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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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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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Стресс-тестирование кредитного риска кластера российских коммерческих банков

Бизнес-информатика. 2019. Т. 13. № 3. С. 35–51.
Биджоян Д. С., Bogdanova T., Neklyudov D.

Stress testing as an instrument of risk evaluating is actively used in many international organizations, as well as by Central banks in many countries. Some organizations (including the Bank of Russia) conducting stress testing do not public results of tests, which are interesting to the business society. They do so to avoid some panic moods on markets which could lead to massive outflow of deposits from banking sector as a whole or from some individual banks. As a rule, stress testing is conducted relying on huge number of unpublished reporting forms, but business society has no access to them. Only four reporting forms are presented in the Bank of Russia’s website.

In this paper we propose a simplified algorithm of credit risk stress testing of a banking cluster, based on the four officially published reporting forms. The algorithm provides modelling of median values of banking variables depending on macroeconomic indicators, and subsequent retranslation of the received values for assessing financial position of each bank included in the cluster. It is assumed that growth rates of banking indicators obtained from the econometrics models relying on median values are the same for each bank in the cluster.

As of 1 January 2018, credit risk stress testing was conducted for 26 banks, nine of which are system-significant credit institutions. Within the stress-testing eight econometric time series models were developed. As a result, it was discovered that 11 out of 26 banks in the cluster will face with certain difficulties regarding statutory requirements related with capital ratios or buffers.

Research target: Mathematics Economics and Management
Priority areas: economics business informatics mathematics
Language: Russian
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Keywords: кредитный рискстресс-тестированиеcredit riskeconometric modelэконометрическая модельstress-testingмедианаmacroeconomic factorsmedianмакроэкономические переменные
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