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Regular version of the site

Centre of Deep Learning and Bayesian Methods

2

November 15

This fall, 4 members of our research group have completed and successfully defended their PhD studies.

November 10

In 2022, 17 articles by the researchers of HSE Faculty of Computer Science were accepted at the NeurIPS (Conference and Workshop on Neural Information Processing Systems), one of the world’s most prestigious events in the field of machine learning and artificial intelligence. The 36th conference will be held in a hybrid format from November 28th to December 9th in New Orleans (USA).
Publications
Book chapter
FFC-SE: Fast Fourier Convolution for Speech Enhancement

Shchekotov I., Andreev P., Ivanov O. et al.

In bk.: InterSpeech 2022. International Speech Communication Association, 2022. P. 1188-1192.

Article
Study on precoding optimization algorithms in massive MIMO system with multi-antenna users

Bobrov E., Kropotov D., Troshin S. et al.

Optimization Methods and Software. 2022. P. 1-16.

Book chapter
Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces

Kirill Struminsky, Artyom Gadetsky, Denis Rakitin et al.

In bk.: Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Curran Associates, Inc., 2021. P. 10999-11011.

Book chapter
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay

Lobacheva E., Kodryan M., Chirkova N. et al.

In bk.: Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Curran Associates, Inc., 2021. P. 21545-21556.

Book chapter
Deterministic Decoding for Discrete Data in Variational Autoencoders

Polykovskiy D., Vetrov D.

In bk.: Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108. Iss. 108. PMLR, 2020. P. 3046-3056.

About the Center

The center conducts research at the intersection of two actively developing areas of data analysis: deep learning and Bayesian methods of machine learning methods. Deep learning is a section that involves building very complex models (neural networks) to solve problems such as classifying images or music, transferring an art style from picture to photograph, predicting the next words in a text. Within the framework of the Bayesian approach, probabilistic models based on the apparatus of probability theory and mathematical statistics are considered for solving such problems.

The center was created on the basis of the Bayesian Methods Research Group.