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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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A procedural egalitarian solution for NTU-games

Discrete Applied Mathematics. 2019. P. 1–25.
Dietzenbacher B., Borm P., Hendrickx R.
In press

This paper introduces and analyzes a procedural egalitarian solution for nontransferable utility games. This concept is based on an egalitarian procedure in which egalitarian opportunities of coalitions are explicitly taken into account. We formulate conditions under which the new solution prescribes a core element and derive a direct expression on the class of bargaining games and the class of bankruptcy games.

Priority areas: economics mathematics
Language: English
DOI
Text on another site
Keywords: egalitarianismegalitarian procedureprocedural egalitarian solutionegalitarian stabilityconstrained relative equal awards ruleNTU-games
Publication based on the results of:
Mechanism Design: internet-markets (2019)
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