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8495-772-95-90*12342
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Address: Kochnovsky proezd, 3, 625
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Supervisor
V. V. Podolskii
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Dmitry Molchanov

  • Dmitry Molchanov has been at HSE since 2017.

Responsibilities

Conduction of the research on neurobayesian methods, work on laboratory's industrial projects, scientific papers writing 

Education

2016

Bachelor in Applied Mathematics and Information Science
Lomonosov Moscow State University

Professional Interests

Courses (2018/2019)

Courses (2017/2018)

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

Publications2


Employment history

03/17–12/17 Intern researcher at Yandex Research.
03/17– Intern researcher at the International Laboratory of Deep Learning and Bayesian Methods, NRU HSE.

Timetable for today

Full timetable

How to Adjust a Smaller Size Neural Network without Quality Loss

Staff members of the HSE Faculty of Computer Science recently presented their papers at the biggest international conference on machine learning, Neural Information Processing Systems (NIPS)’.

How to Adjust a Smaller Size Neural Network without Quality Loss

Staff members of the HSE Faculty of Computer Science recently presented their papers at the biggest international conference on machine learning, Neural Information Processing Systems (NIPS)’.

Faculty of Computer Science Staff Attend International Conference on Machine Learning 

On August 6-11 the 34th International Conference on Machine Learning was held in Sydney, Australia. This conference is ranked A* by CORE, and is one of two leading conferences in the field of machine learning. It has been held annually since 2000, and this year, more than 1,000 participants from different countries took part.

Variational dropout sparsifies DNNs paper has been accepted to ICML'17

The paper authored by laboratory's research assistants Dmitry Molchanov and Arsenii Ashukha and head Dmitry Vetrov has been accepted to the International Conference on Machine Learning'2017. In this research a state-of-the-art result in deep neural networks sparsification was achieved using Bayesian framework applied to deep learning.