b'Dmitry Vetrov'

research

∙ 06/01/2023

UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model

This paper introduces UnDiff, a diffusion probabilistic model capable of...

0 Anastasiia Iashchenko, et al. ∙

research

∙ 03/06/2023

To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning

Transfer learning and ensembling are two popular techniques for improvin...

0 Ildus Sadrtdinov, et al. ∙

research

∙ 02/21/2023

Differentiable Rendering with Reparameterized Volume Sampling

In view synthesis, a neural radiance field approximates underlying densi...

0 Nikita Morozov, et al. ∙

research

∙ 02/10/2023

Star-Shaped Denoising Diffusion Probabilistic Models

Methods based on Denoising Diffusion Probabilistic Models (DDPM) became ...

0 Andrey Okhotin, et al. ∙

research

∙ 12/20/2022

StyleDomain: Analysis of StyleSpace for Domain Adaptation of StyleGAN

Domain adaptation of GANs is a problem of fine-tuning the state-of-the-a...

0 Aibek Alanov, et al. ∙

research

∙ 11/02/2022

Entropic Neural Optimal Transport via Diffusion Processes

We propose a novel neural algorithm for the fundamental problem of compu...

6 Nikita Gushchin, et al. ∙

research

∙ 10/17/2022

HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks

Domain adaptation framework of GANs has achieved great progress in recen...

0 Aibek Alanov, et al. ∙

research

∙ 09/08/2022

Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes

A fundamental property of deep learning normalization techniques, such a...

0 Maxim Kodryan, et al. ∙

research

∙ 04/06/2022

FFC-SE: Fast Fourier Convolution for Speech Enhancement

Fast Fourier convolution (FFC) is the recently proposed neural operator ...

0 Ivan Shchekotov, et al. ∙

research

∙ 03/24/2022

HiFi++: a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement

Generative adversarial networks have recently demonstrated outstanding p...

0 Pavel Andreev, et al. ∙

research

∙ 12/29/2021

Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems

Channel decoding, channel detection, channel assessment, and resource ma...

3 Evgeny Bobrov, et al. ∙

research

∙ 11/23/2021

Variational Autoencoders for Studying the Manifold of Precoding Matrices with High Spectral Efficiency

In multiple-input multiple-output (MIMO) wireless communications systems...

5 Evgeny Bobrov, et al. ∙

research

∙ 10/28/2021

Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces

Structured latent variables allow incorporating meaningful prior knowled...

0 Kirill Struminsky, et al. ∙

research

∙ 08/31/2021

Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study

Generative adversarial networks (GANs) have an enormous potential impact...

0 Pavel Andreev, et al. ∙

research

∙ 06/29/2021

On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay

Despite the conventional wisdom that using batch normalization with weig...

0 Ekaterina Lobacheva, et al. ∙

research

∙ 06/15/2021

Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations

Averaging predictions over a set of models – an ensemble – is widely use...

0 Arsenii Ashukha, et al. ∙

research

∙ 06/08/2021

Towards Practical Credit Assignment for Deep Reinforcement Learning

Credit assignment is a fundamental problem in reinforcement learning, th...

0 Vyacheslav Alipov, et al. ∙

research

∙ 07/16/2020

On Power Laws in Deep Ensembles

Ensembles of deep neural networks are known to achieve state-of-the-art ...

0 Ekaterina Lobacheva, et al. ∙

research

∙ 06/30/2020

Involutive MCMC: a Unifying Framework

Markov Chain Monte Carlo (MCMC) is a computational approach to fundament...

8 Kirill Neklyudov, et al. ∙

research

∙ 06/18/2020

MARS: Masked Automatic Ranks Selection in Tensor Decompositions

Tensor decomposition methods have recently proven to be efficient for co...

0 Maxim Kodryan, et al. ∙

research

∙ 06/11/2020

Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks

Training neural networks with binary weights and activations is a challe...

0 Viktor Yanush, et al. ∙

research

∙ 05/14/2020

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...

0 Nadezhda Chirkova, et al. ∙

research

∙ 05/08/2020

Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics

The overestimation bias is one of the major impediments to accurate off-...

0 Arsenii Kuznetsov, et al. ∙

research

∙ 03/04/2020

Deterministic Decoding for Discrete Data in Variational Autoencoders

Variational autoencoders are prominent generative models for modeling di...

0 Daniil Polykovskiy, et al. ∙

research

∙ 02/22/2020

Stochasticity in Neural ODEs: An Empirical Study

Stochastic regularization of neural networks (e.g. dropout) is a wide-sp...

0 Viktor Oganesyan, et al. ∙

research

∙ 02/21/2020

Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation

Test-time data augmentation—averaging the predictions of a machine learn...

13 Dmitry Molchanov, et al. ∙

research

∙ 02/15/2020

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

Uncertainty estimation and ensembling methods go hand-in-hand. Uncertain...

0 Arsenii Ashukha, et al. ∙

research

∙ 12/20/2019

MLRG Deep Curvature

We present MLRG Deep Curvature suite, a PyTorch-based, open-source packa...

0 Diego Granziol, et al. ∙

research

∙ 11/22/2019

Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution

Learning models with discrete latent variables using stochastic gradient...

0 Artyom Gadetsky, et al. ∙

research

∙ 11/13/2019

Structured Sparsification of Gated Recurrent Neural Networks

Recently, a lot of techniques were developed to sparsify the weights of ...

0 Ekaterina Lobacheva, et al. ∙

research

∙ 10/29/2019

A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models

Generative models produce realistic objects in many domains, including t...

0 Maksim Kuznetsov, et al. ∙

research

∙ 07/17/2019

Subspace Inference for Bayesian Deep Learning

Bayesian inference was once a gold standard for learning with neural net...

3 Pavel Izmailov, et al. ∙

research

∙ 06/09/2019

The Implicit Metropolis-Hastings Algorithm

Recent works propose using the discriminator of a GAN to filter out unre...

0 Kirill Neklyudov, et al. ∙

research

∙ 05/08/2019

Importance Weighted Hierarchical Variational Inference

Variational Inference is a powerful tool in the Bayesian modeling toolki...

32 Artem Sobolev, et al. ∙

research

∙ 05/01/2019

Semi-Conditional Normalizing Flows for Semi-Supervised Learning

This paper proposes a semi-conditional normalizing flow model for semi-s...

0 Andrei Atanov, et al. ∙

research

∙ 04/09/2019

User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks

We propose a novel multi-texture synthesis model based on generative adv...

0 Aibek Alanov, et al. ∙

research

∙ 02/07/2019

A Simple Baseline for Bayesian Uncertainty in Deep Learning

We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose ...

20 Wesley Maddox, et al. ∙

research

∙ 12/12/2018

Bayesian Sparsification of Gated Recurrent Neural Networks

Bayesian methods have been successfully applied to sparsify weights of n...

0 Ekaterina Lobacheva, et al. ∙

research

∙ 11/11/2018

ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks

Neural Network is a powerful Machine Learning tool that shows outstandin...

0 Iurii Kemaev, et al. ∙

research

∙ 11/01/2018

Variational Dropout via Empirical Bayes

We study the Automatic Relevance Determination procedure applied to deep...

0 Valery Kharitonov, et al. ∙

research

∙ 10/25/2018

Bayesian Compression for Natural Language Processing

In natural language processing, a lot of the tasks are successfully solv...

0 Nadezhda Chirkova, et al. ∙

research

∙ 10/16/2018

Metropolis-Hastings view on variational inference and adversarial training

In this paper we propose to view the acceptance rate of the Metropolis-H...

0 Kirill Neklyudov, et al. ∙

research

∙ 10/16/2018

The Deep Weight Prior. Modeling a prior distribution for CNNs using generative models

Bayesian inference is known to provide a general framework for incorpora...

12 Andrei Atanov, et al. ∙

research

∙ 10/11/2018

Pairwise Augmented GANs with Adversarial Reconstruction Loss

We propose a novel autoencoding model called Pairwise Augmented GANs. We...

8 Aibek Alanov, et al. ∙

research

∙ 10/05/2018

Doubly Semi-Implicit Variational Inference

We extend the existing framework of semi-implicit variational inference ...

32 Dmitry Molchanov, et al. ∙

research

∙ 06/26/2018

Conditional Generators of Words Definitions

We explore recently introduced definition modeling technique that provid...

0 Artyom Gadetsky, et al. ∙

research

∙ 06/06/2018

Universal Conditional Machine

We propose a single neural probabilistic model based on variational auto...

4 Oleg Ivanov, et al. ∙

research

∙ 03/14/2018

Averaging Weights Leads to Wider Optima and Better Generalization

Deep neural networks are typically trained by optimizing a loss function...

0 Pavel Izmailov, et al. ∙

research

∙ 02/20/2018

Bayesian Incremental Learning for Deep Neural Networks

In industrial machine learning pipelines, data often arrive in parts. Pa...

0 Max Kochurov, et al. ∙

research

∙ 02/13/2018

Uncertainty Estimation via Stochastic Batch Normalization

In this work, we investigate Batch Normalization technique and propose i...

0 Andrei Atanov, et al. ∙

Dmitry Vetrov

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