Kirill Struminsky

Postgraduate Student:Faculty of Computer Science / Big Data and Information Retrieval School

Responsibilities

To conduct research in the area of deep generative modeling and related areas of deep learning.

Postgraduate Studies

3rd year of study
Approved topic of thesis: Continuous optimization methods in design of error detection and correction schemes
Academic Supervisor: Vetrov, Dmitry

Courses (2016/2017)

Research seminar "Machine Learning and Applications" (Bachelor’s programme; Faculty of Computer Science; 3 year, 1-4 module)Rus

Publications³

Article Фигурнов М. В., Струминский К. А., Ветров Д. П. Устойчивый к шуму метод обучения вариационного автокодировщика // Интеллектуальные системы. Теория и приложения. 2017. Т. 21. № 2. С. 90-109.
Chapter Struminsky K., Kruglik S., Vetrov D., Oseledets I. A new approach for sparse Bayesian channel estimation in SCMA uplink systems, in: 2016 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016. October 13 - 15, Yangzhou, China. NY : Institute of Electrical and Electronic Engineers, 2016. P. 1-5. doi
Preprint Figurnov M., Struminsky K., Vetrov D. Robust Variational Inference / Cornell University. Series arXiv:1611.09226 "arxiv.org". 2016.

Timetable for today

Full timetable

Jun

2017

'Machine Learning Algorithm Able to Find Data Patterns a Human Could Not'

In December 2016, five new international laboratories opened up at the Higher School of Economics, one of which was the International Laboratory of Deep Learning and Bayesian Methods. This lab focuses on combined neural Bayesian models that bring together two of the most successful paradigms in modern-day machine learning – the neural network paradigm and the Bayesian paradigm.