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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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Репродуктивные намерения россиян с детьми в начале пандемии COVID-19

Вопросы экономики. 2023. № 4. С. 85–102.
Vakulenko E., Mitrofanova E., Gorskiy D.

The paper studies the reproductive intentions of the Russians who already have at least one child before and during the quarantine restrictions (lockdowns) due to COVID-19. Binary choice logistic models and a set of machine learning models were evaluated on the data from the Person, Family, Society survey conducted in March—May 2020. Modeling of the respondents’ intentions to have more children in the short term (3 years) and the long term (more than 3 years) showed that external shocks such as the COVID-19 pandemic and the strict quarantine regulations associated with it had an impact on fertility intentions, but only in the short term. These external shocks were less significant than such factors as age of the respondent; the number of children already born; availability of housing needed to expand the family; the maternity capital program availability, which includes the opportunity to improve housing conditions after the birth of children. As Russians with children showed the increase of the probability of having additional children after April 2020, we conclude that the effect of COVID-19 on reproductive intentions was acyclical. The estimation of models with heterogeneous effects showed that the pandemic had a stronger impact on respondents with above-average household incomes.

Research target: Economics and Management Sociology (including Demography and Anthropology
Language: Russian
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Keywords: рождаемостьfertilityэкономические факторыlockdownCOVID-19economic factorsлокдаунCOVID-19intentions to have childrenнамерения родить детей
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