Dmitry Molchanov | DeepAI

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Featured Co-authors

Dmitry Vetrov
62 publications
Alexander Shekhovtsov
22 publications
Kirill Neklyudov
11 publications
Arsenii Ashukha
11 publications
Aibek Alanov
7 publications
Andrei Atanov
7 publications
Timur Garipov
7 publications
Max Kochurov
5 publications
Alexander Lyzhov
4 publications
Viktor Yanush
3 publications
Vladimir Arkhipkin
3 publications

∙ 02/10/2023

Star-Shaped Denoising Diffusion Probabilistic Models

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

0 Andrey Okhotin, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 11/01/2018

Variational Dropout via Empirical Bayes

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

0 Valery Kharitonov, et al. ∙

∙ 10/05/2018

Doubly Semi-Implicit Variational Inference

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

32 Dmitry Molchanov, et al. ∙

∙ 03/10/2018

Variance Networks: When Expectation Does Not Meet Your Expectations

In this paper, we propose variance networks, a new model that stores the...

0 Kirill Neklyudov, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 05/20/2017

Structured Bayesian Pruning via Log-Normal Multiplicative Noise

Dropout-based regularization methods can be regarded as injecting random...

0 Kirill Neklyudov, et al. ∙

∙ 01/19/2017

Variational Dropout Sparsifies Deep Neural Networks

We explore a recently proposed Variational Dropout technique that provid...

0 Dmitry Molchanov, et al. ∙

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