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ORCIDDmitry P. Vetrov
Person information
- affiliation: Samsung AI Center Moscow, Russia
- affiliation: National Research University Higher School of Economics (HSE), Moscow, Russia
- affiliation: Skolkovo Institute of Science and Technology, Moscow Russia
- affiliation: Moscow State University (MSU), Department of Computational Mathematics and Cybernetics, Russia
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2020 – today
- 2024
[c78]Daniil Tiapkin
, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov:
Generative Flow Networks as Entropy-Regularized RL. AISTATS 2024: 4213-4221- [c77]Nikita Morozov, Denis Rakitin, Oleg Desheulin, Dmitry P. Vetrov, Kirill Struminsky:
Differentiable Rendering with Reparameterized Volume Sampling. AISTATS 2024: 4852-4860 - [c76]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 - [c75]Grigory Bartosh, Dmitry P. Vetrov, Christian A. Naesseth:
Neural Diffusion Models. ICML 2024 - [c74]Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Viktor Ohanesian, Aibek Alanov, Dmitry P. Vetrov:
Star-Shaped Denoising Diffusion Probabilistic Models (Extended Abstract). KI 2024: 355-359 - [i73]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 abs/2402.19097 (2024) - [i72]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 abs/2403.03726 (2024) - [i71]Maxim Nikolaev, Mikhail Kuznetsov, Dmitry P. Vetrov, Aibek Alanov:
HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach. CoRR abs/2404.01094 (2024) - [i70]Grigory Bartosh, Dmitry P. Vetrov, Christian A. Naesseth:
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling. CoRR abs/2404.12940 (2024) - 2023
- [c73]Maxim Kodryan, Dmitry Kropotov, Dmitry P. Vetrov:
MARS: Masked Automatic Ranks Selection in Tensor Decompositions. AISTATS 2023: 3718-3732 - [c72]Pavel Andreev, Aibek Alanov, Oleg Ivanov, Dmitry P. Vetrov:
HIFI++: A Unified Framework for Bandwidth Extension and Speech Enhancement. ICASSP 2023: 1-5 - [c71]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: 2184-2194 - [c70]Anastasiia Iashchenko, Pavel Andreev, Ivan Shchekotov, Nicholas Babaev, Dmitry P. Vetrov:
UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model. INTERSPEECH 2023: 4294-4298 - [c69]Nikita Gushchin, Alexander Kolesov, Alexander Korotin, Dmitry P. Vetrov, Evgeny Burnaev:
Entropic Neural Optimal Transport via Diffusion Processes. NeurIPS 2023 - [c68]Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Viktor Ohanesian, Aibek Alanov, Dmitry P. Vetrov:
Star-Shaped Denoising Diffusion Probabilistic Models. NeurIPS 2023 - [c67]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 - [i69]Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Aibek Alanov, Dmitry P. Vetrov:
Star-Shaped Denoising Diffusion Probabilistic Models. CoRR abs/2302.05259 (2023) - [i68]Nikita Morozov, Denis Rakitin, Oleg Desheulin, Dmitry P. Vetrov, Kirill Struminsky:
Differentiable Rendering with Reparameterized Volume Sampling. CoRR abs/2302.10970 (2023) - [i67]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 abs/2303.03374 (2023) - [i66]Anastasiia Iashchenko, Pavel Andreev, Ivan Shchekotov, Nicholas Babaev, Dmitry P. Vetrov:
UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model. CoRR abs/2306.00721 (2023) - [i65]Grigory Bartosh, Dmitry P. Vetrov, Christian A. Naesseth:
Neural Diffusion Models. CoRR abs/2310.08337 (2023) - [i64]Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov:
Generative Flow Networks as Entropy-Regularized RL. CoRR abs/2310.12934 (2023) - [i63]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 abs/2311.06295 (2023) - [i62]Ekaterina Lobacheva, Eduard Pockonechnyy, Maxim Kodryan, Dmitry P. Vetrov:
Large Learning Rates Improve Generalization: But How Large Are We Talking About? CoRR abs/2311.11303 (2023) - 2022
- [c66]Ivan Shchekotov, Pavel K. Andreev, Oleg Ivanov, Aibek Alanov, Dmitry P. Vetrov:
FFC-SE: Fast Fourier Convolution for Speech Enhancement. INTERSPEECH 2022: 1188-1192 - [c65]Aibek Alanov, Vadim Titov, Dmitry P. Vetrov:
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks. NeurIPS 2022 - [c64]Maxim Kodryan, Ekaterina Lobacheva, Maksim Nakhodnov, Dmitry P. Vetrov:
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes. NeurIPS 2022 - [i61]Pavel Andreev, Aibek Alanov, Oleg Ivanov, Dmitry P. Vetrov:
HiFi++: a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement. CoRR abs/2203.13086 (2022) - [i60]Ivan Shchekotov, Pavel Andreev, Oleg Ivanov, Aibek Alanov, Dmitry P. Vetrov:
FFC-SE: Fast Fourier Convolution for Speech Enhancement. CoRR abs/2204.03042 (2022) - [i59]Maxim Kodryan, Ekaterina Lobacheva, Maksim Nakhodnov, Dmitry P. Vetrov:
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes. CoRR abs/2209.03695 (2022) - [i58]Aibek Alanov, Vadim Titov, Dmitry P. Vetrov:
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks. CoRR abs/2210.08884 (2022) - [i57]Nikita Gushchin, Alexander Kolesov, Alexander Korotin, Dmitry P. Vetrov, Evgeny Burnaev:
Entropic Neural Optimal Transport via Diffusion Processes. CoRR abs/2211.01156 (2022) - [i56]Aibek Alanov, Vadim Titov, Maksim Nakhodnov, Dmitry P. Vetrov:
StyleDomain: Analysis of StyleSpace for Domain Adaptation of StyleGAN. CoRR abs/2212.10229 (2022) - 2021
- [c63]Kirill Struminsky, Artyom Gadetsky, Denis Rakitin, Danil Karpushkin, Dmitry P. Vetrov:
Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces. NeurIPS 2021: 10999-11011 - [c62]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: 21545-21556 - [i55]Vyacheslav Alipov, Riley Simmons-Edler, Nikita Putintsev, Pavel Kalinin, Dmitry P. Vetrov:
Towards Practical Credit Assignment for Deep Reinforcement Learning. CoRR abs/2106.04499 (2021) - [i54]Arsenii Ashukha, Andrei Atanov, Dmitry P. Vetrov:
Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations. CoRR abs/2106.08038 (2021) - [i53]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 abs/2106.15739 (2021) - [i52]Pavel Andreev, Alexander Fritzler, Dmitry P. Vetrov:
Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study. CoRR abs/2108.13996 (2021) - [i51]Arsenii Kuznetsov, Alexander Grishin, Artem Tsypin, Arsenii Ashukha, Dmitry P. Vetrov:
Automating Control of Overestimation Bias for Continuous Reinforcement Learning. CoRR abs/2110.13523 (2021) - [i50]Kirill Struminsky, Artyom Gadetsky, Denis Rakitin, Danil Karpushkin, Dmitry P. Vetrov:
Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces. CoRR abs/2110.15072 (2021) - [i49]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 abs/2112.14423 (2021) - 2020
- [c61]Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich, Dmitry P. Vetrov:
Structured Sparsification of Gated Recurrent Neural Networks. AAAI 2020: 4989-4996 - [c60]Artyom Gadetsky, Kirill Struminsky, Christopher Robinson, Novi Quadrianto, Dmitry P. Vetrov:
Low-Variance Black-Box Gradient Estimates for the Plackett-Luce Distribution. AAAI 2020: 10126-10135 - [c59]Daniil Polykovskiy, Dmitry P. Vetrov:
Deterministic Decoding for Discrete Data in Variational Autoencoders. AISTATS 2020: 3046-3056 - [c58]Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry P. Vetrov:
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning. ICLR 2020 - [c57]Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin, Dmitry P. Vetrov:
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics. ICML 2020: 5556-5566 - [c56]Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov:
Involutive MCMC: a Unifying Framework. ICML 2020: 7273-7282 - [c55]Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry P. Vetrov:
On Power Laws in Deep Ensembles. NeurIPS 2020 - [c54]Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Molchanov, Dmitry P. Vetrov:
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation. UAI 2020: 1308-1317 - [c53]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: 214-221 - [i48]Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry P. Vetrov:
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning. CoRR abs/2002.06470 (2020) - [i47]Dmitry Molchanov, Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry P. Vetrov:
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation. CoRR abs/2002.09103 (2020) - [i46]Viktor Oganesyan, Alexandra Volokhova, Dmitry P. Vetrov:
Stochasticity in Neural ODEs: An Empirical Study. CoRR abs/2002.09779 (2020) - [i45]Daniil Polykovskiy, Dmitry P. Vetrov:
Deterministic Decoding for Discrete Data in Variational Autoencoders. CoRR abs/2003.02174 (2020) - [i44]Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin, Dmitry P. Vetrov:
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics. CoRR abs/2005.04269 (2020) - [i43]Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry P. Vetrov:
Deep Ensembles on a Fixed Memory Budget: One Wide Network or Several Thinner Ones? CoRR abs/2005.07292 (2020) - [i42]Viktor Yanush, Alexander Shekhovtsov, Dmitry Molchanov, Dmitry P. Vetrov:
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks. CoRR abs/2006.06880 (2020) - [i41]Maxim Kodryan, Dmitry Kropotov, Dmitry P. Vetrov:
MARS: Masked Automatic Ranks Selection in Tensor Decompositions. CoRR abs/2006.10859 (2020) - [i40]Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov:
Involutive MCMC: a Unifying Framework. CoRR abs/2006.16653 (2020) - [i39]Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry P. Vetrov:
On Power Laws in Deep Ensembles. CoRR abs/2007.08483 (2020)
2010 – 2019
- 2019
- [c52]Kirill Struminsky, Dmitry P. Vetrov:
A Simple Method to Evaluate Support Size and Non-uniformity of a Decoder-Based Generative Model. AIST 2019: 81-93 - [c51]Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry P. Vetrov:
Doubly Semi-Implicit Variational Inference. AISTATS 2019: 2593-2602 - [c50]Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitry P. Vetrov, Max Welling:
The Deep Weight Prior. ICLR (Poster) 2019 - [c49]Oleg Ivanov, Michael Figurnov, Dmitry P. Vetrov:
Variational Autoencoder with Arbitrary Conditioning. ICLR (Poster) 2019 - [c48]Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov:
Variance Networks: When Expectation Does Not Meet Your Expectations. ICLR (Poster) 2019 - [c47]Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov:
Uncertainty Estimation via Stochastic Batch Normalization. ISNN (1) 2019: 261-269 - [c46]Artem Sobolev, Dmitry P. Vetrov:
Importance Weighted Hierarchical Variational Inference. NeurIPS 2019: 601-613 - [c45]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: 4104-4114 - [c44]Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
A Simple Baseline for Bayesian Uncertainty in Deep Learning. NeurIPS 2019: 13132-13143 - [c43]Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov:
The Implicit Metropolis-Hastings Algorithm. NeurIPS 2019: 13932-13942 - [c42]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: 40-48 - [c41]Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Subspace Inference for Bayesian Deep Learning. UAI 2019: 1169-1179 - [i38]Wesley J. Maddox, Timur Garipov, Pavel Izmailov, Dmitry P. Vetrov, Andrew Gordon Wilson:
A Simple Baseline for Bayesian Uncertainty in Deep Learning. CoRR abs/1902.02476 (2019) - [i37]Aibek Alanov, Max Kochurov, Denis Volkhonskiy, Daniil Yashkov, Evgeny Burnaev, Dmitry P. Vetrov:
User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks. CoRR abs/1904.04751 (2019) - [i36]Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha, Ivan Sosnovik, Dmitry P. Vetrov:
Semi-Conditional Normalizing Flows for Semi-Supervised Learning. CoRR abs/1905.00505 (2019) - [i35]Artem Sobolev, Dmitry P. Vetrov:
Importance Weighted Hierarchical Variational Inference. CoRR abs/1905.03290 (2019) - [i34]Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov:
The Implicit Metropolis-Hastings Algorithm. CoRR abs/1906.03644 (2019) - [i33]Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Subspace Inference for Bayesian Deep Learning. CoRR abs/1907.07504 (2019) - [i32]Maksim Kuznetsov, Daniil Polykovskiy, Dmitry P. Vetrov, Alexander Zhebrak:
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models. CoRR abs/1910.13148 (2019) - [i31]Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich, Dmitry P. Vetrov:
Structured Sparsification of Gated Recurrent Neural Networks. CoRR abs/1911.05585 (2019) - [i30]Artyom Gadetsky, Kirill Struminsky, Christopher Robinson, Novi Quadrianto, Dmitry P. Vetrov:
Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution. CoRR abs/1911.10036 (2019) - [i29]Diego Granziol, Xingchen Wan, Timur Garipov, Dmitry P. Vetrov, Stephen Roberts:
MLRG Deep Curvature. CoRR abs/1912.09656 (2019) - 2018
- [c40]Artyom Gadetsky, Ilya Yakubovskiy, Dmitry P. Vetrov:
Conditional Generators of Words Definitions. ACL (2) 2018: 266-271 - [c39]Iurii Kemaev, Daniil Polykovskiy, Dmitry P. Vetrov:
ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks. ACML 2018: 422-437 - [c38]Sergey Bartunov, Dmitry P. Vetrov:
Few-shot Generative Modelling with Generative Matching Networks. AISTATS 2018: 670-678 - [c37]Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry P. Vetrov:
Bayesian Compression for Natural Language Processing. EMNLP 2018: 2910-2915 - [c36]Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov:
Uncertainty Estimation via Stochastic Batch Normalization. ICLR (Workshop) 2018 - [c35]Max Kochurov, Timur Garipov, Dmitry Podoprikhin, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov:
Bayesian Incremental Learning for Deep Neural Networks. ICLR (Workshop) 2018 - [c34]Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew Gordon Wilson:
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. NeurIPS 2018: 8803-8812 - [c33]Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Averaging Weights Leads to Wider Optima and Better Generalization. UAI 2018: 876-885 - [i28]Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov:
Uncertainty Estimation via Stochastic Batch Normalization. CoRR abs/1802.04893 (2018) - [i27]Max Kochurov, Timur Garipov, Dmitry Podoprikhin, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov:
Bayesian Incremental Learning for Deep Neural Networks. CoRR abs/1802.07329 (2018) - [i26]Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew Gordon Wilson:
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. CoRR abs/1802.10026 (2018) - [i25]Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Averaging Weights Leads to Wider Optima and Better Generalization. CoRR abs/1803.05407 (2018) - [i24]Oleg Ivanov, Michael Figurnov, Dmitry P. Vetrov:
Universal Conditional Machine. CoRR abs/1806.02382 (2018) - [i23]Artyom Gadetsky, Ilya Yakubovskiy, Dmitry P. Vetrov:
Conditional Generators of Words Definitions. CoRR abs/1806.10090 (2018) - [i22]Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry P. Vetrov:
Doubly Semi-Implicit Variational Inference. CoRR abs/1810.02789 (2018) - [i21]Aibek Alanov, Max Kochurov, Daniil Yashkov, Dmitry P. Vetrov:
Pairwise Augmented GANs with Adversarial Reconstruction Loss. CoRR abs/1810.04920 (2018) - [i20]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 abs/1810.06943 (2018) - [i19]Kirill Neklyudov, Pavel Shvechikov, Dmitry P. Vetrov:
Metropolis-Hastings view on variational inference and adversarial training. CoRR abs/1810.07151 (2018) - [i18]Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry P. Vetrov:
Bayesian Compression for Natural Language Processing. CoRR abs/1810.10927 (2018) - [i17]Valery Kharitonov, Dmitry Molchanov, Dmitry P. Vetrov:
Variational Dropout via Empirical Bayes. CoRR abs/1811.00596 (2018) - [i16]Iurii Kemaev, Daniil Polykovskiy, Dmitry P. Vetrov:
ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks. CoRR abs/1811.04380 (2018) - [i15]Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry P. Vetrov:
Bayesian Sparsification of Gated Recurrent Neural Networks. CoRR abs/1812.05692 (2018) - 2017
- [c32]Michael Figurnov, Maxwell D. Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry P. Vetrov, Ruslan Salakhutdinov:
Spatially Adaptive Computation Time for Residual Networks. CVPR 2017: 1790-1799 - [c31]Sergey Bartunov, Dmitry P. Vetrov:
Fast Adaptation in Generative Models with Generative Matching Networks. ICLR (Workshop) 2017 - [c30]Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov:
Variational Dropout Sparsifies Deep Neural Networks. ICML 2017: 2498-2507 - [c29]Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov:
Structured Bayesian Pruning via Log-Normal Multiplicative Noise. NIPS 2017: 6775-6784 - [i14]Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov:
Variational Dropout Sparsifies Deep Neural Networks. CoRR abs/1701.05369 (2017) - [i13]Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry P. Vetrov:
Bayesian Sparsification of Recurrent Neural Networks. CoRR abs/1708.00077 (2017) - [i12]Michael Figurnov, Artem Sobolev, Dmitry P. Vetrov:
Probabilistic Adaptive Computation Time. CoRR abs/1712.00386 (2017) - 2016
- [j4]Mikhail Belyaev, Evgeny Burnaev, Ermek Kapushev, Maxim Panov, Pavel V. Prikhodko, Dmitry P. Vetrov, Dmitry Yarotsky:
GTApprox: Surrogate modeling for industrial design. Adv. Eng. Softw. 102: 29-39 (2016) - [c28]Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry P. Vetrov:
Breaking Sticks and Ambiguities with Adaptive Skip-gram. AISTATS 2016: 130-138 - [c27]Alexander Kirillov, Mikhail Gavrikov, Ekaterina Lobacheva, Anton Osokin, Dmitry P. Vetrov:
Deep Part-Based Generative Shape Model with Latent Variables. BMVC 2016 - [c26]Mikhail Figurnov, Aizhan Ibraimova, Dmitry P. Vetrov, Pushmeet Kohli:
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions. NIPS 2016: 947-955 - [c25]Kirill Struminsky, Stanislav Kruglik, Dmitry P. Vetrov, Ivan V. Oseledets:
A new approach for sparse Bayesian channel estimation in SCMA uplink systems. WCSP 2016: 1-5 - [i11]Mikhail Belyaev, Evgeny Burnaev, Ermek Kapushev, Maxim Panov, Pavel V. Prikhodko, Dmitry P. Vetrov, Dmitry Yarotsky:
GTApprox: surrogate modeling for industrial design. CoRR abs/1609.01088 (2016) - [i10]Timur Garipov, Dmitry Podoprikhin, Alexander Novikov, Dmitry P. Vetrov:
Ultimate tensorization: compressing convolutional and FC layers alike. CoRR abs/1611.03214 (2016) - [i9]Michael Figurnov, Kirill Struminsky, Dmitry P. Vetrov:
Robust Variational Inference. CoRR abs/1611.09226 (2016) - [i8]Sergey Bartunov, Dmitry P. Vetrov:
Fast Adaptation in Generative Models with Generative Matching Networks. CoRR abs/1612.02192 (2016) - [i7]Michael Figurnov, Maxwell D. Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry P. Vetrov, Ruslan Salakhutdinov:
Spatially Adaptive Computation Time for Residual Networks. CoRR abs/1612.02297 (2016) - 2015
- [j3]Anton Osokin, Dmitry P. Vetrov:
Submodular Relaxation for Inference in Markov Random Fields. IEEE Trans. Pattern Anal. Mach. Intell. 37(7): 1347-1359 (2015) - [c24]Alexander Kirillov, Bogdan Savchynskyy, Dmitrij Schlesinger, Dmitry P. Vetrov, Carsten Rother:
Inferring M-Best Diverse Labelings in a Single One. ICCV 2015: 1814-1822 - [c23]Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry P. Vetrov:
Tensorizing Neural Networks. NIPS 2015: 442-450 - [c22]