoint generators to approximate a complex real distribution. In our model, we propose an adjustment component that collects all the generated data points from the generators, learns the bo... | multi generator to capture Pdata, solve the competition and one-beat-all problem |
We capitalize on the natural compositional structure of images in order to learn object segmentation with weakly labeled images. The intuition behind our approach is that removing objects from images will yield natural images, however removing random patches will yield unnatural images. We leverage this signal to devel... | Weakly-supervised image segmentation using compositional structure of images and generative models. |
Adversarial examples are a pervasive phenomenon of machine learning models where seemingly imperceptible perturbations to the input lead to misclassifications for otherwise statistically accurate models. We propose a geometric framework, drawing on tools from the manifold reconstruction literature, to analyze the high-... | We present a geometric framework for proving robustness guarantees and highlight the importance of codimension in adversarial examples. |
Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues, learn morphology, and move us closer to completely end-to-end translation systems. Unfortunately, they are also very brittle and easily falter when presented with noisy data. In this paper, we confront NMT models with syntheti... | CharNMT is brittle |
As neural networks grow deeper and wider, learning networks with hard-threshold activations is becoming increasingly important, both for network quantization, which can drastically reduce time and energy requirements, and for creating large integrated systems of deep networks, which may have non-differentiable componen... | We learn deep networks of hard-threshold units by setting hidden-unit targets using combinatorial optimization and weights by convex optimization, resulting in improved performance on ImageNet. |
The robust and efficient recognition of visual relations in images is a hallmark of biological vision. Here, we argue that, despite recent progress in visual recognition, modern machine vision algorithms are severely limited in their ability to learn visual relations. Through controlled experiments, we demonstrate that... | Using a novel, controlled, visual-relation challenge, we show that same-different tasks critically strain the capacity of CNNs; we argue that visual relations can be better solved using attention-mnemonic strategies. |
Visual Active Tracking (VAT) aims at following a target object by autonomously controlling the motion system of a tracker given visual observations. Previous work has shown that the tracker can be trained in a simulator via reinforcement learning and deployed in real-world scenarios. However, during training, such a me... | We propose AD-VAT, where the tracker and the target object, viewed as two learnable agents, are opponents and can mutually enhance during training. |
Identifying the hypernym relations that hold between words is a fundamental task in NLP. Word embedding methods have recently shown some capability to encode hypernymy. However, such methods tend not to explicitly encode the hypernym hierarchy that exists between words. In this paper, we propose a method to learn a hie... | We presented a method to jointly learn a Hierarchical Word Embedding (HWE) using a corpus and a taxonomy for identifying the hypernymy relations between words. |
While self-organizing principles have motivated much of early learning models, such principles have rarely been included in deep learning architectures. Indeed, from a supervised learning perspective it seems that topographic constraints are rather decremental to optimal performance. Here we study a network model that ... | integration of self-organization and supervised learning in a hierarchical neural network |
Quantization of a neural network has an inherent problem called accumulated quantization error, which is the key obstacle towards ultra-low precision, e.g., 2- or 3-bit precision. To resolve this problem, we propose precision highway, which forms an end-to-end high-precision information flow while performing the ultra-... | precision highway; a generalized concept of high-precision information flow for sub 4-bit quantization |
The vast majority of natural sensory data is temporally redundant. For instance, video frames or audio samples which are sampled at nearby points in time tend to have similar values. Typically, deep learning algorithms take no advantage of this redundancy to reduce computations. This can be an obscene waste of ener... | An algorithm for training neural networks efficiently on temporally redundant data. |
Information bottleneck (IB) is a method for extracting information from one random variable X that is relevant for predicting another random variable Y. To do so, IB identifies an intermediate "bottleneck" variable T that has low mutual information I(X;T) and high mutual information I(Y;T). The "IB curve" characterizes... | Information bottleneck behaves in surprising ways whenever the output is a deterministic function of the input. |
We prove, under two sufficient conditions, that idealised models can have no adversarial examples. We discuss which idealised models satisfy our conditions, and show that idealised Bayesian neural networks (BNNs) satisfy these. We continue by studying near-idealised BNNs using HMC inference, demonstrating the theoretic... | We prove that idealised Bayesian neural networks can have no adversarial examples, and give empirical evidence with real-world BNNs. |
Deep neural networks are susceptible to adversarial attacks. In computer vision, well-crafted perturbations to images can cause neural networks to make mistakes such as confusing a cat with a computer. Previous adversarial attacks have been designed to degrade performance of models or cause machine learning models to p... | We introduce the first instance of adversarial attacks that reprogram the target model to perform a task chosen by the attacker---without the attacker needing to specify or compute the desired output for each test-time input. |
As shown in recent research, deep neural networks can perfectly fit randomly labeled data, but with very poor accuracy on held out data. This phenomenon indicates that loss functions such as cross-entropy are not a reliable indicator of generalization. This leads to the crucial question of how generalization gap should... | We develop a new scheme to predict the generalization gap in deep networks with high accuracy. |
We propose a new algorithm to learn a one-hidden-layer convolutional neural network where both the convolutional weights and the outputs weights are parameters to be learned. Our algorithm works for a general class of (potentially overlapping) patches, including commonly used structures for computer vision tasks. Our a... | We propose an algorithm for provably recovering parameters (convolutional and output weights) of a convolutional network with overlapping patches. |
Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unlabeled) data. Convergence problems during training are overcome by Wasserstein GANs which minimize the distance between the model and the empirical distribution in terms of a dif... | A new regularization term can improve your training of wasserstein gans |
We introduce a new method for training GANs by applying the Wasserstein-2 metric proximal on the generators.
The approach is based on the gradient operator induced by optimal transport, which connects the geometry of sample space and parameter space in implicit deep generative models. From this theory, we obtain an e... | We propose the Wasserstein proximal method for training GANs. |
Influence diagrams provide a modeling and inference framework for sequential decision problems, representing the probabilistic knowledge by a Bayesian network and the preferences of an agent by utility functions over the random variables and decision variables.
MDPs and POMDPS, widely used for planning under uncertain... | This paper introduces an elimination based heuristic function for sequential decision making, suitable for guiding AND/OR search algorithms for solving influence diagrams. |
Probabilistic Neural Networks deal with various sources of stochasticity: input noise, dropout, stochastic neurons, parameter uncertainties modeled as random variables, etc.
In this paper we revisit a feed-forward propagation approach that allows one to estimate for each neuron its mean and variance w.r.t. all mention... | Approximating mean and variance of the NN output over noisy input / dropout / uncertain parameters. Analytic approximations for argmax, softmax and max layers. |
Generative Adversarial Networks are one of the leading tools in generative modeling, image editing and content creation.
However, they are hard to train as they require a delicate balancing act between two deep networks fighting a never ending duel. Some of the most promising adversarial models today minimize a Wasse... | A discriminator that is not easily fooled by adversarial example makes GAN training more robust and leads to a smoother objective. |
We propose a method to learn stochastic activation functions for use in probabilistic neural networks.
First, we develop a framework to embed stochastic activation functions based on Gaussian processes in probabilistic neural networks.
Second, we analytically derive expressions for the propagation of means and covari... | We model the activation function of each neuron as a Gaussian Process and learn it alongside the weight with Variational Inference. |
Recent results from linear algebra stating that any matrix can be decomposed into products of diagonal and circulant matrices has lead to the design of compact deep neural network architectures that perform well in practice. In this paper, we bridge the gap between these good empirical results
and the theoretical app... | We provide a theoretical study of the properties of Deep circulant-diagonal ReLU Networks and demonstrate that they are bounded width universal approximators. |
Camera drones, a rapidly emerging technology, offer people the ability to remotely inspect an environment with a high degree of mobility and agility. However, manual remote piloting of a drone is prone to errors. In contrast, autopilot systems can require a significant degree of environmental knowledge and are not nece... | StarHopper is a novel touch screen interface for efficient and flexible object-centric camera drone navigation |
Recurrent neural networks (RNN), convolutional neural networks (CNN) and self-attention networks (SAN) are commonly used to produce context-aware representations. RNN can capture long-range dependency but is hard to parallelize and not time-efficient. CNN focuses on local dependency but does not perform well on some ta... | A self-attention network for RNN/CNN-free sequence encoding with small memory consumption, highly parallelizable computation and state-of-the-art performance on several NLP tasks |
End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering... | A new state-of-the-art model for multi-evidence question answering using coarse-grain fine-grain hierarchical attention. |
Generative adversarial networks (GANs) are an expressive class of neural generative models with tremendous success in modeling high-dimensional continuous measures. In this paper, we present a scalable method for unbalanced optimal transport (OT) based on the generative-adversarial framework. We formulate unbalanced OT... | We propose new methodology for unbalanced optimal transport using generative adversarial networks. |
Extracting saliency maps, which indicate parts of the image important to classification, requires many tricks to achieve satisfactory performance when using classifier-dependent methods. Instead, we propose classifier-agnostic saliency map extraction, which finds all parts of the image that any classifier could use, no... | We propose a new saliency map extraction method which results in extracting higher quality maps. |
Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs). However, previous methods often fail in challenging cases, in particular, when an image has multiple target instances and a translation task involves significa... | We propose a novel method to incorporate the set of instance attributes for image-to-image translation. |
Deep neural networks (DNNs) generalize remarkably well without explicit regularization even in the strongly over-parametrized regime where classical learning theory would instead predict that they would severely overfit. While many proposals for some kind of implicit regularization have been made to rationalise this... | The parameter-function map of deep networks is hugely biased; this can explain why they generalize. We use PAC-Bayes and Gaussian processes to obtain nonvacuous bounds. |
We establish a theoretical link between evolutionary algorithms and variational parameter optimization of probabilistic generative models with binary hidden variables.
While the novel approach is independent of the actual generative model, here we use two such models to investigate its applicability and scalability: a... | We present Evolutionary EM as a novel algorithm for unsupervised training of generative models with binary latent variables that intimately connects variational EM with evolutionary optimization |
While deep neural networks have achieved groundbreaking prediction results in many tasks, there is a class of data where existing architectures are not optimal -- sequences of probability distributions. Performing forward prediction on sequences of distributions has many important applications. However, there are two m... | We propose an efficient recurrent network model for forward prediction on time-varying distributions. |
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over thei... | A novel approach to processing graph-structured data by neural networks, leveraging attention over a node's neighborhood. Achieves state-of-the-art results on transductive citation network tasks and an inductive protein-protein interaction task. |
While bigger and deeper neural network architectures continue to advance the state-of-the-art for many computer vision tasks, real-world adoption of these networks is impeded by hardware and speed constraints. Conventional model compression methods attempt to address this problem by modifying the architecture manually ... | A novel reinforcement learning based approach to compress deep neural networks with knowledge distillation |
Recent advances in conditional image generation tasks, such as image-to-image translation and image inpainting, are largely accounted to the success of conditional GAN models, which are often optimized by the joint use of the GAN loss with the reconstruction loss. However, we reveal that this training recipe shared by ... | We prove that the mode collapse in conditional GANs is largely attributed to a mismatch between reconstruction loss and GAN loss and introduce a set of novel loss functions as alternatives for reconstruction loss. |
Generative models are important tools to capture and investigate the properties of complex empirical data. Recent developments such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) use two very similar, but \textit{reverse}, deep convolutional architectures, one to generate and one to extr... | We use causal inference to characterise the architecture of generative models |
Many deep reinforcement learning approaches use graphical state representations,
this means visually distinct games that share the same underlying structure cannot
effectively share knowledge. This paper outlines a new approach for learning
underlying game state embeddings irrespective of the visual rendering of the... | An approach to learning a shared embedding space between visually distinct games. |
We study discrete time dynamical systems governed by the state equation $h_{t+1}=ϕ(Ah_t+Bu_t)$. Here A,B are weight matrices, ϕ is an activation function, and $u_t$ is the input data. This relation is the backbone of recurrent neural networks (e.g. LSTMs) which have broad applications in sequential learning tasks. We u... | We study the state equation of a recurrent neural network. We show that SGD can efficiently learn the unknown dynamics from few input/output observations under proper assumptions. |
Although deep neural networks show their extraordinary power in various tasks, they are not feasible for deploying such large models on embedded systems due to high computational cost and storage space limitation. The recent work knowledge distillation (KD) aims at transferring model knowledge from a well-trained teach... | A new knowledge distill method for transfer learning |
We present a simple and general method to train a single neural network executable at different widths (number of channels in a layer), permitting instant and adaptive accuracy-efficiency trade-offs at runtime. Instead of training individual networks with different width configurations, we train a shared network with s... | We present a simple and general method to train a single neural network executable at different widths (number of channels in a layer), permitting instant and adaptive accuracy-efficiency trade-offs at runtime. |
Measuring visual (dis)similarity between two or more instances within a data distribution is a fundamental task in many applications, specially in image retrieval. Theoretically, non-metric distances are able to generate a more complex and accurate similarity model than metric distances, provided that the non-linear da... | Similarity network to learn a non-metric visual similarity estimation between a pair of images |
Training a model to perform a task typically requires a large amount of data from the domains in which the task will be applied.
However, it is often the case that data are abundant in some domains but scarce in others. Domain adaptation deals with the challenge of adapting a model trained from a data-rich source doma... | A new cyclic adversarial learning augmented with auxiliary task model which improves domain adaptation performance in low resource supervised and unsupervised situations |
Nodes residing in different parts of a graph can have similar structural roles within their local network topology. The identification of such roles provides key insight into the organization of networks and can also be used to inform machine learning on graphs. However, learning structural representations of nodes is ... | We develop a method for learning structural signatures in networks based on the diffusion of spectral graph wavelets. |
Driving simulators play an important role in vehicle research. However, existing virtual reality simulators do not give users a true sense of presence. UniNet is our driving simulator, designed to allow users to interact with and visualize simulated traffic in mixed reality. It is powered by SUMO and Unity. UniNet's mo... | A mixed reality driving simulator using stereo cameras and passthrough VR evaluated in a user study with 24 participants. |
We consider the problem of improving kernel approximation via feature maps. These maps arise as Monte Carlo approximation to integral representations of kernel functions and scale up kernel methods for larger datasets. We propose to use more efficient numerical integration technique to obtain better estimates of the in... | Quadrature rules for kernel approximation. |
Human world knowledge is both structured and flexible. When people see an object, they represent it not as a pixel array but as a meaningful arrangement of semantic parts. Moreover, when people refer to an object, they provide descriptions that are not merely true but also relevant in the current context. Here, we comb... | How to build neural-speakers/listeners that learn fine-grained characteristics of 3D objects, from referential language. |
Object-based factorizations provide a useful level of abstraction for interacting with the world. Building explicit object representations, however, often requires supervisory signals that are difficult to obtain in practice. We present a paradigm for learning object-centric representations for physical scene understan... | We present a framework for learning object-centric representations suitable for planning in tasks that require an understanding of physics. |
We study the error landscape of deep linear and nonlinear neural networks with the squared error loss. Minimizing the loss of a deep linear neural network is a nonconvex problem, and despite recent progress, our understanding of this loss surface is still incomplete. For deep linear networks, we present necessary and s... | We provide efficiently checkable necessary and sufficient conditions for global optimality in deep linear neural networks, with some initial extensions to nonlinear settings. |
Recurrent auto-encoder model can summarise sequential data through an encoder structure into a fixed-length vector and then reconstruct into its original sequential form through the decoder structure. The summarised information can be used to represent time series features. In this paper, we propose relaxing the dimens... | Using recurrent auto-encoder model to extract multidimensional time series features |
We view molecule optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph.... | We introduce a graph-to-graph encoder-decoder framework for learning diverse graph translations. |
Partial differential equations (PDEs) are widely used across the physical and computational sciences. Decades of research and engineering went into designing fast iterative solution methods. Existing solvers are general purpose, but may be sub-optimal for specific classes of problems. In contrast to existing hand-craft... | We learn a fast neural solver for PDEs that has convergence guarantees. |
Variational Bayesian neural networks (BNN) perform variational inference over weights, but it is difficult to specify meaningful priors and approximating posteriors in a high-dimensional weight space. We introduce functional variational Bayesian neural networks (fBNNs), which maximize an Evidence Lower BOund (ELBO) def... | We perform functional variational inference on the stochastic processes defined by Bayesian neural networks. |
Words are not created equal. In fact, they form an aristocratic graph with a latent hierarchical structure that the next generation of unsupervised learned word embeddings should reveal. In this paper, justified by the notion of delta-hyperbolicity or tree-likeliness of a space, we propose to embed words in a Cartesian... | We embed words in the hyperbolic space and make the connection with the Gaussian word embeddings. |
Answering questions about a text frequently requires aggregating information from multiple places in that text. End-to-end neural network models, the dominant approach in the current literature, can theoretically learn how to distill and manipulate representations of the text without explicit supervision about how to d... | Memory Networks do not learn multi-hop reasoning unless we supervise them. |
Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but the underlying mathematics are not well understood. We compute deep convolutional network generators by inverting a fixed embedding operator. Therefore, they do not require to be o... | We introduce generative networks that do not require to be learned with a discriminator or an encoder; they are obtained by inverting a special embedding operator defined by a wavelet Scattering transform. |
Recurrent neural networks (RNNs) can model natural language by sequentially ''reading'' input tokens and outputting a distributed representation of each token. Due to the sequential nature of RNNs, inference time is linearly dependent on the input length, and all inputs are read regardless of their importance. Efforts ... | We propose a new model for neural speed reading that utilizes the inherent punctuation structure of a text to define effective jumping and skipping behavior. |
One of the key challenges of session-based recommender systems is to enhance users’ purchase intentions. In this paper, we formulate the sequential interactions between user sessions and a recommender agent as a Markov Decision Process (MDP). In practice, the purchase reward is delayed and sparse, and may be buried by ... | We propose the IRN architecture to augment sparse and delayed purchase reward for session-based recommendation. |
The question why deep learning algorithms generalize so well has attracted increasing
research interest. However, most of the well-established approaches,
such as hypothesis capacity, stability or sparseness, have not provided complete
explanations (Zhang et al., 2016; Kawaguchi et al., 2017). In this work, we focus... | Explaining the generalization of stochastic deep learning algorithms, theoretically and empirically, via ensemble robustness |
Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet. However, such models require many thousands of gradient-based weight updates and unique image examples for training. Ideally, the models would rapidly learn visual conc... | Few-shot learning PixelCNN |
Neural networks exhibit good generalization behavior in the
over-parameterized regime, where the number of network parameters
exceeds the number of observations. Nonetheless,
current generalization bounds for neural networks fail to explain this
phenomenon. In an attempt to bridge this gap, we study the problem of
... | We show that SGD learns two-layer over-parameterized neural networks with Leaky ReLU activations that provably generalize on linearly separable data. |
A central challenge in reinforcement learning is discovering effective policies for tasks where rewards are sparsely distributed. We postulate that in the absence of useful reward signals, an effective exploration strategy should seek out {\it decision states}. These states lie at critical junctions in the state space ... | Training agents with goal-policy information bottlenecks promotes transfer and yields a powerful exploration bonus |
Many applications in machine learning require optimizing a function whose true gradient is unknown, but where surrogate gradient information (directions that may be correlated with, but not necessarily identical to, the true gradient) is available instead. This arises when an approximate gradient is easier to compute t... | We propose an optimization method for when only biased gradients are available--we define a new gradient estimator for this scenario, derive the bias and variance of this estimator, and apply it to example problems. |
Point clouds are an important type of geometric data and have widespread use in computer graphics and vision. However, learning representations for point clouds is particularly challenging due to their nature as being an unordered collection of points irregularly distributed in 3D space. Graph convolution, a generaliza... | A GAN using graph convolution operations with dynamically computed graphs from hidden features |
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