Dmitry Molchanov

According to our database, Dmitry Molchanov authored at least 13 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

TEncDM: Understanding the Properties of Diffusion Model in the Space of Language Model Encodings.

Alexander Shabalin, Viacheslav Meshchaninov, Tingir Badmaev, Dmitry Molchanov, Grigory Bartosh, Sergey Markov, Dmitry P. Vetrov

CoRR, 2024

2023

Star-Shaped Denoising Diffusion Probabilistic Models.

Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Aibek Alanov, Dmitry P. Vetrov

CoRR, 2023

Star-Shaped Denoising Diffusion Probabilistic Models.

Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Viktor Ohanesian, Aibek Alanov, Dmitry P. Vetrov

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2020

Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks.

Viktor Yanush, Alexander Shekhovtsov, Dmitry Molchanov, Dmitry P. Vetrov

CoRR, 2020

Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation.

Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Molchanov, Dmitry P. Vetrov

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning.

Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry P. Vetrov

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Uncertainty Estimation via Stochastic Batch Normalization.

Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov

Proceedings of the Advances in Neural Networks - ISNN 2019, 2019

Variance Networks: When Expectation Does Not Meet Your Expectations.

Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov

Proceedings of the 7th International Conference on Learning Representations, 2019

Doubly Semi-Implicit Variational Inference.

Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry P. Vetrov

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Variational Dropout via Empirical Bayes.

Valery Kharitonov, Dmitry Molchanov, Dmitry P. Vetrov

CoRR, 2018

Bayesian Incremental Learning for Deep Neural Networks.

Max Kochurov, Timur Garipov, Dmitry Podoprikhin, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Structured Bayesian Pruning via Log-Normal Multiplicative Noise.

Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variational Dropout Sparsifies Deep Neural Networks.

Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov

Proceedings of the 34th International Conference on Machine Learning, 2017