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News
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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Создание, приобретение и выбор партнёров: стратегии обучения среди российских компаний

Журнал институциональных исследований. 2018. Т. 10. № 4. С. 78–96.
Marques II I.

How do firms decide between different strategies for acquiring highly skilled workers? Existing theory on firms’ training strategies is premised on the ability of firms to solve a fundamental commitment problem, which retards cooperation between firms and other actors. Only with a strong civil society – employers’ associations and labor unions –or free, transparent and efficient market mechanisms is cooperation possible. Contemporary Russia presents a puzzle to this work. On the one hand, firms increasingly make costly co-investments with state-run schools and outsource training to private third-parties. On the other hand, civil society and judicial institutions are weak in Russia, making it difficult for firms to punish counterparties, at the same time that its markets have failed to fully make the transition to free and transparent capitalism. This paper argues that absent strong civil society and free, transparent markets, firms can overcome commitment problems and work with third-parties so long as their regional governments have strong state capacity and are politically accountable. The former assures firms that central, regional authorities can monitor school officials and private-third parties to ensure agreements are honored, while the latter creates incentives for regional authorities to do so. These theories are tested on original survey data covering 690 firms in 12 Russian regions.

Priority areas: economics
Language: Russian
DOI
Text on another site
Keywords: человеческий капиталРоссияповышение квалификацииRussiaeducation strategiesстратегии обученияhuman capitalстратегии компанийUpgrading QualificationsFirms' Strategies
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