Dmitry Molchanov

22 papers A* 4A 2Misc 1Journal 10Unranked 5

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model Encodings. AAAI Alexander Shabalin, Viacheslav Meshchaninov, Egor Chimbulatov, Vladislav Lapikov, Roman Kim, Grigory Bartosh, Dmitry Molchanov, Sergey Markov, Dmitry P. Vetrov
2024	Misc	`conf`	Star-Shaped Denoising Diffusion Probabilistic Models (Extended Abstract). KI Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Viktor Ohanesian, Aibek Alanov, Dmitry P. Vetrov
2024	J	`jnl`	TEncDM: Understanding the Properties of Diffusion Model in the Space of Language Model Encodings. CoRR Alexander Shabalin, Viacheslav Meshchaninov, Tingir Badmaev, Dmitry Molchanov, Grigory Bartosh, Sergey Markov, Dmitry P. Vetrov
2023	A*	`conf`	Star-Shaped Denoising Diffusion Probabilistic Models. NeurIPS Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Viktor Ohanesian, Aibek Alanov, Dmitry P. Vetrov
2023	J	`jnl`	Star-Shaped Denoising Diffusion Probabilistic Models. CoRR Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Aibek Alanov, Dmitry P. Vetrov
2020	A	`conf`	Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation. UAI Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Molchanov, Dmitry P. Vetrov
2020	J	`jnl`	Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation. CoRR Dmitry Molchanov, Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry P. Vetrov
2020	A*	`conf`	Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning. ICLR Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry P. Vetrov
2020	J	`jnl`	Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning. CoRR Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry P. Vetrov
2020	J	`jnl`	Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks. CoRR Viktor Yanush, Alexander Shekhovtsov, Dmitry Molchanov, Dmitry P. Vetrov
2019	A	`conf`	Doubly Semi-Implicit Variational Inference. AISTATS Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry P. Vetrov
2019	—	`conf`	Uncertainty Estimation via Stochastic Batch Normalization. ISNN (1) Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov
2019	—	`conf`	Variance Networks: When Expectation Does Not Meet Your Expectations. ICLR (Poster) Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
2018	—	`conf`	Bayesian Incremental Learning for Deep Neural Networks. ICLR (Workshop) Max Kochurov, Timur Garipov, Dmitry Podoprikhin, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
2018	J	`jnl`	Bayesian Incremental Learning for Deep Neural Networks. CoRR Max Kochurov, Timur Garipov, Dmitry Podoprikhin, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
2018	J	`jnl`	Doubly Semi-Implicit Variational Inference. CoRR Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry P. Vetrov
2018	—	`conf`	Uncertainty Estimation via Stochastic Batch Normalization. ICLR (Workshop) Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov
2018	J	`jnl`	Uncertainty Estimation via Stochastic Batch Normalization. CoRR Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov
2018	J	`jnl`	Variational Dropout via Empirical Bayes. CoRR Valery Kharitonov, Dmitry Molchanov, Dmitry P. Vetrov
2017	—	`conf`	Structured Bayesian Pruning via Log-Normal Multiplicative Noise. NIPS Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
2017	A*	`conf`	Variational Dropout Sparsifies Deep Neural Networks. ICML Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
2017	J	`jnl`	Variational Dropout Sparsifies Deep Neural Networks. CoRR Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov