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Regular version of the site

Centre of Deep Learning and Bayesian Methods

2

September 11

The second Summer School on Deep Learning and Bayesian Methods was held in Moscow from August 27 to September 1, this year in English. During 6 days participants were studying and implementing Bayesian methods in neural networks, exchanging their experience and discussing research ideas.

March 05

Samsung-HSE Laboratory will develop mechanisms of Bayesian inference in modern neural networks, which will solve a number of problems in deep learning. The laboratory team will be made up of the members of the Bayesian Methods Research Group — one of the strongest scientific groups in Russia in the field of machine learning and Bayesian inference. It will be headed by a professor of the Higher School of Economics Dmitry Vetrov.
Publications
Article
Randomized Block Cubic Newton Method
In press

Doikov Nikita, Richtarik P.

Proceedings of Machine Learning Research. 2018. No. 80. P. 1290-1298.

Book chapter
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
In press

Struminsky K., Lacoste-Julien S., Osokin A.

In bk.: Advances in Neural Information Processing Systems 31 (NIPS 2018). 2018.

Working paper
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition

Izmailov P., Novikov A., Kroptov D.

math. arxive. Cornell University, 2017

About the Center

International Laboratory of Deep Learning and Bayesian Methods is established on the basis of Bayesian Methods Research Group. The group is one of the strongest scientific groups in Russia in the area of machine learning and probabilistic modeling. The laboratory researches the neurobayesian models that combine the advantages of the two most successful machine learning approaches, namely neural networks and Bayesian methods.