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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.
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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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Построение COGARCH (Continuous GARCH) модели

С. 171–176.
Panov V., Markova A.

       The article considers the procedure of constructing COGARCH volatility models with continuous time based on the Levy processes. The article describes the procedure of constructing the model in the general case and in the case of compound Poisson process.

Language: Russian
Full text
Text on another site
Keywords: волатильностьстохастическое моделированиеvolatilityLevy processesstochastic modelingПроцессы ЛевиCOGARCH modelмодель COGARCH
Publication based on the results of:
Стохастические и функционально-аналитические методы в исследовании сложных динамических процессов в экономике (2014)

In book

Математическое моделирование в экономике, страховании и управлении рисками: сборник материалов IV Международной молодежной научно-практической конференции
Т. 1. , Саратов: Издательство Саратовского университета, 2015.
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