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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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Matchings with Simplest Semiorder Preference Relations

P. 119–121.
Kiselgof S. G.

Matching problem with preferences being simplest semiorders is analysed. It is proved that a stable matching always exists. Furthermore, for any stable matching there exists a linear extension of preferences, which does not sontradict stability of a matching. In the college admission problem common goal is to find student-optimal stable matching. We provide a simple criteria (Stable Improvement Cycle existence), that allows to check, whether some particular stable matching is student-side Pareto optimal.

Language: English
Full text
Keywords: matchingscollege admission problemsemiorder preferences

In book

Game Theory and Management. Collected abstracts of papers presented on the Fifth International Conference Game Theory and Management
St. Petersburg: Graduate School of Management, St. Petersburg University, 2011.
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