Dmitry P. Vetrov

Moscow State University, Moscow, Russia 119992

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Papers65 papers

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Structured Sparsification of Gated Recurrent Neural Networks

Ekaterina Lobacheva,Nadezhda Chirkova, Aleksandr Markovich,Dmitry Vetrov

national conference on artificial intelligence, (2020)

Cited by2Bibtex

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

Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Molchanov,Dmitry P. Vetrov

UAI, pp.1308-1317, (2020)

EIBibtex

Involutive MCMC: One Way to Derive Them All

Kirill Neklyudov,Max Welling, Evgenii Egorov,Dmitry Vetrov

international conference on machine learning, (2020)

Bibtex

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

Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov,Dmitry Vetrov

ICLR, (2020)

Cited by1EIBibtex

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

Molchanov Dmitry, Lyzhov Alexander, Molchanova Yuliya, Ashukha Arsenii,Vetrov Dmitry

uncertainty in artificial intelligence, pp.1308-1317, (2020)

Bibtex

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

Artyom Gadetsky, Kirill Struminsky, Chris Robinson, Novi Quadrianto,Dmitry Vetrov

national conference on artificial intelligence, (2020)

Bibtex

Variance Networks: When Expectation Does Not Meet Your Expectations

Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha,Dmitry P. Vetrov

international conference on learning representations, (2019)

Cited by3EIBibtex

Metropolis-Hastings view on variational inference and adversarial training

Neklyudov Kirill, Egorov Evgenii, Shvechikov Pavel,Vetrov Dmitry

Cited by5Bibtex

Importance Weighted Hierarchical Variational Inference

Artem Sobolev,Dmitry P. Vetrov

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019): 601-613

Cited by5EIBibtex

A Simple Baseline for Bayesian Uncertainty in Deep Learning

Wesley J Maddox, Pavel Izmailov, Timur Garipov,Dmitry Vetrov,Andrew Wilson

NeurIPS, pp.13132-13143, (2019)

Cited by57EIBibtex

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

Maxim Kuznetsov, Daniil Polykovskiy,Dmitry Vetrov, Alexander Zhebrak

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019): 4104-4114

Cited by2EIBibtex

Subspace Inference for Bayesian Deep Learning

Pavel Izmailov, Wesley Maddox,Polina Kirichenko, Timur Garipov,Dmitry P. Vetrov,Andrew Gordon Wilson

UAI, pp.435, (2019)

Cited by9EIBibtex

The Implicit Metropolis-Hastings Algorithm

Kirill Neklyudov, Evgenii Egorov,Dmitry P. Vetrov

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019): 13932-13942

EIBibtex

Semi-Conditional Normalizing Flows for Semi-Supervised Learning

Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha, Ivan Sosnovik,Dmitry Vetrov

arXiv: Machine Learning, (2019)

EIBibtex

Variational Autoencoder with Arbitrary Conditioning

Oleg Ivanov, Michael Figurnov,Dmitry P. Vetrov

international conference on learning representations, (2019)

Cited by24EIBibtex

The Deep Weight Prior

Andrei Atanov, Arsenii Ashukha, Kirill Struminsky,Dmitriy Vetrov,Max Welling

ICLR, (2019)

Cited by12EIBibtex

A Simple Baseline for Bayesian Uncertainty in Deep Learning

Wesley Maddox, Timur Garipov, Pavel Izmailov,Dmitry P. Vetrov,Andrew Gordon Wilson

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019)

EIBibtex

User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks

Aibek Alanov, Max Kochurov,Denis Volkhonskiy, Daniil Yashkov,Evgeny Burnaev,Dmitry P. Vetrov

arXiv: Computer Vision and Pattern Recognition, (2019)

EIBibtex

Bayesian Sparsification of Gated Recurrent Neural Networks

Ekaterina Lobacheva,Nadezhda Chirkova,Dmitry P. Vetrov

arXiv: Learning, (2018)

Cited by1EIBibtex

Predictive model for bottomhole pressure based on machine learning

Pavel Spesivtsev, Konstantin Sinkov, Ivan Sofronov, Anna Zimina,Alexey V. Umnov, Ramil Yarullin,Dmitry P. Vetrov

Journal of Petroleum Science and Engineering, (2018): 825-841

Bibtex

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