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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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Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning

Plos One. 2020. Vol. 15. No. 12. P. 1–15.
Petrovskaya A., Kirillov B., Asmolova A., Galli G., Feurra M., Medvedeva A.

We aimed to replicate a published effect of transcranial direct-current stimulation (tDCS) -induced recognition enhancement over the human ventrolateral prefrontal cortex [1] and analyze the data with machine learning. We investigated effects over an adjacent region, the dorsolateral PFC. We found weak or absent effects over the VLPFC and DLPFC. We conducted machine learning studies to examine the effects of semantic and phonetic features on memorization, which revealed no effect of VLPFC tDCS on the original dataset or the current data. The highest contributing factor to memory performance was individual differences in memory not explained by word features, tDCS group, or sample size, while semantic, phonetic, and orthographic word characteristics did not contribute significantly. To our knowledge,We aimed to replicate a published effect of transcranial direct-current stimulation (tDCS) - induced recognition enhancement over the human ventrolateral prefrontal cortex (VLPFC) and analyze the data with machine learning. We investigated effects over an adjacent region, the dorsolateral prefrontal cortex (DLPFC). In total, we analyzed data from 97 participants after exclusions. We found weak or absent effects over the VLPFC and DLPFC. We conducted machine learning studies to examine the effects of semantic and phonetic features on memorization, which revealed no effect of VLPFC tDCS on the original dataset or the current data. The highest contributing factor to memory performance was individual differencesin memory not explained by word features, tDCS group, or sample size, while
semantic, phonetic, and orthographic word characteristics did not contribute significantly. To our knowledge, this is the first tDCS study to investigate cognitive effects with machine learning, and future studies may benefit from studying physiological as well as cognitive effects with data-driven approaches and computational models.

Research target: Medical Technologies
Priority areas: humanitarian
Language: English
DOI
Text on another site
Keywords: machine learningtranscranial direct current stimulation episodic memory
Publication based on the results of:
Multidisciplinary investigation of neurocognitive brain processes using neurophysiological, behavioral and structural approaches (2020)
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