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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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Использование метода главных компонент для анализа надежности цепей поставок

Логистика и управление цепями поставок. 2018. № 4 (87). С. 27–33.
Kuznetsov V. O.

One of the options for a more flexible approach to analyzing the reliability of supply chains is the principal component analysis (PCA). With a large number of variables describing supply chain, it is a difficult task to analyze the structure of variables in two-dimensional space. Within the analysis of the variables dependencies PCA allows to go from multidimensional space to low-dimensional space, leaving the most informative data in the array for analysis. Based on the generated data set, this paper demonstrates a possibility of applying PCA to supply chain reliability analysis. The generated data set includes observations of 50 supply chains described by five variables. Based on the array, maximizing the linear combination of parameters for each observation, we determined load coefficients and estimates of each of the main components. The calculation of these coefficients made it possible to move from multidimensional space to a two-dimensional one. The two-dimensional representation of all the data whose axes are the first two main components, explaining 84% of the variance, allows to see the structure of all supply chains, to identify outsiders and leaders in this set.

Research target: Computer Science Economics and Management Mathematics
Priority areas: economics IT and mathematics mathematics
Language: Russian
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Keywords: машинное обучениенадежностьцепь поставокметод главных компонентsupply chain«обучение без учителя»machine learningUnsupervised learning principal component analysis Reliability
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