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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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Innovations and regional economic development in Russia

P. 306–317.
Kolchinskaya E.

To be competitive in the global economy Russia needs in production innovative products. According to the Government of the Russian Federation the innovative socially oriented model of development is the main method of long-term development of the country. The data for research were taken from Russians Federal State Statistics Service and portal of the State support of innovative development of business. The main method of this research is a production function which was constructed for the Russian manufacturing. Labour, capital, infrastructure indices (included innovations) were evaluated. I investigate 9 years (from 2005 to 2014) and almost all Russian regions. The coefficients for the selected function were obtained using the correlation-regression analysis approach. The result is that the coefficient of innovation in logarithmic production function is 0,1 and it’s significant on the 1% level. One of the main factor except labor and capital is innovations that means that to increase manufacturing production in Russia the government should improve innovation infrastructure. And also two groups of regions were compared. The result is that regions with innovative clusters on their territory have a better economic indexes than regions without clusters.

Language: English
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Keywords: innovationsproduction functionmanufacturing

In book

Proceedings of the 4th International Conference “Innovation Management, Enterpreneurship and Corporate Sustainability”
Prague: University of Economics, 2016.
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