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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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Сравнение моделей оценок VAR на интервалах прогнозирования разной срочности для акций российского фондового рынка

Прикладная эконометрика. 2011. № 4. С. 58–70.
Щерба А. В.

The paper aims at finding the most accurate VaR model for the four most liquid Russian stocks. Among the possible VaR modeling techniques, the estimates considered in this work are based on GARCH models with six different distributions. A back testing analysis is performed to evaluate the accuracy of the alternative models and to find the worst predictable period in terms of the volatility behavior.

Priority areas: economics
Language: Russian
Keywords: VARGARCHmarket riskback testingсумма под рискомGARCH-моделиоценка рыночного рискабэк-тестинг
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