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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.
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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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The Modified Algorithm of Viterbi Convolutional Decoding

P. 111–119.
Nikitin O., Polushin P., Saleh H.

The modification of algorithm of Viterbi convolutional decoding for the fading channels and use of interleaving of symbols is described. The modification represents the use of additional correcting coefficients in the process of calculation of metrics of various parts in the trellis diagram. It gives opportunity to reduce the probability of errors of decoded symbols.

Language: English
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Keywords: Viterbi AlgorithmConvolutional DecodingInterleaving of Symbols

In book

Actual Problems of System and Software Engineering 2017. Proceedings of the 5th International Conference on Actual Problems of System and Software Engineering Supported by Russian Foundation for Basic Research. Project #17-07-20565 Moscow, Russia, November 14-16, 2017, 408 P.
Vol. 1989. , Aachen: CEUR Workshop Proceedings, 2017.
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