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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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Моделирование тепловых режимов электронных компонентов

С. 229–232.
К. О. Петросянц
Language: Russian
Full text
Keywords: электронные компонентыelectronic componentsтепловые режимыthermal regimesElectro-thermal simulationэлектро-тепловое моделированиеmultilevel modelingмногоуровневое моделирование
Publication based on the results of:
Development of the methods for multilevel research and simulation of modern temperature and radiation hardened microelectronic components from material level to circuit level (2013)

In book

Твердотельная электроника. Сложные функциональные блоки РЭА. Материалы XII Научно-технической конференции
М.: МНТОРЭС им. А. С. Попова, 2013.
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