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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
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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.
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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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Strategic Trading around Anticipated Supply/Demand Shocks

NRU HSE , 2022. No. WP9/2022/02.
Fardeau V.
I study the price/quantity effects of anticipated supply or demand shocks in a model of strategic trading, where imperfectly competitive traders share risk with price-takers. When there are at least two traders, anticipated shocks lead to the V-shaped pattern observed empirically: prices drift away from fundamentals before the shock, and slowly revert afterwards. How traders behave before the shock depends on whether they compete `a la Cournot (i.e. submit market orders) or in demand schedules (using limit orders). Consistent with empirical evidence, Cournot traders act as contrarians, while demand schedule traders first trade against, then with the shock.
Language: English
Text on another site
Keywords: price impactstrategic trading liquiditythin marketsV-shaped price patternsmomentum and reversalanticipated shocks
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Added: November 30, 2022
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