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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
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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.
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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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SK-languages as a Powerful and Flexible Semantic Formalism for the Systems of Cross-Lingual Intelligent Information Access

Informatica. An International Journal of Computing and Informatics. 2017. Vol. 41. No. 2. P. 221–232.
Fomichov V. A.

The first starting point of this paper is the broadly accepted idea of employing, as a promising methodology, an artificial semantic language-intermediary for the realization of automatic cross-lingual intelligent information access to natural language (NL) texts on the Web. The second one is the emergence in computational semantics during 2013-2016 of great interest in the semantic formalism (more exactly, notation) called Abstract Meaning Representation (AMR). This formalism was introduced in 2013 in an ACL publication by a group consisting of ten researchers from UK and USA. This paper shows that much broader prospects for creating semantic languages-intermediaries in comparison with AMR are opened by the theory of K-representations (TKR), developed by V. A. Fomichov. The basic mathematical model of TKR describes the regularities of NL structured meanings. The mathematical essence is that this model introduces a system consisting of ten partial operations on conceptual structures. Initial version of this model was published in 1996 in Informatica (Slovenia). The second version of the model (stated in a monograph released by Springer in 2010) defines a class of formal languages called SK-languages (standard knowledge languages). It is demonstrated that SK-languages allow us to simulate all expressive mechanisms of AMR. The advantages in comparison with AMR are, in particular, the possibilities to construct semantic representations of compound infinitive constructions (expressing goals, commitments, etc), of compound descriptions of notions and sets, and of complex discourses and knowledge pieces.

Research target: Computer Science Philology and Linguistics
Priority areas: humanitarian IT and mathematics
Language: English
Full text
Keywords: теория К-представленийtheory of K-representationssemantic parsingabstract meaning representationformal representation of semantic contentсемантический парсингабстрактное представление смысловформальное представление семантического содержания
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