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  • Деревья решений для классификации демографических последовательностей
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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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Деревья решений для классификации демографических последовательностей

С. 1–13.
Muratova A., Ignatov D. I.
Language: Russian
Full text
Text on another site
Keywords: анализ демографических последовательностейdemographic sequence mining
Publication based on the results of:
Разработка и апробация методик анализа демографических последовательностей (2016)

In book

Программа секций XVIII Апрельской конференции. Сессия P-04. Исследование демографических последовательностей
НИУ ВШЭ, 2017.
Similar publications
Поиск закономерностей в индивидуальных демографических траекториях
Gizdatullin D., Ignatov D. I., Baixeries J., В кн.: Программа секций XVIII Апрельской конференции. Сессия P-04. Исследование демографических последовательностей.: НИУ ВШЭ, 2017. С. 1–11.
В данной работе представлены результаты применения узорных структур (pattern structures) и “контрастных” закономер- ностей (emerging patterns) в анализе демографических последова- тельностей для данных по России. Панельные данные Российской части исследования GGS (Generation and Gender Survey) на основе трех волн опроса в 2004, 2007, и 2011 описывают 11 поколений ре- спондентов, начиная с 1930 по 1984. ...
Added: February 9, 2020
Программа секций XVIII Апрельской конференции. Сессия P-04. Исследование демографических последовательностей
НИУ ВШЭ, 2017.
Added: February 9, 2020
Learning Interpretable Prefix-Based Patterns from Demographic Sequences
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There are many different methods for computing relevant patterns in sequential data and interpreting the results. In this paper, we compute emerging patterns (EP) in demographic sequences using sequence-based pattern structures, along with different algorithmic solutions. The purpose of this method is to meet the following domain requirement: the obtained patterns must be (closed) frequent contiguous prefixes of the input sequences. ...
Added: February 9, 2020
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