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News
June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
June 5, 2026
‘In the Age of Technology, It Is Interesting to Look into the Past and Think about What We Can Take from It
Polina Tabakova decided to apply for a Philology degree at HSE in Nizhny Novgorod because she grew up in Mari El and did not want to move far away from the Russian forests. In an interview for the Young Scientists of HSE University project, she spoke about the genre of the campus novel, the existential drama of Kolobok, and a blackout version of Eugene Onegin.
June 5, 2026
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.

 

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Исследование возможности классификации фондовых рынков различных стран с помощью сетевой модели

Вестник Воронежского государственного университета. Серия: Системный анализ и информационные технологии. 2016. № 3. С. 111–115.
Vizgunov A. N., Наумова А. С.

The globalization process makes all countries stock markets similar to each other. In the paper we try to evaluate this process by analyzing the network models of some countries stock markets by means of neural networks. Our results show that each of the considered countries stock market has a peculiarity that can be used to distinguish the markets. 

Priority areas: IT and mathematics business informatics
Language: Russian
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Keywords: фондовый рынокглобализацияglobalizationнейронные сетиneural networksMarket network modelсетевая модельstock market
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