Dmitry P. Vetrov

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 112

Top Venues

CoRR

NeurIPS

AISTATS

ICML

Publications

Artem Tsypin, Leonid Ugadiarov, Kuzma Khrabrov, Alexander Telepov, Egor Rumiantsev, Alexey Skrynnik, Aleksandr Panov, Dmitry P. Vetrov, Elena Tutubalina, Artur Kadurin
Gradual Optimization Learning for Conformational Energy Minimization. ICLR (2024)
Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov
Generative Flow Networks as Entropy-Regularized RL. AISTATS (2024)
Maxim Nikolaev, Mikhail Kuznetsov, Dmitry P. Vetrov, Aibek Alanov
HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach. CoRR (2024)
Grigory Bartosh, Dmitry P. Vetrov, Christian A. Naesseth
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling. CoRR (2024)
Nikita Morozov, Denis Rakitin, Oleg Desheulin, Dmitry P. Vetrov, Kirill Struminsky
Differentiable Rendering with Reparameterized Volume Sampling. AISTATS (2024)
Viacheslav Meshchaninov, Pavel V. Strashnov, Andrey Shevtsov, Fedor Nikolaev, Nikita Ivanisenko, Olga L. Kardymon, Dmitry P. Vetrov
Diffusion on language model embeddings for protein sequence generation. CoRR (2024)
Alexander Shabalin, Viacheslav Meshchaninov, Tingir Badmaev, Dmitry Molchanov, Grigory Bartosh, Sergey Markov, Dmitry P. Vetrov
TEncDM: Understanding the Properties of Diffusion Model in the Space of Language Model Encodings. CoRR (2024)
Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov
Generative Flow Networks as Entropy-Regularized RL. CoRR (2023)
Ildus Sadrtdinov, Dmitrii Pozdeev, Dmitry P. Vetrov, Ekaterina Lobacheva
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning. NeurIPS (2023)
Maxim Kodryan, Dmitry Kropotov, Dmitry P. Vetrov
MARS: Masked Automatic Ranks Selection in Tensor Decompositions. AISTATS (2023)
Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Viktor Ohanesian, Aibek Alanov, Dmitry P. Vetrov
Star-Shaped Denoising Diffusion Probabilistic Models. NeurIPS (2023)
Aibek Alanov, Vadim Titov, Maksim Nakhodnov, Dmitry P. Vetrov
StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation. ICCV (2023)
Grigory Bartosh, Dmitry P. Vetrov, Christian A. Naesseth
Neural Diffusion Models. CoRR (2023)
Anastasiia Iashchenko, Pavel Andreev, Ivan Shchekotov, Nicholas Babaev, Dmitry P. Vetrov
UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model. CoRR (2023)
Anastasiia Iashchenko, Pavel Andreev, Ivan Shchekotov, Nicholas Babaev, Dmitry P. Vetrov
UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model. INTERSPEECH (2023)
Nikita Morozov, Denis Rakitin, Oleg Desheulin, Dmitry P. Vetrov, Kirill Struminsky
Differentiable Rendering with Reparameterized Volume Sampling. CoRR (2023)
Pavel Andreev, Aibek Alanov, Oleg Ivanov, Dmitry P. Vetrov
HIFI++: A Unified Framework for Bandwidth Extension and Speech Enhancement. ICASSP (2023)
Nikita Gushchin, Alexander Kolesov, Alexander Korotin, Dmitry P. Vetrov, Evgeny Burnaev
Entropic Neural Optimal Transport via Diffusion Processes. NeurIPS (2023)
Artem Tsypin, Leonid Ugadiarov, Kuzma Khrabrov, Manvel Avetisian, Alexander Telepov, Egor Rumiantsev, Alexey Skrynnik, Aleksandr I. Panov, Dmitry P. Vetrov, Elena Tutubalina, Artur Kadurin
Gradual Optimization Learning for Conformational Energy Minimization. CoRR (2023)
Ekaterina Lobacheva, Eduard Pockonechnyy, Maxim Kodryan, Dmitry P. Vetrov
Large Learning Rates Improve Generalization: But How Large Are We Talking About? CoRR (2023)
Ildus Sadrtdinov, Dmitrii Pozdeev, Dmitry P. Vetrov, Ekaterina Lobacheva
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning. CoRR (2023)
Maxim Kodryan, Ekaterina Lobacheva, Maksim Nakhodnov, Dmitry P. Vetrov
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes. CoRR (2022)
Nikita Gushchin, Alexander Kolesov, Alexander Korotin, Dmitry P. Vetrov, Evgeny Burnaev
Entropic Neural Optimal Transport via Diffusion Processes. CoRR (2022)
Aibek Alanov, Vadim Titov, Dmitry P. Vetrov
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks. NeurIPS (2022)
Maxim Kodryan, Ekaterina Lobacheva, Maksim Nakhodnov, Dmitry P. Vetrov
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes. NeurIPS (2022)
Arsenii Ashukha, Andrei Atanov, Dmitry P. Vetrov
Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations. CoRR (2021)
Kirill Struminsky, Artyom Gadetsky, Denis Rakitin, Danil Karpushkin, Dmitry P. Vetrov
Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces. NeurIPS (2021)
Evgeny Bobrov, Sergey Troshin, Nadezhda Chirkova, Ekaterina Lobacheva, Sviatoslav Panchenko, Dmitry P. Vetrov, Dmitry Kropotov
Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems. CoRR (2021)
Ekaterina Lobacheva, Maxim Kodryan, Nadezhda Chirkova, Andrey Malinin, Dmitry P. Vetrov
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay. NeurIPS (2021)
Arsenii Kuznetsov, Alexander Grishin, Artem Tsypin, Arsenii Ashukha, Dmitry P. Vetrov
Automating Control of Overestimation Bias for Continuous Reinforcement Learning. CoRR (2021)
Kirill Struminsky, Artyom Gadetsky, Denis Rakitin, Danil Karpushkin, Dmitry P. Vetrov
Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces. CoRR (2021)
Ekaterina Lobacheva, Maxim Kodryan, Nadezhda Chirkova, Andrey Malinin, Dmitry P. Vetrov
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay. CoRR (2021)
Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin, Dmitry P. Vetrov
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics. CoRR (2020)
Daniil Polykovskiy, Dmitry P. Vetrov
Deterministic Decoding for Discrete Data in Variational Autoencoders. CoRR (2020)
Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich, Dmitry P. Vetrov
Structured Sparsification of Gated Recurrent Neural Networks. AAAI (2020)
Dmitry Molchanov, Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry P. Vetrov
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation. CoRR (2020)
Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov
Involutive MCMC: a Unifying Framework. CoRR (2020)
Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry P. Vetrov
On Power Laws in Deep Ensembles. NeurIPS (2020)
Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry P. Vetrov
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning. CoRR (2020)
Viktor Oganesyan, Alexandra Volokhova, Dmitry P. Vetrov
Stochasticity in Neural ODEs: An Empirical Study. CoRR (2020)
Viktor Yanush, Alexander Shekhovtsov, Dmitry Molchanov, Dmitry P. Vetrov
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks. CoRR (2020)
Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov
Involutive MCMC: a Unifying Framework. ICML (2020)
Maxim Kodryan, Dmitry Kropotov, Dmitry P. Vetrov
MARS: Masked Automatic Ranks Selection in Tensor Decompositions. CoRR (2020)
Artyom Gadetsky, Kirill Struminsky, Christopher Robinson, Novi Quadrianto, Dmitry P. Vetrov
Low-Variance Black-Box Gradient Estimates for the Plackett-Luce Distribution. AAAI (2020)
Aibek Alanov, Max Kochurov, Denis Volkhonskiy, Daniil Yashkov, Evgeny Burnaev, Dmitry P. Vetrov
User-controllable Multi-texture Synthesis with Generative Adversarial Networks. VISIGRAPP (4: VISAPP) (2020)
Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry P. Vetrov
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning. ICLR (2020)
Daniil Polykovskiy, Dmitry P. Vetrov
Deterministic Decoding for Discrete Data in Variational Autoencoders. AISTATS (2020)
Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry P. Vetrov
On Power Laws in Deep Ensembles. CoRR (2020)
Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Molchanov, Dmitry P. Vetrov
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation. UAI (2020)
Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry P. Vetrov
Deep Ensembles on a Fixed Memory Budget: One Wide Network or Several Thinner Ones? CoRR (2020)
Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin, Dmitry P. Vetrov
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics. ICML (2020)
Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov
The Implicit Metropolis-Hastings Algorithm. NeurIPS (2019)
Diego Granziol, Xingchen Wan, Timur Garipov, Dmitry P. Vetrov, Stephen Roberts
MLRG Deep Curvature. CoRR (2019)
Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitry P. Vetrov, Max Welling
The Deep Weight Prior. ICLR (Poster) (2019)
Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov
Uncertainty Estimation via Stochastic Batch Normalization. ISNN (1) (2019)
Artem Sobolev, Dmitry P. Vetrov
Importance Weighted Hierarchical Variational Inference. NeurIPS (2019)
Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich, Dmitry P. Vetrov
Structured Sparsification of Gated Recurrent Neural Networks. CoRR (2019)
Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov
The Implicit Metropolis-Hastings Algorithm. CoRR (2019)
Pavel Izmailov, Wesley Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
Subspace Inference for Bayesian Deep Learning. UAI (2019)
Maxim Kodryan, Artem M. Grachev, Dmitry I. Ignatov, Dmitry P. Vetrov
Efficient Language Modeling with Automatic Relevance Determination in Recurrent Neural Networks. RepL4NLP@ACL (2019)
Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry P. Vetrov
Doubly Semi-Implicit Variational Inference. AISTATS (2019)
Maksim Kuznetsov, Daniil Polykovskiy, Dmitry P. Vetrov, Alexander Zhebrak
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models. NeurIPS (2019)
Aibek Alanov, Max Kochurov, Denis Volkhonskiy, Daniil Yashkov, Evgeny Burnaev, Dmitry P. Vetrov
User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks. CoRR (2019)
Oleg Ivanov, Michael Figurnov, Dmitry P. Vetrov
Variational Autoencoder with Arbitrary Conditioning. ICLR (Poster) (2019)
Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
A Simple Baseline for Bayesian Uncertainty in Deep Learning. NeurIPS (2019)
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
Variance Networks: When Expectation Does Not Meet Your Expectations. ICLR (Poster) (2019)
Artyom Gadetsky, Kirill Struminsky, Christopher Robinson, Novi Quadrianto, Dmitry P. Vetrov
Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution. CoRR (2019)
Kirill Struminsky, Dmitry P. Vetrov
A Simple Method to Evaluate Support Size and Non-uniformity of a Decoder-Based Generative Model. AIST (2019)
Maksim Kuznetsov, Daniil Polykovskiy, Dmitry P. Vetrov, Alexander Zhebrak
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models. CoRR (2019)
Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha, Ivan Sosnovik, Dmitry P. Vetrov
Semi-Conditional Normalizing Flows for Semi-Supervised Learning. CoRR (2019)
Artem Sobolev, Dmitry P. Vetrov
Importance Weighted Hierarchical Variational Inference. CoRR (2019)
Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry P. Vetrov, Andrew Gordon Wilson
A Simple Baseline for Bayesian Uncertainty in Deep Learning. CoRR (2019)
Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
Subspace Inference for Bayesian Deep Learning. CoRR (2019)
Iurii Kemaev, Daniil Polykovskiy, Dmitry P. Vetrov
ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks. ACML (2018)
Valery Kharitonov, Dmitry Molchanov, Dmitry P. Vetrov
Variational Dropout via Empirical Bayes. CoRR (2018)
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew Gordon Wilson
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. CoRR (2018)
Max Kochurov, Timur Garipov, Dmitry Podoprikhin, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
Bayesian Incremental Learning for Deep Neural Networks. CoRR (2018)
Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov
Uncertainty Estimation via Stochastic Batch Normalization. CoRR (2018)
Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry P. Vetrov
Bayesian Compression for Natural Language Processing. CoRR (2018)
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew Gordon Wilson
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. NeurIPS (2018)
Kirill Neklyudov, Pavel Shvechikov, Dmitry P. Vetrov
Metropolis-Hastings view on variational inference and adversarial training. CoRR (2018)
Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitry P. Vetrov, Max Welling
The Deep Weight Prior. Modeling a prior distribution for CNNs using generative models. CoRR (2018)
Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry P. Vetrov
Bayesian Compression for Natural Language Processing. EMNLP (2018)
Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry P. Vetrov
Doubly Semi-Implicit Variational Inference. CoRR (2018)
Oleg Ivanov, Michael Figurnov, Dmitry P. Vetrov
Universal Conditional Machine. CoRR (2018)
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
Averaging Weights Leads to Wider Optima and Better Generalization. CoRR (2018)
Artyom Gadetsky, Ilya Yakubovskiy, Dmitry P. Vetrov
Conditional Generators of Words Definitions. CoRR (2018)
Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov
Uncertainty Estimation via Stochastic Batch Normalization. ICLR (Workshop) (2018)
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
Averaging Weights Leads to Wider Optima and Better Generalization. UAI (2018)
Aibek Alanov, Max Kochurov, Daniil Yashkov, Dmitry P. Vetrov
Pairwise Augmented GANs with Adversarial Reconstruction Loss. CoRR (2018)
Iurii Kemaev, Daniil Polykovskiy, Dmitry P. Vetrov
ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks. CoRR (2018)
Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry P. Vetrov
Bayesian Sparsification of Gated Recurrent Neural Networks. CoRR (2018)
Sergey Bartunov, Dmitry P. Vetrov
Few-shot Generative Modelling with Generative Matching Networks. AISTATS (2018)
Max Kochurov, Timur Garipov, Dmitry Podoprikhin, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
Bayesian Incremental Learning for Deep Neural Networks. ICLR (Workshop) (2018)
Artyom Gadetsky, Ilya Yakubovskiy, Dmitry P. Vetrov
Conditional Generators of Words Definitions. ACL (2) (2018)
Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
Variational Dropout Sparsifies Deep Neural Networks. CoRR (2017)
Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry P. Vetrov
Bayesian Sparsification of Recurrent Neural Networks. CoRR (2017)
Sergey Bartunov, Dmitry P. Vetrov
Fast Adaptation in Generative Models with Generative Matching Networks. ICLR (Workshop) (2017)
Michael Figurnov, Artem Sobolev, Dmitry P. Vetrov
Probabilistic Adaptive Computation Time. CoRR (2017)
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
Structured Bayesian Pruning via Log-Normal Multiplicative Noise. NIPS (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Dmitry P. Vetrov