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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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Modelling demand for mortgage loans using loan-level data

P. 241–248.
Ozhegov E. M.

This paper is concerned with modeling the demand for mortgage loans. The   demand for loans can be represented as two functions: probability of borrowing   and the loan amount, depending on borrower-specific characteristics, contract   terms and set of macrovariables. The decision-making process for borrowing can   be described as the sequence of decisions on: 1) choosing the credit program;    2) approving of a borrower; 3) choosing contract terms from a feasible set;    4) and loan performance. Following Philips and Yezer (1996) and Attanasio,    Goldberg and Kyriazidou (2008) the author proposes an econometric approach   that deals with endogeneity and self-selection of borrowers when estimating the    demand-for-loan equations and specifies the structure of data that is required for   implementation.

Language: English
Full text
Text on another site
Keywords: sample selectionэндогенностьendogeneitydemand for loansспрос на кредитывыборочная селективность
Publication based on the results of:
Полупараметрическое оценивание функции спроса на товар с эндогенными характеристиками (2014)

In book

Financial Econometrics and Empirical Market Microstructure
NY: Springer, 2015.
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