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Subject
News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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NeurIPS 2024 Optimization for ML Workshop

2025.
Chapters
Lion's sign noise can make training more stable
Elistratov S., Podivilov A., Iuzhakov T. et al., , in: NeurIPS 2024 Optimization for ML Workshop.: [б.и.], 2025.
Lion is a novel optimization method that has outperformed traditional optimizers like Adam across a variety of tasks. Despite its empirical success, the reasons behind Lion's superiority remain unclear. In this paper, we investigate the mechanisms contributing to Lion's enhanced performance, focusing on the structured noise introduced by the use of the sign function in ...
Added: February 5, 2026
Language: English
Text on another site
Keywords: optimization machine learning
NeurIPS 2024 Optimization for ML Workshop
Similar publications
От неизвестности к прозрачности: обзор технологий объяснимого ИИ (XAI)
Avdoshin S. M., Pesotskaya E. Y., Информационные технологии 2026 Т. 32 № 4 С. 185–194
With the rapid advancement of artificial intelligence, and deep learning in particular, models have emerged that are capable of delivering highly accurate predictions. However, the internal logic of such models remains difficult to interpret—an issue of critical importance, especially in domains where the correctness of an algorithm directly affects high-stakes decision-making. One promising avenue for ...
Added: May 8, 2026
Explainable AI for Industry 5.0: Shedding light on the black box
Avdoshin S. M., Pesotskaya E. Y., Business Informatics 2026 Vol. 20 No. 1 P. 7–28
The rapid development of artificial intelligence (AI) is accompanied by increasing computational complexity and decreasing model transparency, which significantly limits its adoption in critical domains that require a high level of trust, interpretability, and justification of decisions. Under these conditions, the field of Explainable Artificial Intelligence (XAI) has gained particular importance as it focuses on approaches and technologies that ...
Added: May 8, 2026
Алгоритм анализа новостной информации для принятия экономических решений
Чудинова О. С., Первицкая Л. А., Ramenskaya A., Индустриальная экономика 2026 № 1 С. 65–78
This article is devoted to the development of an algorithm for analyzing news information using machine learning methods implemented in Python libraries. The choice of tools used at each stage of the algorithm is justified by calculating metrics for the quality of the solution to the corresponding machine learning problems. The algorithm’s results are presented ...
Added: April 20, 2026
Modeling cosolvent effects on solubility in supercritical CO2 using data-driven approaches
Makarov D. M., Kalikin N., Gurikov P. et al., Journal of Supercritical Fluids 2026 Vol. 235 Article 106979
Supercritical CO2 (scCO2 ) is an environmentally friendly solvent, but its low polarity limits the solubility of polar compounds. Cosolvents are commonly used to enhance solvation capability, yet comprehensive datadriven studies are scarce. We compiled the largest dataset to date — 4401 experimental solubility records with 22 cosolvents for 93 nonionic solutes, plus 4855 records ...
Added: April 19, 2026
Эффективность применения прогнозов волатильности в активных торговых стратегиях институциональных инвесторов на российском рынке акций
Lysenok N., Фундаментальная и прикладная математика 2026 Т. 26 № 3 С. 33–42
This study examines the impact of realized volatility forecasts on the performance of active trading strategies in the Russian equity market. Using a sample of 17 liquid stocks over the period 2014–2026, a hybrid forecasting model is developed that combines HAR-J with gradient boosting; its superiority over the baseline HAR-J specification is confirmed by the ...
Added: April 17, 2026
Efficiency of Machine Learning Tasks on HPC Devices
Efremov A., Timofeev A., Ilyasov Y. et al., , in: ПАРАЛЛЕЛЬНЫЕ ВЫЧИСЛИТЕЛЬНЫЕ ТЕХНОЛОГИИ (ПаВТ’2025).: Издательский центр Южно-Уральского государственного университета, 2025. P. 56–81.
Accurate benchmarking is critical for selecting computing architectures optimized for machine learning (ML) tasks. Conventional benchmarks such as High-Performance Linpack (HPL) and High Performance Conjugate Gradients (HPCG) often fail to capture the diversity and complexity of modern ML workloads. This study investigates the correlation between hardware parameters (e.g., processor architecture, cache size, frequency) and ML ...
Added: April 4, 2026
Использование машинного обучения и классических статистических методов для построения скоринговой модели в автостраховании
Mironkina Y., Тимофеев Д. И., В кн.: Математическое и компьютерное моделирование в экономике, страховании и управлении рисками: сборник статей. Выпуск 10. Материалы XIV Научно-практической конференции. Саратов, 20–22 ноября 2025 г.Вып. 10.: Саратов: Саратовский университет, 2025. С. 51–58.
In the field of auto insurance, one of the most pressing challenges is the problem of financial losses arising from the misclassification of clients in terms of the potential unprofitability of their insurance contracts. This study is devoted to the development of a scoring system based on machine learning and classical statistical methods, using portfolio data from a ...
Added: March 31, 2026
Определение фолликулярного резерва яичников по данным ультразвукового исследования на основе методов машинного обучения
Moshkin A., Лапутин Ф. А., Сидоров И. В., DIGITAL DIAGNOSTICS 2024 Т. 5 № S1 С. 40–42
BACKGROUND: Ovarian reserve reflects a woman's ability to successfully realize reproductive function. The assessment of ovarian reserve is an urgent task for clinical practice [1] and is important in scientific research. The use of computerized diagnostic image processing methods can accelerate and facilitate the performance of routine tasks in clinical practice. Their use in retrospective ...
Added: February 21, 2026
The Fourteenth International Conference on Learning Representations (ICLR 2026)
ICLR, 2026.
The Fourteenth International Conference on Learning Representations ...
Added: February 16, 2026
Как прогнозировать дефолты банков: эволюция методов, моделей и факторов риска
Shchepeleva M., Столбов М. И., Экономика и математические методы 2026 Т. 62 № 1 С. 63–77
Predicting bank defaults is an important task for the entire economy. Early identification of troubled banks helps to prevent impending bank failures or minimize the losses associated with them. The paper discusses the state of the art of instrumental methods and data used for this purpose. The theoretical background, the evolution of methodological approaches used ...
Added: February 13, 2026
Development of a Language Model for Automated Classification of English-Language Scientific Articles by SRSTI Codes
V. V. Zunin, A. I. Afonin, V. I. Anoshin et al., Automatic Documentation and Mathematical Linguistics 2025 Vol. 59 No. 5 P. 287–293
The development of an artificial intelligence-based language model for classifying English-language scientific articles by SRSTI codes is described. This improves the processes of reviewing and indexing scientific publications. A pre-processed dataset of scientific articles was used for training and testing the models. An architecture for cascade classification was developed, and the performance of models with ...
Added: February 11, 2026
Automatic detection of dyslexia based on eye movements during reading in Russian
Laurinavichyute A., Lopukhina A., Reich D., , in: Proceedings of the 63rd Annual Meeting of the Association for Computational LinguisticsVol. 2: Short papers.: Wien: Association for Computational Linguistics, 2025. P. 59–66.
Dyslexia, a common learning disability, requires an early diagnosis. However, current screening tests are very time- and resourceconsuming. We present an LSTM that aims to automatically classify dyslexia based on eye movements recorded during natural reading combined with basic demographic information and linguistic features. The proposed model reaches an AUC of 0.93 and outperforms the ...
Added: January 19, 2026
Artificial Intelligence for Urban Planning and Building Smart Cities
Demekhina A., Milshina Y., , in: Artificial Intelligence Enabled Real Time Environmental Monitoring.: Springer, 2026. P. 253–281.
Added: January 13, 2026
Parallel Processing and Applied Mathematics. 15th International Conference, PPAM 2024, Ostrava, Czech Republic, September 8–11, 2024, Revised Selected Papers, Part I
Springer, 2025.
This book constitutes the refereed proceedings of the 15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024, held in Ostrava, Czech Republic, during September 8–11, 2024. The 75 full papers included in this book were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: Part I : Numerical ...
Added: December 26, 2025
Применение нейросетевого подхода к построению современного образовательного процесса в вузе
Бояров Е. Н., Абрамова С. В., Купцова О. В. et al., Педагогика. Вопросы теории и практики 2025 Т. 10 № 5 С. 588–599
The aim of this research is to theoretically substantiate the possibility of applying a neural network approach to constructing a modern educational process in higher education. The article examines the main machine learning methods used for analyzing educational data and justifies the necessity of their implementation into the educational process. Particular attention is paid to ...
Added: December 9, 2025
Method of Automated Dataset Collection for Microwave Filters Synthesis
Arinin O. V., Bakhmach D. M., Katsnelson A. et al., , in: 2025 Systems of Signals Generating and Processing in the Field of on Board Communications.: IEEE, 2025. P. 1–5.
This research discusses the method of dataset collection automatization for microwave filter synthesis by integrating machine learning techniques, thus reducing development time. Utilizing the 3D electromagnetic analysis software package, the study involves simulation and collecting geometric parameters and amplitude-frequency characteristics from three variants of passband highly selective microstrip tworesonator combined filters with stepped impedance resonators. ...
Added: December 6, 2025
Смежные права на результаты интеллектуальной деятельности, созданные искусственным интеллектом: философско-правовой анализ замены критерия творчества на критерий инвестиций
Pakshin P., Актуальные проблемы российского права 2025 Т. 20 № 11 С. 11–18
The paper substantiates the necessity of providing legal protection for the results of intellectual works created by artificial intelligence through the mechanism of related rights. It examines ways to reduce legal risks associated with the creation of intellectual property using artificial intelligence technologies and offers a philosophical and legal analysis of the proposed hypothesis, namely, ...
Added: November 27, 2025
Determining the boundary of dynamical chaos in the generalized Chirikov map via machine learning
Chernyshov D., Satanin A., Shchur L., / Series arXiv "math". 2025.
We investigate the boundary separating regular and chaotic dynamics in the generalized Chirikov map, an extension of the standard map with phase-shifted secondary kicks. Lyapunov maps were computed across the parameter space (K,K(α, τ)) and used to train a convolutional neural network (ResNet18) for binary classification of dynamical regimes. The model reproduces the known critical ...
Added: November 21, 2025
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