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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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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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Using the regulation of accuracy to study performance when the correct answer is not known

Scandinavian Journal of Psychology. 2017. Vol. 58. P. 275–283.
Luna K., Martín-Luengo B.

We examined memory performance in multiple-choice questions when correct answers were not always present. How do participants answer when they are aware that the correct alternative may not be present? To answer this question we allowed participants to decide on the number of alternatives in their final answer (the plurality option), and whether they wanted to report or withhold their answer (report option). We also studied the memory benefits when both the plurality and the report options were available. In two experiments participants watched a crime and then answered questions with five alternatives. Half of the questions were presented with the correct alternative and half were not. Participants selected one alternative and rated confidence, then selected three alternatives and again rated confidence, and finally indicated whether they preferred the answer with one or with three alternatives (plurality option). Lastly, they decided whether to report or withhold the answer (report option). Results showed that participants’ confidence in their selections was higher, that they chose more single answers, and that they preferred to report more often when the correct alternative was presented. We also attempted to classify a posteriori questions as either presented with or without the correct alternative from participants’ selection. Classification was better than chance, and encouraging, but the forensic application of the classification technique is still limited since there was a large percentage of responses that were incorrectly classified. Our results also showed that the memory benefits of both plurality and report options overlap. Key words: Metamemory, eyewitness memory, plurality option, report option.

Research target: Psychology
Language: English
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Text on another site
Keywords: metamemoryreport optionplurality option
Publication based on the results of:
Активное и пассивное декодирование нейрональных процессов при выполнении когнитивных и нейроэкономических задач (2017)
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