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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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Методика создания комплексной экономико-математической модели массовой оценки стоимости объектов недвижимости на примере квартирного рынка города Перми

Вестник Пермского университета. Серия: Экономика. 2016. № 2(29). С. 54–69.
Yasnitsky L., Ясницкий В. Л.

Currently, there are a number of economic and mathematical models designed for mass appraisal of real estate, tailored to their construction and performance properties, but taking no account of the evolving macroeconomic situation in the country and the world. The disadvantage of such static models is their rapid obsolescence, the need for constant updating, and unsuitability for medium-term forecasting. On the other hand, there are dynamic models that take into account the current macroeconomic situation; however, they are intended for predicting and studying the overall price situation in the real estate market, but not for mass appraisal of real estate with their variety of construction and performance properties. In this regard, the aim of this research is to develop methods of creating complex models having the properties of the static and dynamic models mentioned, i.e., taking into account construction and performance properties, as well as changing macroeconomic situation in the country and in the world. Methods and models are developed with the use of neural network technology basing on the example of the residential real estate in Perm and on statistical information of the market over the period from 2005 to 2015. In addition to its primary purpose – mass appraisal of apartments, the model is suitable for medium-term forecasting and identification of the real estate market regularities. For example, it has been found out that the price of Perm apartments as a whole tends to increase due to the rise of the oil prices, but the dependence of the cost of apartments on the price of oil is stable and direct only when the latter exceeds $ 60 – 80 per barrel. In case the volume of mortgage lending rises, the price of apartments in Perm will increase. However, the growth rate of the cost of elite four-room apartments will begin to slow down, with an increase in mortgage lending volumes above 2400 – 2700 million rubles, whereas this effect will not be apparent for cheap one- and two-bedroom apartments. Further housing construction in Perm to 1,400 sq m in the short term will not cause a noticeable change in residential property prices in Perm, which suggests that the market is still far from saturation.

Research target: Computer Science Economics and Management
Priority areas: economics state and public administration IT and mathematics
Language: Russian
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Keywords: рынок недвижимостинейронная сетьmass appraisalмассовая оценкаreal estate marketartificial neural networksmacroeconomic indicators экономическое прогнозированиеanalytical forecasting
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