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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.
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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.
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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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The State as the Driver of Competitiveness in Russian Higher Education: The Case of Project 5-100

Ch. 9. P. 237–257.
Froumin I., Lisyutkin M.

This chapter begins with explanations of the historical transformations and peculiarities of the Soviet higher education system. It describes the Soviet government’s interpretations of university competitiveness, which have influenced the current state of the Russian higher education system. Then the chapter discusses the post-Soviet path of Russian universities in their search for balance between the de-Sovietization and Westernization of higher education. The discussion proceeds to outlining the new environment of global competition among universities, shaped by world university rankings and the rapid expansion of governmental policies in higher education. Finally, the chapter describes how the Russian government is reacting to the changing environment and the policies used to enhance higher education competitiveness. We consider, as a case study, the Russian excellence initiative in higher education – Project 5-100 – whose basic idea is that at least five Russian universities will be ranked among the world’s top 100 universities by 2020. Some lessons for future policies and questions for future research are formulated in the concluding part.

Language: English
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Keywords: Global Competitivenessглобальная конкурентоспособностьпроект 5-100Project 5-100

In book

International Status Anxiety and Higher Education: The Soviet Legacy in China & Russia
International Status Anxiety and Higher Education: The Soviet Legacy in China & Russia
Hong Cong: Springer, 2018.
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