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Regular version of the site
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

Book chapter
Computing majority by constant depth majority circuits with low fan-in gates

Kulikov A., Podolskii V. V.

In bk.: 34th Symposium on Theoretical Aspects of Computer Science (STACS 2017). March 8–11, 2017, Hannover, Germany. Vol. 66. Leipzig: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2017. P. 1-14.

Book chapter
GANs for Biological Image Synthesis

Osokin A., Chessel A., Carazo Salas R. E. et al.

In bk.: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017). Venice: IEEE, 2017. P. 2252-2261.

Article
Correction to the leading term of asymptotics in the problem of counting the number of points moving on a metric tree

V.L. Chernyshev, Tolchennikov A.

Russian Journal of Mathematical Physics. 2017. Vol. 24. No. 3. P. 290-298.

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.

Research&Expertise – News

Teaching assistant of our department is starting three-month internship in DeepMind

Teaching assistant of our department (Sergey Bartunov) is starting his three-month internship in DeepMind, a research division of Google. There he will work on developing new methods of deep learning and on new new architectures of neural netowkrs.

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).

First Year PhD Student of the Department to Do an Internship in Austria

Dmitry Kondrashkin, a first-year PhD student of the Department, will do an internship from June 1 till August 31, 2015 at the Institute of Science and Technology, Austria.
Dmitry will work in the group of Dr. Lampert. He will do his research in the field of machine learning.

La Serena School for Data Science: Applied Tools for Astronomy

La Serena School for Data Science: Applied Tools for Astronomy
The LA SERENA SCHOOL FOR DATA SCIENCE: Applied Tools for Astronomy is an intensive week of interdisciplinary lectures focused on applied tools for handling big astronomical data. Participants will be instructed in how astronomical data are processed, accessed and analyzed, including reduction pipelines, databases, and scientific programming.  The School will be taught by an international and interdisciplinary group of professors who will use real data and examples. Participants will work on team-based projects and be provided training and access to the National Laboratory for High Performance Computing located at the University of Chile's Center for Mathematical Modeling.

The head of Department is invited to give a series of talks on Microsoft Machine Learning and Intelligence School

The head of Department is invited to give a series of talks on Microsoft Machine Learning and Intelligence School
The head of Department Dmitry Vetrov is invited to give a series of talks on Microsoft Machine Learning and Intelligence School that will take place in Saint Petersburg, Russia, from July 29 to August 5, 2015.
The School is sponsored by Microsoft Research and Yandex.

Machine Learning Becomes Major Research Field at Big Data and Information Retrieval School

Sergey Bartunov, Lecturer at the Big Data and Information Retrieval School, gave a talk about machine learning to first-year students. He mentioned theoretical issues and main applied problems that can be successfully solved using the methods of machine learning.

Best Presentations on Data Mining

SlideShare data mining presentations cover many topics, offering a unique way of consuming data mining content and exploring a variety of slideshows, both narrow and broad in scope.

'Big Data' Help Doctors Choose a Treatment Method

Over the course of 20 years, since the beginning of contemporary medicine’s transition into a digital format, a vast amount of largely unused data has amassed. The analysis of these data and the extraction of a new logic of control from them is one of the most popular areas of focus in applied mathematics, Oleg Pianykh, a Professor in HSE’s Department of Data Analysis and Artificial Intelligence and an Associate Professor at Harvard Medical School, said in a report. His report, 'Big Data in Medicine: How to Make them Work,' was presented at HSE’s academic seminar 'Mathematical Models of Information Technologies.'
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