Vetrov, Dmitry P

32 publications

AAAI 2025 TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model Encodings Alexander Shabalin, Viacheslav Meshchaninov, Egor Chimbulatov, Vladislav Lapikov, Roman Kim, Grigory Bartosh, Dmitry Molchanov, Sergey Markov, Dmitry P. Vetrov

AISTATS 2024 Differentiable Rendering with Reparameterized Volume Sampling Nikita Morozov, Denis Rakitin, Oleg Desheulin, Dmitry P Vetrov, Kirill Struminsky

AISTATS 2024 Generative Flow Networks as Entropy-Regularized RL Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P Vetrov

ICLR 2024 Gradual Optimization Learning for Conformational Energy Minimization Artem Tsypin, Leonid Anatolievich Ugadiarov, Kuzma Khrabrov, Alexander Telepov, Egor Rumiantsev, Alexey Skrynnik, Aleksandr Panov, Dmitry P. Vetrov, Elena Tutubalina, Artur Kadurin

ECCV 2024 Guide-and-Rescale: Self-Guidance Mechanism for Effective Tuning-Free Real Image Editing Vadim Titov, Madina Khalmatova, Alexandra Ivanova, Dmitry P Vetrov, Aibek Alanov

NeurIPS 2023 Entropic Neural Optimal Transport via Diffusion Processes Nikita Gushchin, Alexander Kolesov, Alexander Korotin, Dmitry P Vetrov, Evgeny Burnaev

NeurIPS 2023 Star-Shaped Denoising Diffusion Probabilistic Models Andrey Okhotin, Dmitry Molchanov, Arkhipkin Vladimir, Grigory Bartosh, Viktor Ohanesian, Aibek Alanov, Dmitry P Vetrov

NeurIPS 2023 To Stay or Not to Stay in the Pre-Train Basin: Insights on Ensembling in Transfer Learning Ildus Sadrtdinov, Dmitrii Pozdeev, Dmitry P Vetrov, Ekaterina Lobacheva

NeurIPS 2022 HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks Aibek Alanov, Vadim Titov, Dmitry P Vetrov

NeurIPS 2022 Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes Maxim Kodryan, Ekaterina Lobacheva, Maksim Nakhodnov, Dmitry P Vetrov

NeurIPS 2021 Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces Kirill Struminsky, Artyom Gadetsky, Denis Rakitin, Danil Karpushkin, Dmitry P Vetrov

NeurIPS 2021 On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay Ekaterina Lobacheva, Maxim Kodryan, Nadezhda Chirkova, Andrey Malinin, Dmitry P Vetrov

AAAI 2020 Low-Variance Black-Box Gradient Estimates for the Plackett-Luce Distribution Artyom Gadetsky, Kirill Struminsky, Christopher Robinson, Novi Quadrianto, Dmitry P. Vetrov

NeurIPS 2020 On Power Laws in Deep Ensembles Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry P Vetrov

AAAI 2020 Structured Sparsification of Gated Recurrent Neural Networks Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich, Dmitry P. Vetrov

NeurIPS 2019 A Prior of a Googol Gaussians: A Tensor Ring Induced Prior for Generative Models Maxim Kuznetsov, Daniil Polykovskiy, Dmitry P Vetrov, Alex Zhebrak

NeurIPS 2019 A Simple Baseline for Bayesian Uncertainty in Deep Learning Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, Andrew Gordon Wilson

NeurIPS 2019 Importance Weighted Hierarchical Variational Inference Artem Sobolev, Dmitry P Vetrov

NeurIPS 2019 The Implicit Metropolis-Hastings Algorithm Kirill Neklyudov, Evgenii Egorov, Dmitry P Vetrov

UAI 2018 Averaging Weights Leads to Wider Optima and Better Generalization Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson

AISTATS 2018 Few-Shot Generative Modelling with Generative Matching Networks Sergey Bartunov, Dmitry P. Vetrov

NeurIPS 2018 Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, Andrew G Wilson

ICLR 2017 Fast Adaptation in Generative Models with Generative Matching Networks Sergey Bartunov, Dmitry P. Vetrov

NeurIPS 2017 Structured Bayesian Pruning via Log-Normal Multiplicative Noise Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P Vetrov

AISTATS 2016 Breaking Sticks and Ambiguities with Adaptive Skip-Gram Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry P. Vetrov

NeurIPS 2016 PerforatedCNNs: Acceleration Through Elimination of Redundant Convolutions Mikhail Figurnov, Aizhan Ibraimova, Dmitry P Vetrov, Pushmeet Kohli

NeurIPS 2015 M-Best-Diverse Labelings for Submodular Energies and Beyond Alexander Kirillov, Dmytro Shlezinger, Dmitry P Vetrov, Carsten Rother, Bogdan Savchynskyy

NeurIPS 2015 Tensorizing Neural Networks Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, Dmitry P Vetrov

ECCV 2012 Submodular Relaxation for MRFs with High-Order Potentials Anton Osokin, Dmitry P. Vetrov

ECCVW 2012 Submodular Relaxation for MRFs with High-Order Potentials Anton Osokin, Dmitry P. Vetrov

CVPR 2011 Submodular Decomposition Framework for Inference in Associative Markov Networks with Global Constraints Anton Osokin, Dmitry P. Vetrov, Vladimir Kolmogorov

ICML 2007 On One Method of Non-Diagonal Regularization in Sparse Bayesian Learning Dmitry Kropotov, Dmitry P. Vetrov