Vetrov, Dmitry

36 publications

NeurIPS 2025 Cosmos: Compressed and Smooth Latent Space for Text Diffusion Modeling Viacheslav Meshchaninov, Egor Chimbulatov, Alexander Shabalin, Aleksandr Abramov, Dmitry Vetrov

ICML 2025 Diffusion on Language Model Encodings for Protein Sequence Generation Viacheslav Meshchaninov, Pavel Strashnov, Andrey Shevtsov, Fedor Nikolaev, Nikita Ivanisenko, Olga Kardymon, Dmitry Vetrov

ICML 2025 SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations Grigory Bartosh, Dmitry Vetrov, Christian A. Naesseth

ICLRW 2025 SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations Grigory Bartosh, Dmitry Vetrov, Christian A. Naesseth

NeurIPS 2024 HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach Maxim Nikolaev, Mikhail Kuznetsov, Dmitry Vetrov, Aibek Alanov

ICMLW 2024 Improving GFlowNets with Monte Carlo Tree Search Nikita Morozov, Daniil Tiapkin, Sergey Samsonov, Alexey Naumov, Dmitry Vetrov

NeurIPSW 2024 Lion's Sign Noise Can Make Training More Stable Simon Elistratov, Andrey Podivilov, Timofei Iuzhakov, Dmitry Vetrov

ICML 2024 Neural Diffusion Models Grigory Bartosh, Dmitry Vetrov, Christian A. Naesseth

NeurIPS 2024 Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling Grigory Bartosh, Dmitry Vetrov, Christian A. Naesseth

ICMLW 2024 Regularized Distribution Matching Distillation for One-Step Unpaired Image-to-Image Translation Denis Rakitin, Ivan Shchekotov, Dmitry Vetrov

CVPR 2024 The Devil Is in the Details: StyleFeatureEditor for Detail-Rich StyleGAN Inversion and High Quality Image Editing Denis Bobkov, Vadim Titov, Aibek Alanov, Dmitry Vetrov

NeurIPS 2024 Where Do Large Learning Rates Lead Us? Ildus Sadrtdinov, Maxim Kodryan, Eduard Pokonechny, Ekaterina Lobacheva, Dmitry Vetrov

ICMLW 2024 Where Do Large Learning Rates Lead Us? a Feature Learning Perspective Ildus Sadrtdinov, Maxim Kodryan, Eduard Pokonechny, Ekaterina Lobacheva, Dmitry Vetrov

NeurIPSW 2023 Large Learning Rates Improve Generalization: But How Large Are We Talking About? Ekaterina Lobacheva, Eduard Pokonechny, Maxim Kodryan, Dmitry Vetrov

AISTATS 2023 MARS: Masked Automatic Ranks Selection in Tensor Decompositions Maxim Kodryan, Dmitry Kropotov, Dmitry Vetrov

ICCV 2023 StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-Shot and Few-Shot Domain Adaptation Aibek Alanov, Vadim Titov, Maksim Nakhodnov, Dmitry Vetrov

ICML 2020 Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin, Dmitry Vetrov

AISTATS 2020 Deterministic Decoding for Discrete Data in Variational Autoencoders Daniil Polykovskiy, Dmitry Vetrov

UAI 2020 Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Molchanov, Dmitry Vetrov

ICLR 2020 Implicit Λ-Jeffreys Autoencoders: Taking the Best of Both Worlds Aibek Alanov, Max Kochurov, Artem Sobolev, Daniil Yashkov, Dmitry Vetrov

ICML 2020 Involutive MCMC: A Unifying Framework Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov

ICLR 2020 Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry Vetrov

ICLRW 2020 Stochasticity in Neural ODEs: An Empirical Study Alexandra Volokhova, Viktor Oganesyan, Dmitry Vetrov

ICLR 2020 Towards Understanding the True Loss Surface of Deep Neural Networks Using Random Matrix Theory and Iterative Spectral Methods Diego Granziol, Timur Garipov, Dmitry Vetrov, Stefan Zohren, Stephen Roberts, Andrew Gordon Wilson

AISTATS 2019 Doubly Semi-Implicit Variational Inference Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry Vetrov

UAI 2019 Subspace Inference for Bayesian Deep Learning Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson

ICLR 2019 Variance Networks: When Expectation Does Not Meet Your Expectations Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov

ICLR 2019 Variational Autoencoder with Arbitrary Conditioning Oleg Ivanov, Michael Figurnov, Dmitry Vetrov

ACML 2018 ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks Iurii Kemaev, Daniil Polykovskiy, Dmitry Vetrov

CVPR 2017 Spatially Adaptive Computation Time for Residual Networks Michael Figurnov, Maxwell D. Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, Ruslan Salakhutdinov

ICML 2017 Variational Dropout Sparsifies Deep Neural Networks Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov

ICCV 2015 Inferring M-Best Diverse Labelings in a Single One Alexander Kirillov, Bogdan Savchynskyy, Dmitrij Schlesinger, Dmitry Vetrov, Carsten Rother

ICML 2014 Putting MRFs on a Tensor Train Alexander Novikov, Anton Rodomanov, Anton Osokin, Dmitry Vetrov

ICML 2014 Variational Inference for Sequential Distance Dependent Chinese Restaurant Process Sergey Bartunov, Dmitry Vetrov

CVPR 2013 Spatial Inference Machines Roman Shapovalov, Dmitry Vetrov, Pushmeet Kohli

ACML 2010 Variational Relevance Vector Machine for Tabular Data Dmitry Kropotov, Dmitry Vetrov, Lior Wolf, Tal Hassner