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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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Использование искусственного интеллекта в вопросах выявления и противодействия коррупции: обзор международного опыта

Государственное управление. Электронный вестник. 2021. № 84. С. 241–255.
Krylova D., Maksimenko A.

In this article, the authors, using the example of several foreign publications, analyze the trends in the use of artificial intelligence and machine learning in discernment of corruption. Based on the international review, the authors make the conclusion that the mechanisms for detecting corruption, based on the use of artificial intelligence, described in foreign sources, have different potential effectiveness. The most promising application of the presented intelligent systems in the field of combating corruption is using them to detect latent relationships and calculate collusion (cartels) in the public procurement system, electronic auctions organized by companies, and ensure transparency of government procedures (electronic digital services). An analysis of a number of articles on the use of artificial intelligence to create visualized maps based on the number of publications on corruption topics in the media in various territorial entities allowed the authors to conclude that the proposed tool is not sufficiently informative for a comparative assessment of the real level of corruption. The authors draw attention to the fact that a large number of such publications may be due not only to the increased level of corruption in a given territory. It is necessary to take into account the influence of such factors as the growth of the public authorities and civil society anti-corruption activity, the possible bias of some journalists for someone’s political and economic purposes, including direct corruption of journalists for publishing ordered articles, the lack of competence of some media on anticorruption issues, use of false information in publications. These maps rather clearly illustrate both the level of interest of society and the media in the problem of combating corruption, and the intensity of the fight against corruption and the activity of civil control by the media

Research target: Political Science, International Relations, and Public Administration Sociology (including Demography and Anthropology Psychology Computer Science
Priority areas: sociology state and public administration IT and mathematics
Language: Russian
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Keywords: коррупцияискусственный интеллектмашинное обучениеcorruptioninternational experienceмеждународный опытmachine learningartificial intelligenceanti-corruptionanti-corruption methodstrends in the use of artificial intelligenceвыявление коррупцииметоды борьбы с коррупциейтенденции использования искусственного интеллекта
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