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News
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.
June 4, 2026
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
June 3, 2026
Pocket Money, Personal Interest, and Family Practices: What Shapes Students Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.

 

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How to use neural network and web technologies in modeling complex technical systems

.
Semenenko M. G., Kniazeva I. V., Beckel L. S., Rutskiy V., Tsarev R., Yamskikh T. N., Kartsan I. N.
Language: English
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Keywords: дифференциальные уравнениянейронные сетипроцесс обученияметоды прогнозированияneural networksdifferential equationslearning processMATLABcomplex technical systemsвизуальное моделированиеvisual modelingsimulinkОБЛАЧНЫЕ РЕШЕНИЯmethods of forecastingcloud-based solutionsimulation softwareкомлпексные технические системыпрограммная симуляцияМАТЛАБСимулинк

In book

IOP Conference Series: Materials Science and Engineering, Volume 537, Issue 3
IOP Conference Series: Materials Science and Engineering, Volume 537, Issue 3
Vol. 537. Issue 3. , Institute of Physics Publishing (IOP), 2019.
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