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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
‘In the Age of Technology, It Is Interesting to Look into the Past and Think about What We Can Take from It
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.
June 5, 2026
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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Parameter-Efficient Tuning of Transformer Models for Anglicism Detection and Substitution in Russian

P. 295–306.
Daniil Lukichev, Kryanina D., Anastasia Bystrova, Fenogenova A., Tikhonova M.

This article is devoted to the problem of Anglicisms in texts in Russian: the tasks of detection and automatic rewriting of the text with the substitution of Anglicisms by their Russian-language equivalents. Within the framework of the study, we present a parallel corpus of Anglicisms and models that identify Anglicisms in the text and replace them with the Russian equivalent, preserving the stylistics of the original text.

Language: English
Full text
DOI
Text on another site
Keywords: natural language processingparaphraseanglicisms language modelsstyletransfer
Publication based on the results of:
Theoretical and methodological innovations in network analysis: new tools for management decision-making (2023)

In book

Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог». Вып. 22.
Вып. 22. , [б.и.], 2023.
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