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  • ОЦЕНКА ФАКТОРОВ, ВЛИЯЮЩИХ НА НАЛОГОВЫЕ ПОСТУПЛЕНИЯ В БЮДЖЕТ РЕГИОНОВ
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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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?

ОЦЕНКА ФАКТОРОВ, ВЛИЯЮЩИХ НА НАЛОГОВЫЕ ПОСТУПЛЕНИЯ В БЮДЖЕТ РЕГИОНОВ

Вестник Московского университета. Серия 6: Экономика. 2021. № 5. С. 136–154.
Lavrenchuk E. N., Kirpishchikov D.

The purpose of this article is to identify and estimation of factors affecting tax revenues to the Russian’s regional budgets. The following factors were highlighted: the gross regional product, the average wage level, the inflation rate, the share of unprofitable organizations in the region, the level of employment, the share of the agricultural sector, investment in fixed assets and the share of exports. Also, the model included information on the areas of advanced socio-economic development in the region. The study was conducted using open data for 83 regions of the Russian Federation from 2010 to 2017, the total of 664 observations. Through the least-squares estimate of a model with fixed effects, a significant impact of the studied parameters was established. Thus, in this paper, common features for all regions are established in the context of the analyzed indicators’ impact. The results of this study may be used in forecasting the revenue of regional budgets.

Research target: Economics and Management
Language: Russian
Full text
Keywords: налогиtaxestax revenuesрегиональный бюджетregional budgetsналоговые доходы
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