
On Power Laws in Deep Ensembles
Ensembles of deep neural networks are known to achieve stateoftheart ...
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Involutive MCMC: a Unifying Framework
Markov Chain Monte Carlo (MCMC) is a computational approach to fundament...
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MARS: Masked Automatic Ranks Selection in Tensor Decompositions
Tensor decomposition methods have recently proven to be efficient for co...
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Reintroducing StraightThrough Estimators as Principled Methods for Stochastic Binary Networks
Training neural networks with binary weights and activations is a challe...
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Deep Ensembles on a Fixed Memory Budget: One Wide Network or Several Thinner Ones?
One of the generally accepted views of modern deep learning is that incr...
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Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
The overestimation bias is one of the major impediments to accurate off...
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Deterministic Decoding for Discrete Data in Variational Autoencoders
Variational autoencoders are prominent generative models for modeling di...
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Stochasticity in Neural ODEs: An Empirical Study
Stochastic regularization of neural networks (e.g. dropout) is a widesp...
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Greedy Policy Search: A Simple Baseline for Learnable TestTime Augmentation
Testtime data augmentation—averaging the predictions of a machine learn...
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Pitfalls of InDomain Uncertainty Estimation and Ensembling in Deep Learning
Uncertainty estimation and ensembling methods go handinhand. Uncertain...
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MLRG Deep Curvature
We present MLRG Deep Curvature suite, a PyTorchbased, opensource packa...
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Lowvariance Blackbox Gradient Estimates for the PlackettLuce Distribution
Learning models with discrete latent variables using stochastic gradient...
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Structured Sparsification of Gated Recurrent Neural Networks
Recently, a lot of techniques were developed to sparsify the weights of ...
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A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models
Generative models produce realistic objects in many domains, including t...
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Subspace Inference for Bayesian Deep Learning
Bayesian inference was once a gold standard for learning with neural net...
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The Implicit MetropolisHastings Algorithm
Recent works propose using the discriminator of a GAN to filter out unre...
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Importance Weighted Hierarchical Variational Inference
Variational Inference is a powerful tool in the Bayesian modeling toolki...
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SemiConditional Normalizing Flows for SemiSupervised Learning
This paper proposes a semiconditional normalizing flow model for semis...
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UserControllable MultiTexture Synthesis with Generative Adversarial Networks
We propose a novel multitexture synthesis model based on generative adv...
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A Simple Baseline for Bayesian Uncertainty in Deep Learning
We propose SWAGaussian (SWAG), a simple, scalable, and general purpose ...
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Bayesian Sparsification of Gated Recurrent Neural Networks
Bayesian methods have been successfully applied to sparsify weights of n...
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ReSet: Learning Recurrent Dynamic Routing in ResNetlike Neural Networks
Neural Network is a powerful Machine Learning tool that shows outstandin...
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Variational Dropout via Empirical Bayes
We study the Automatic Relevance Determination procedure applied to deep...
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Bayesian Compression for Natural Language Processing
In natural language processing, a lot of the tasks are successfully solv...
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MetropolisHastings view on variational inference and adversarial training
In this paper we propose to view the acceptance rate of the MetropolisH...
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The Deep Weight Prior. Modeling a prior distribution for CNNs using generative models
Bayesian inference is known to provide a general framework for incorpora...
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Pairwise Augmented GANs with Adversarial Reconstruction Loss
We propose a novel autoencoding model called Pairwise Augmented GANs. We...
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Doubly SemiImplicit Variational Inference
We extend the existing framework of semiimplicit variational inference ...
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Conditional Generators of Words Definitions
We explore recently introduced definition modeling technique that provid...
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Universal Conditional Machine
We propose a single neural probabilistic model based on variational auto...
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Averaging Weights Leads to Wider Optima and Better Generalization
Deep neural networks are typically trained by optimizing a loss function...
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Bayesian Incremental Learning for Deep Neural Networks
In industrial machine learning pipelines, data often arrive in parts. Pa...
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Uncertainty Estimation via Stochastic Batch Normalization
In this work, we investigate Batch Normalization technique and propose i...
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Probabilistic Adaptive Computation Time
We present a probabilistic model with discrete latent variables that con...
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Bayesian Sparsification of Recurrent Neural Networks
Recurrent neural networks show stateoftheart results in many text ana...
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Structured Bayesian Pruning via LogNormal Multiplicative Noise
Dropoutbased regularization methods can be regarded as injecting random...
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Variational Dropout Sparsifies Deep Neural Networks
We explore a recently proposed Variational Dropout technique that provid...
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Spatially Adaptive Computation Time for Residual Networks
This paper proposes a deep learning architecture based on Residual Netwo...
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GTApprox: surrogate modeling for industrial design
We describe GTApprox  a new tool for mediumscale surrogate modeling in...
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Tensorizing Neural Networks
Deep neural networks currently demonstrate stateoftheart performance ...
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PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
We propose a novel approach to reduce the computational cost of evaluati...
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Breaking Sticks and Ambiguities with Adaptive Skipgram
Recently proposed Skipgram model is a powerful method for learning high...
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Submodular relaxation for inference in Markov random fields
In this paper we address the problem of finding the most probable state ...
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Multiutility Learning: Structuredoutput Learning with Multiple Annotationspecific Loss Functions
Structuredoutput learning is a challenging problem; particularly so bec...
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Submodular Decomposition Framework for Inference in Associative Markov Networks with Global Constraints
In the paper we address the problem of finding the most probable state o...
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Dmitry Vetrov
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Research Professor, Head of the Centre:Faculty of Computer Science, Laboratory Head:Faculty of Computer Science at Higher School of Economics , Leading researcher at Yandex