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Regular version of the site
Book chapter
Stochasticity in Algorithmic Statistics for Polynomial Time

Vereshchagin N., Milovanov A.

In bk.: 32nd Computational Complexity Conference. Вадерн: Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, 2017. P. 1-18.

Working paper
Spatially Adaptive Computation Time for Residual Networks

Figurnov M., Collins M. D., Zhu Y. et al.

arXiv:1612.02297. arXiv. Cornell University, 2016

Summer school on Machine Learning in High Energy Physics

August 27-30, St.-Petersburg. MLHEP summer school is intended to cover the relatively young area of data analysis and computational research that has started to emerge in High Energy Physics (HEP). It is known by several names including “Multivariate Analysis”, “Neural Networks”, “Classification/Clusterization techniques”.  In more generic terms, these techniques belong to the field of “Machine Learning”, which is an area that is based on research performed in Statistics and has received a lot of attention from the Data Science community.

Website of school: http://www.hse.ru/mlhep2015/