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We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature. By taking advantage of transfer learning, we are able to efficiently use different data sources that are related to the sam... | Transfer learning for estimating causal effects using neural networks. |
Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatio-temporally coordinated activation patterns, or "motifs", are thought to be building blocks of neural representations and information processing. We here propose LeMoNADe, a new exploratory data analysis method that facilitates hunting fo... | We present LeMoNADe, an end-to-end learned motif detection method directly operating on calcium imaging videos. |
A noisy and diverse demonstration set may hinder the performances of an agent aiming to acquire certain skills via imitation learning. However, state-of-the-art imitation learning algorithms often assume the optimality of the given demonstration set.
In this paper, we address such optimal assumption by learning only f... | We propose a framework to learn a good policy through imitation learning from a noisy demonstration set via meta-training a demonstration suitability assessor. |
We introduce causal implicit generative models (CiGMs): models that allow sampling from not only the true observational but also the true interventional distributions. We show that adversarial training can be used to learn a CiGM, if the generator architecture is structured based on a given causal graph. We consider th... | We introduce causal implicit generative models, which can sample from conditional and interventional distributions and also propose two new conditional GANs which we use for training them. |
Self-normalizing discriminative models approximate the normalized probability of a class without having to compute the partition function. This property is useful to computationally-intensive neural network classifiers, as the cost of computing the partition function grows linearly with the number of classes and may be... | We prove that NCE is self-normalized and demonstrate it on datasets |
Learning word representations from large available corpora relies on the distributional hypothesis that words present in similar contexts tend to have similar meanings. Recent work has shown that word representations learnt in this manner lack sentiment information which, fortunately, can be leveraged using external kn... | Enriching word embeddings with affect information improves their performance on sentiment prediction tasks. |
Different kinds of representation learning techniques on graph have shown significant effect in downstream machine learning tasks. Recently, in order to inductively learn representations for graph structures that is unobservable during training, a general framework with sampling and aggregating (GraphSAGE) was proposed... | For unsupervised and inductive network embedding, we propose a novel approach to explore most relevant neighbors and preserve previously learnt knowledge of nodes by utilizing bi-attention architecture and introducing global bias, respectively |
Learning distributed representations for nodes in graphs is a crucial primitive in network analysis with a wide spectrum of applications. Linear graph embedding methods learn such representations by optimizing the likelihood of both positive and negative edges while constraining the dimension of the embedding vectors. ... | We argue that the generalization of linear graph embedding is not due to the dimensionality constraint but rather the small norm of embedding vectors. |
Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning. We propose the quasi-hyperbolic momentum algorithm (QHM) as an extremely simple alteration of momentum SGD, averaging a plain SGD step with a momentum step. We describe numerous connections to and identities with other alg... | Mix plain SGD and momentum (or do something similar with Adam) for great profit. |
Reinforcement Learning (RL) can model complex behavior policies for goal-directed sequential decision making tasks. A hallmark of RL algorithms is Temporal Difference (TD) learning: value function for the current state is moved towards a bootstrapped target that is estimated using the next state's value function. lambd... | A novel way to generalize lambda-returns by allowing the RL agent to decide how much it wants to weigh each of the n-step returns. |
Current end-to-end deep learning driving models have two problems: (1) Poor
generalization ability of unobserved driving environment when diversity of train-
ing driving dataset is limited (2) Lack of accident explanation ability when driving
models don’t work as expected. To tackle these two problems, rooted on the... | we proposed a new self-driving model which is composed of perception module for see and think and driving module for behave to acquire better generalization and accident explanation ability. |
Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational complexity and memory requirements. In this paper, we relax this limitation by introducing the MultI-Level Embedding (MILE) framework – a gener... | A generic framework to scale existing graph embedding techniques to large graphs. |
Anomaly detection discovers regular patterns in unlabeled data and identifies the non-conforming data points, which in some cases are the result of malicious attacks by adversaries. Learners such as One-Class Support Vector Machines (OCSVMs) have been successfully in anomaly detection, yet their performance may degrade... | A novel method to increase the resistance of OCSVMs against targeted, integrity attacks by selective nonlinear transformations of data to lower dimensions. |
In this paper, we present a layer-wise learning of stochastic neural networks (SNNs) in an information-theoretic perspective. In each layer of an SNN, the compression and the relevance are defined to quantify the amount of information that the layer contains about the input space and the target space, respectively. We ... | Learning a better neural networks' representation with Information Bottleneck principle |
The maximum mean discrepancy (MMD) between two probability measures P
and Q is a metric that is zero if and only if all moments of the two measures
are equal, making it an appealing statistic for two-sample tests. Given i.i.d. samples
from P and Q, Gretton et al. (2012) show that we can construct an unbiased
estima... | We propose an estimator for the maximum mean discrepancy, appropriate when a target distribution is only accessible via a biased sample selection procedure, and show that it can be used in a generative network to correct for this bias. |
We propose Bayesian Deep Q-Network (BDQN), a practical Thompson sampling based Reinforcement Learning (RL) Algorithm. Thompson sampling allows for targeted exploration in high dimensions through posterior sampling but is usually computationally expensive. We address this limitation by introducing uncertainty only at ... | Using Bayesian regression to estimate the posterior over Q-functions and deploy Thompson Sampling as a targeted exploration strategy with efficient trade-off the exploration and exploitation |
In this work, we propose the polynomial convolutional neural network (PolyCNN), as a new design of a weight-learning efficient variant of the traditional CNN. The biggest advantage of the PolyCNN is that at each convolutional layer, only one convolutional filter is needed for learning the weights, which we call the see... | PolyCNN only needs to learn one seed convolutional filter at each layer. This is an efficient variant of traditional CNN, with on-par performance. |
Detecting the emergence of abrupt property changes in time series is a challenging problem. Kernel two-sample test has been studied for this task which makes fewer assumptions on the distributions than traditional parametric approaches. However, selecting kernels is non-trivial in practice. Although kernel selection fo... | In this paper, we propose KL-CPD, a novel kernel learning framework for time series CPD that optimizes a lower bound of test power via an auxiliary generative model as a surrogate to the abnormal distribution. |
Theories in cognitive psychology postulate that humans use similarity as a basis
for object categorization. However, work in image classification generally as-
sumes disjoint and equally dissimilar classes to achieve super-human levels of
performance on certain datasets. In our work, we adapt notions of similarity u... | Cluster before you classify; using weak labels to improve classification |
Deep reinforcement learning algorithms that estimate state and state-action value functions have been shown to be effective in a variety of challenging domains, including learning control strategies from raw image pixels. However, algorithms that estimate state and state-action value functions typically assume a fully ... | Advantage-based regret minimization is a new deep reinforcement learning algorithm that is particularly effective on partially observable tasks, such as 1st person navigation in Doom and Minecraft. |
Recent deep multi-task learning (MTL) has been witnessed its success in alleviating data scarcity of some task by utilizing domain-specific knowledge from related tasks. Nonetheless, several major issues of deep MTL, including the effectiveness of sharing mechanisms, the efficiency of model complexity and the flexibili... | a deep multi-task learning model adapting tensor ring representation |
Neural Processes (NPs) (Garnelo et al., 2018) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data eff... | A model for regression that learns conditional distributions of a stochastic process, by incorporating attention into Neural Processes. |
Deconvolutional layers have been widely used in a variety of deep
models for up-sampling, including encoder-decoder networks for
semantic segmentation and deep generative models for unsupervised
learning. One of the key limitations of deconvolutional operations
is that they result in the so-called checkerboard prob... | Solve checkerboard problem in Deconvolutional layer by building dependencies between pixels |
In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem. To this end, a quantizing prior that leads to a multi-modal, sparse posterior distribution over weights, is introduced and a differentiable Kullback-Leibler divergence approximation ... | We quantize and prune neural network weights using variational Bayesian inference with a multi-modal, sparsity inducing prior. |
Deep neural networks (DNNs) although achieving human-level performance in many domains, have very large model size that hinders their broader applications on edge computing devices. Extensive research work have been conducted on DNN model compression or pruning. However, most of the previous work took heuristic approac... | We implement a DNN weight pruning approach that achieves the highest pruning rates. |
In this paper, we present a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. The architecture is based on the use of a semi-parametric interpolation network followed by the application of a prediction network. The interpolatio... | This paper presents a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. |
We introduce an analytic distance function for moderately sized point sets of known cardinality that is shown to have very desirable properties, both as a loss function as well as a regularizer for machine learning applications. We compare our novel construction to other point set distance functions and show proof of c... | Permutation-invariant loss function for point set prediction. |
We introduce a hierarchical model for efficient placement of computational graphs onto hardware devices, especially in heterogeneous environments with a mixture of CPUs, GPUs, and other computational devices. Our method learns to assign graph operations to groups and to allocate those groups to available devices. The g... | We introduce a hierarchical model for efficient, end-to-end placement of computational graphs onto hardware devices. |
Motion is an important signal for agents in dynamic environments, but learning to represent motion from unlabeled video is a difficult and underconstrained problem. We propose a model of motion based on elementary group properties of transformations and use it to train a representation of image motion. While most metho... | We propose of method of using group properties to learn a representation of motion without labels and demonstrate the use of this method for representing 2D and 3D motion. |
This paper introduces the concept of continuous convolution to neural networks and deep learning applications in general. Rather than directly using discretized information, input data is first projected into a high-dimensional Reproducing Kernel Hilbert Space (RKHS), where it can be modeled as a continuous function us... | This paper proposes a novel convolutional layer that operates in a continuous Reproducing Kernel Hilbert Space. |
Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on... | ImageNet-trained CNNs are biased towards object texture (instead of shape like humans). Overcoming this major difference between human and machine vision yields improved detection performance and previously unseen robustness to image distortions. |
In this work, we exploited different strategies to provide prior knowledge to commonly used generative modeling approaches aiming to obtain speaker-dependent low dimensional representations from short-duration segments of speech data, making use of available information of speaker identities. Namely, convolutional vari... | We evaluate the effectiveness of having auxiliary discriminative tasks performed on top of statistics of the posterior distribution learned by variational autoencoders to enforce speaker dependency. |
The importance-weighted autoencoder (IWAE) approach of Burda et al. defines a sequence of increasingly tighter bounds on the marginal likelihood of latent variable models. Recently, Cremer et al. reinterpreted the IWAE bounds as ordinary variational evidence lower bounds (ELBO) applied to increasingly accurate variatio... | Variational inference is biased, let's debias it. |
In this paper, we consider the problem of autonomous lane changing for self driving vehicles in a multi-lane, multi-agent setting. We present a framework that demonstrates a more structured and data efficient alternative to end-to-end complete policy learning on problems where the high-level policy is hard to formulate... | A framework that provides a policy for autonomous lane changing by learning to make high-level tactical decisions with deep reinforcement learning, and maintaining a tight integration with a low-level controller to take low-level actions. |
Despite the recent successes in robotic locomotion control, the design of robot relies heavily on human engineering. Automatic robot design has been a long studied subject, but the recent progress has been slowed due to the large combinatorial search space and the difficulty in evaluating the found candidates. To addre... | Automatic robotic design search with graph neural networks |
Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks only, which is in contrast with advances in generative models for images and text. Is it possible to transfer this progress to the domain of graphs? We propose to sid... | We demonstate an autoencoder for graphs. |
Long Short-Term Memory (LSTM) is one of the most widely used recurrent structures in sequence modeling. Its goal is to use gates to control the information flow (e.g., whether to skip some information/transformation or not) in the recurrent computations, although its practical implementation based on soft gates only pa... | We propose a new algorithm for LSTM training by learning towards binary-valued gates which we shown has many nice properties. |
We present a personalized recommender system using neural network for recommending
products, such as eBooks, audio-books, Mobile Apps, Video and Music.
It produces recommendations based on customer’s implicit feedback history such
as purchases, listens or watches. Our key contribution is to formulate recommendation
... | Improving recommendations using time sensitive modeling with neural networks in multiple product categories on a retail website |
Deep Learning (DL) algorithms based on Generative Adversarial Network (GAN) have demonstrated great potentials in computer vision tasks such as image restoration. Despite the rapid development of image restoration algorithms using DL and GANs, image restoration for specific scenarios, such as medical image enhancement ... | Couple the GAN based image restoration framework with another task-specific network to generate realistic image while preserving task-specific features. |
Unsupervised anomaly detection on multi- or high-dimensional data is of great importance in both fundamental machine learning research and industrial applications, for which density estimation lies at the core. Although previous approaches based on dimensionality reduction followed by density estimation have made fruit... | An end-to-end trained deep neural network that leverages Gaussian Mixture Modeling to perform density estimation and unsupervised anomaly detection in a low-dimensional space learned by deep autoencoder. |
Generalization from limited examples, usually studied under the umbrella of meta-learning, equips learning techniques with the ability to adapt quickly in dynamical environments and proves to be an essential aspect of lifelong learning. In this paper, we introduce the Projective Subspace Networks (PSN), a deep learning... | We proposed Projective Subspace Networks for few-shot and semi-supervised few-shot learning |
This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning. To investigate this question, we consider an instantiation of this hypothesis evaluated on the Arcade Learning Element (ALE). In this study, we develop an a... | We investigate contingency-awareness and controllable aspects in exploration and achieve state-of-the-art performance on Montezuma's Revenge without expert demonstrations. |
Disentangling factors of variation has always been a challenging problem in representation learning. Existing algorithms suffer from many limitations, such as unpredictable disentangling factors, bad quality of generated images from encodings, lack of identity information, etc. In this paper, we proposed a supervised a... | We proposed a supervised algorithm, DNA-GAN, to disentangle multiple attributes of images. |
Representations learnt through deep neural networks tend to be highly informative, but opaque in terms of what information they learn to encode. We introduce an approach to probabilistic modelling that learns to represent data with two separate deep representations: an invariant representation that encodes the informat... | This paper presents a novel latent-variable generative modelling technique that enables the representation of global information into one latent variable and local information into another latent variable. |
Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expe... | We approach to the problem of active learning as a core-set selection problem and show that this approach is especially useful in the batch active learning setting which is crucial when training CNNs. |
Recurrent neural networks are known for their notorious exploding and vanishing gradient problem (EVGP). This problem becomes more evident in tasks where the information needed to correctly solve them exist over long time scales, because EVGP prevents important gradient components from being back-propagated adequately ... | A simple algorithm to improve optimization and handling of long term dependencies in LSTM |
Convolutional Neural Networks (CNNs) significantly improve the state-of-the-art for many applications, especially in computer vision. However, CNNs still suffer from a tendency to confidently classify out-distribution samples from unknown classes into pre-defined known classes. Further, they are also vulnerable to adve... | Properly training CNNs with dustbin class increase their robustness to adversarial attacks and their capacity to deal with out-distribution samples. |
Modern deep artificial neural networks have achieved impressive results through models with very large capacity---compared to the number of training examples---that control overfitting with the help of different forms of regularization. Regularization can be implicit, as is the case of stochastic gradient descent or pa... | In a deep convolutional neural network trained with sufficient level of data augmentation, optimized by SGD, explicit regularizers (weight decay and dropout) might not provide any additional generalization improvement. |
Text editing on mobile devices can be a tedious process. To perform various editing operations, a user must repeatedly move his or her fingers between the text input area and the keyboard, making multiple round trips and breaking the flow of typing. In this work, we present Gedit, a system of on-keyboard gestures for c... | In this work, we present Gedit, a system of on-keyboard gestures for convenient mobile text editing. |
Deep learning achieves remarkable generalization capability with overwhelming number of model parameters. Theoretical understanding of deep learning generalization receives recent attention yet remains not fully explored. This paper attempts to provide an alternative understanding from the perspective of maximum entrop... | We prove that DNN is a recursively approximated solution to the maximum entropy principle. |
As people learn to navigate the world, autonomic nervous system (e.g., ``fight or flight) responses provide intrinsic feedback about the potential consequence of action choices (e.g., becoming nervous when close to a cliff edge or driving fast around a bend.) Physiological changes are correlated with these biological ... | We present a novel approach to reinforcement learning that leverages a task-independent intrinsic reward function trained on peripheral pulse measurements that are correlated with human autonomic nervous system responses. |
Deep convolutional neural networks (CNNs) are known to be robust against label noise on extensive datasets. However, at the same time, CNNs are capable of memorizing all labels even if they are random, which means they can memorize corrupted labels. Are CNNs robust or fragile to label noise? Much of researches focusing... | Are CNNs robust or fragile to label noise? Practically, robust. |
Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence. Autoregressive models, such as WaveNet, model local structure at the expense of global latent structure and slow iterative sampling, while Generative A... | High-quality audio synthesis with GANs |
In this work we propose a novel approach for learning graph representation of the data using gradients obtained via backpropagation. Next we build a neural network architecture compatible with our optimization approach and motivated by graph filtering in the vertex domain. We demonstrate that the learned graph has rich... | Graph Optimization with signal filtering in the vertex domain. |
The use of AR in an industrial context could help for the training of new operators. To be able to use an AR guidance system, we need a tool to quickly create a 3D representation of the assembly line and of its AR annotations. This tool should be very easy to use by an operator who is not an AR or VR specialist: typica... | This paper describe a 3D authoring tool for providing AR in assembly lines of industry 4.0 |
Generative adversarial network (GAN) is one of the best known unsupervised learning techniques these days due to its superior ability to learn data distributions. In spite of its great success in applications, GAN is known to be notoriously hard to train. The tremendous amount of time it takes to run the training algor... | We show that, with a proper stepsize choice, the widely used first-order iterative algorithm in training GANs would in fact converge to a stationary solution with a sublinear rate. |
Social dilemmas, where mutual cooperation can lead to high payoffs but participants face incentives to cheat, are ubiquitous in multi-agent interaction. We wish to construct agents that cooperate with pure cooperators, avoid exploitation by pure defectors, and incentivize cooperation from the rest. However, often the a... | We show how to use deep RL to construct agents that can solve social dilemmas beyond matrix games. |
In distributed training, the communication cost due to the transmission of gradients
or the parameters of the deep model is a major bottleneck in scaling up the number
of processing nodes. To address this issue, we propose dithered quantization for
the transmission of the stochastic gradients and show that training ... | The paper proposes and analyzes two quantization schemes for communicating Stochastic Gradients in distributed learning which would reduce communication costs compare to the state of the art while maintaining the same accuracy. |
Deep neural networks have been shown to perform well in many classical machine learning problems, especially in image classification tasks. However, researchers have found that neural networks can be easily fooled, and they are surprisingly sensitive to small perturbations imperceptible to humans. Carefully crafted i... | Jointly train an adversarial noise generating network with a classification network to provide better robustness to adversarial attacks. |
Deep learning has become the state of the art approach in many machine learning problems such as classification. It has recently been shown that deep learning is highly vulnerable to adversarial perturbations. Taking the camera systems of self-driving cars as an example, small adversarial perturbations can cause the sy... | Using ensemble methods as a defense to adversarial perturbations against deep neural networks. |
In this paper, we propose the Associative Conversation Model that generates visual information from textual information and uses it for generating sentences in order to utilize visual information in a dialogue system without image input. In research on Neural Machine Translation, there are studies that generate transla... | Proposal of the sentence generation method based on fusion between textual information and visual information associated with the textual information |
Feedforward convolutional neural network has achieved a great success in many computer vision tasks. While it validly imitates the hierarchical structure of biological visual system, it still lacks one essential architectural feature: contextual recurrent connections with feedback, which widely exists in biological vis... | we proposed a novel contextual recurrent convolutional network with robust property of visual learning |
Deep neural networks have led to a series of breakthroughs, dramatically improving the state-of-the-art in many domains. The techniques driving these advances, however, lack a formal method to account for model uncertainty. While the Bayesian approach to learning provides a solid theoretical framework to handle uncerta... | We show that training a deep network using batch normalization is equivalent to approximate inference in Bayesian models, and we demonstrate how this finding allows us to make useful estimates of the model uncertainty in conventional networks. |
Data-parallel neural network training is network-intensive, so gradient dropping was designed to exchange only large gradients. However, gradient dropping has been shown to slow convergence. We propose to improve convergence by having each node combine its locally computed gradient with the sparse global gradient e... | We improve gradient dropping (a technique of only exchanging large gradients on distributed training) by incorporating local gradients while doing a parameter update to reduce quality loss and further improve the training time. |
We establish the relation between Distributional RL and the Upper Confidence Bound (UCB) approach to exploration.
In this paper we show that the density of the Q function estimated by Distributional RL can be successfully used for the estimation of UCB. This approach does not require counting and, therefore, g... | Exploration using Distributional RL and truncagted variance. |
Good representations facilitate transfer learning and few-shot learning. Motivated by theories of language and communication that explain why communities with large number of speakers have, on average, simpler languages with more regularity, we cast the representation learning problem in terms of learning to communicat... | Motivated by theories of language and communication, we introduce community-based autoencoders, in which multiple encoders and decoders collectively learn structured and reusable representations. |
Humans are experts at high-fidelity imitation -- closely mimicking a demonstration, often in one attempt. Humans use this ability to quickly solve a task instance, and to bootstrap learning of new tasks. Achieving these abilities in autonomous agents is an open problem. In this paper, we introduce an off-policy RL alg... | We present MetaMimic, an algorithm that takes as input a demonstration dataset and outputs (i) a one-shot high-fidelity imitation policy (ii) an unconditional task policy. |
Normalization methods are a central building block in the deep learning toolbox. They accelerate and stabilize training, while decreasing the dependence on manually tuned learning rate schedules. When learning from multi-modal distributions, the effectiveness of batch normalization (BN), arguably the most prominent nor... | We present a novel normalization method for deep neural networks that is robust to multi-modalities in intermediate feature distributions. |
Multilingual machine translation, which translates multiple languages with a single model, has attracted much attention due to its efficiency of offline training and online serving. However, traditional multilingual translation usually yields inferior accuracy compared with the counterpart using individual models for e... | We proposed a knowledge distillation based method to boost the accuracy of multilingual neural machine translation. |
What makes humans so good at solving seemingly complex video games? Unlike computers, humans bring in a great deal of prior knowledge about the world, enabling efficient decision making. This paper investigates the role of human priors for solving video games. Given a sample game, we conduct a series of ablation stud... | We investigate the various kinds of prior knowledge that help human learning and find that general priors about objects play the most critical role in guiding human gameplay. |
Driven by the need for parallelizable hyperparameter optimization methods, this paper studies \emph{open loop} search methods: sequences that are predetermined and can be generated before a single configuration is evaluated. Examples include grid search, uniform random search, low discrepancy sequences, and other sampl... | Driven by the need for parallelizable, open-loop hyperparameter optimization methods, we propose the use of $k$-determinantal point processes in hyperparameter optimization via random search. |
In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch.
When using fine-tuning, the underlying assumption is that the pre-trained model extracts generic features, which are at least partially relevant for solving the target task, but would be diff... | In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch. |
Artificial neural networks have opened up a world of possibilities in data science and artificial intelligence, but neural networks are cumbersome tools that grow with the complexity of the learning problem. We make contributions to this issue by considering a modified version of the fully connected layer we call a blo... | We look at neural networks with block diagonal inner product layers for efficiency. |
One of the challenges in the study of generative adversarial networks is the instability of its training.
In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator.
Our new normalization technique is computationally light and easy to... | We propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator of GANs. |
Humans acquire complex skills by exploiting previously learned skills and making transitions between them. To empower machines with this ability, we propose a method that can learn transition policies which effectively connect primitive skills to perform sequential tasks without handcrafted rewards. To efficiently trai... | Transition policies enable agents to compose complex skills by smoothly connecting previously acquired primitive skills. |
Gated recurrent units (GRUs) were inspired by the common gated recurrent unit, long short-term memory (LSTM), as a means of capturing temporal structure with less complex memory unit architecture. Despite their incredible success in tasks such as natural and artificial language processing, speech, video, and polyphonic... | We classify the the dynamical features one and two GRU cells can and cannot capture in continuous time, and verify our findings experimentally with k-step time series prediction. |
Stacked hourglass network has become an important model for Human pose estimation. The estimation of human body posture depends on the global information of the keypoints type and the local information of the keypoints location. The consistent processing of inputs and constraints makes it difficult to form differentiat... | Differentiated inputs cause functional differentiation of the network, and the interaction of loss functions between networks can affect the optimization process. |
We present a new unsupervised method for learning general-purpose sentence embeddings.
Unlike existing methods which rely on local contexts, such as words
inside the sentence or immediately neighboring sentences, our method selects, for
each target sentence, influential sentences in the entire document based on a do... | To train a sentence embedding using technical documents, our approach considers document structure to find broader context and handle out-of-vocabulary words. |
Neural network training relies on our ability to find ````````"good" minimizers of highly non-convex loss functions. It is well known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and well-chosen training parameters (batch size, learning rate, optimizer) pr... | We explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. |
Deep models are state-of-the-art for many computer vision tasks including image classification and object detection. However, it has been shown that deep models are vulnerable to adversarial examples. We highlight how one-hot encoding directly contributes to this vulnerability and propose breaking away from this widely... | We demonstrate that by leveraging a multi-way output encoding, rather than the widely used one-hot encoding, we can make deep models more robust to adversarial attacks. |
Existing approaches to neural machine translation condition each output word on previously generated outputs. We introduce a model that avoids this autoregressive property and produces its outputs in parallel, allowing an order of magnitude lower latency during inference. Through knowledge distillation, the use of inpu... | We introduce the first NMT model with fully parallel decoding, reducing inference latency by 10x. |
While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs. Defenses based on regularization and adversarial training have been proposed, but often followed by new, stronger attacks ... | We demonstrate a certifiable, trainable, and scalable method for defending against adversarial examples. |
We formulate an information-based optimization problem for supervised classification. For invertible neural networks, the control of these information terms is passed down to the latent features and parameter matrix in the last fully connected layer, given that mutual information is invariant under invertible map. We... | we propose a regularizer that improves the classification performance of neural networks |
Powerful generative models, particularly in Natural Language Modelling, are commonly trained by maximizing a variational lower bound on the data log likelihood. These models often suffer from poor use of their latent variable, with ad-hoc annealing factors used to encourage retention of information in the latent variab... | This paper introduces a novel generative modelling framework that avoids latent-variable collapse and clarifies the use of certain ad-hoc factors in training Variational Autoencoders. |
This paper studies the problem of domain division which aims to segment instances drawn from different probabilistic distributions. This problem exists in many previous recognition tasks, such as Open Set Learning (OSL) and Generalized Zero-Shot Learning (G-ZSL), where the testing instances come from either seen or uns... | This paper studies the problem of domain division by segmenting instances drawn from different probabilistic distributions. |
Stochastic gradient descent (SGD) is widely believed to perform implicit regularization when used to train deep neural networks, but the precise manner in which this occurs has thus far been elusive. We prove that SGD minimizes an average potential over the posterior distribution of weights along with an entropic regul... | SGD implicitly performs variational inference; gradient noise is highly non-isotropic, so SGD does not even converge to critical points of the original loss |
The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both 'what' and 'how' to imitate. We pursue an alternative paradigm wherein an agent first explores the world without any expert supervision and then distills its experience into a goal-conditioned skill policy ... | Agents can learn to imitate solely visual demonstrations (without actions) at test time after learning from their own experience without any form of supervision at training time. |
Distributional Semantics Models(DSM) derive word space from linguistic items
in context. Meaning is obtained by defining a distance measure between vectors
corresponding to lexical entities. Such vectors present several problems. This
work concentrates on quality of word embeddings, improvement of word embedding
ve... | Paper provides a description of a procedure to enhance word vector space model with an evaluation of Paragram and GloVe models for Similarity Benchmarks. |
Recurrent neural networks have achieved excellent performance in many applications. However, on portable devices with limited resources, the models are often too large to deploy. For applications on the server with large scale concurrent requests, the latency during inference can also be very critical for costly comput... | We propose a new quantization method and apply it to quantize RNNs for both compression and acceleration |
The goal of this paper is to demonstrate a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA). We employ MERA as a replacement for linear layers in a neural network and test this implementation on... | We replace the fully connected layers of a neural network with the multi-scale entanglement renormalization ansatz, a type of quantum operation which describes long range correlations. |
Deep learning models have outperformed traditional methods in many fields such
as natural language processing and computer vision. However, despite their
tremendous success, the methods of designing optimal Convolutional Neural Networks
(CNNs) are still based on heuristics or grid search. The resulting networks
obt... | We present a single shot analysis of a trained neural network to remove redundancy and identify optimal network structure |
Recent work has introduced attacks that extract the architecture information of deep neural networks (DNN), as this knowledge enhances an adversary’s capability to conduct attacks on black-box networks. This paper presents the first in-depth security analysis of DNN fingerprinting attacks that exploit cache side-channe... | We conduct the first in-depth security analysis of DNN fingerprinting attacks that exploit cache side-channels, which represents a step toward understanding the DNN’s vulnerability to side-channel attacks. |
Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class f... | We propose Complement Objective Training (COT), a new training paradigm that optimizes both the primary and complement objectives for effectively learning the parameters of neural networks. |
We present a new method for uncertainty estimation and out-of-distribution detection in neural networks with softmax output. We extend softmax layer with an additional constant input. The corresponding additional output is able to represent the uncertainty of the network. The proposed method requires neither additional... | Uncertainty estimation in a single forward pass without additional learnable parameters. |
When deep learning is applied to sensitive data sets, many privacy-related implementation issues arise. These issues are especially evident in the healthcare, finance, law and government industries. Homomorphic encryption could allow a server to make inferences on inputs encrypted by a client, but to our best knowledge... | We made a feature-rich system for deep learning with encrypted inputs, producing encrypted outputs, preserving privacy. |
In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant. Interactive theorem provers such as Coq enable users to construct machine-checkable proofs in a step-by-step manner. Hence, they provide an opportuni... | We introduce a system called GamePad to explore the application of machine learning methods to theorem proving in the Coq proof assistant. |
Deep neural networks are usually huge, which significantly limits the deployment on low-end devices. In recent years, many
weight-quantized models have been proposed. They have small storage and fast inference, but training can still be time-consuming. This can be improved with distributed learning. To reduce the hig... | In this paper, we studied efficient training of loss-aware weight-quantized networks with quantized gradient in a distributed environment, both theoretically and empirically. |
Sequential learning, also called lifelong learning, studies the problem of learning tasks in a sequence with access restricted to only the data of the current task. In this paper we look at a scenario with fixed model capacity, and postulate that the learning process should not be selfish, i.e. it should account for fu... | A regularization strategy for improving the performance of sequential learning |
A Synaptic Neural Network (SynaNN) consists of synapses and neurons. Inspired by the synapse research of neuroscience, we built a synapse model with a nonlinear synapse function of excitatory and inhibitory channel probabilities. Introduced the concept of surprisal space and constructed a commutative diagram, we proved... | A synaptic neural network with synapse graph and learning that has the feature of topological conjugation and Bose-Einstein distribution in surprisal space. |
Many types of relations in physical, biological, social and information systems can be modeled as homogeneous or heterogeneous concept graphs. Hence, learning from and with graph embeddings has drawn a great deal of research interest recently, but only ad hoc solutions have been obtained this far. In this paper, we con... | Generalized Graph Embedding Models |
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