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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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?

Спрос на навыки: анализ на основе онлайн данных о вакансиях

Экономический журнал Высшей школы экономики. 2022. Т. 26. № 3. С. 343–374.
Volgin A., Gimpelson V. E.

Ultimate essence of the human capital is in what workers know and what they are able
to do, or in their productive skills. This study explores demand for various occupational and
social skills in the Russian economy. Job structure and demand for skills posted by employers
reflect technological level of the economy and its structure. However, the structure of demand
for skills is observed poorly, if we rely on traditional data sources. In this study, we derive the
information from vacancy ads posted on the website of one of the leading Russian internet job
search boards during 2019–2020. Our sample consists of roughly 3.5 million ads, containing
explicit sets of skills expected from successful job candidates. We select skills from job ads relying
on key words and then aggregate them in groups. As the next step, we rank all aggregate skills
groups and discuss their frequencies, taking into account their complementarity. Further on, we
regress wages offered in ads on skills and controls, thus getting a skill premium for each skill
group and their bundles. Many skills are complementary to each other and their combination increases
the premium. Our results suggest that social and client oriented skills as well as medium
level occupational skills are in the highest demand on the Russian labor market.

Research target: Economics and Management Sociology (including Demography and Anthropology
Language: Russian
Full text
DOI
Text on another site
Keywords: профессиинавыкиskillsспрос на трудвакансииlabor demandvacanciesoccupations
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