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News
October 6, 2026
International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.
October 5, 2026
‘The Climate Transition Is Not Necessarily a Limitation for Business
Linara Khadimullina works in the field of low-carbon development. In an interview with the Young Scientists of HSE project, she spoke about why nature is not just a beautiful backdrop, her research on the role of sustainable corporate governance in reducing greenhouse gas emissions, and growing plants as a source of inspiration.
October 5, 2026
Africa, Youth, and Civic Dialogue: Public Diplomacy Discussed at HSE University
In late September, HSE University hosted a roundtable discussion titled Civil Society in African Countries and Youth Participation in Public Diplomacy. Representatives of non-governmental organisations from Ghana, Ethiopia, and Russia, along with students from HSE University’s Bachelor’s Programme in Public Administration, discussed how young people without official diplomatic status can influence relations between countries and how the nonprofit sector can remain sustainable amid declining grant funding.

 

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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
The Transport Coding Model for Delay Reduction in Communication Networks using OMNET++ toolkit
Petrovanov I., Sergeev A., Krouk E., , in: 2025 XIХ International Symposium on Problems of Redundancy in Information and Control Systems (Redundancy), 5-7 Nov. 2025.: IEEE, 2025. P. 1–7.
Transport coding is a promising method for reducing message delay in packet-switched networks by adding controlled redundancy at the transport layer. Classical analytical works show that encoding k original packets into n coded packets and reconstructing the message after the first k successful deliveries effectively shifts the latency metric from the maximum to the k-th ...
Added: September 29, 2026
Metaheuristics in Machine Learning: Theory and Applications
Cham: Springer, 2021.
Added: September 18, 2026
Data Analysis and Optimization for Engineering and Computing Problems
Cham: Springer, 2020.
Added: September 18, 2026
Modelos y técnicas de optimización aplicados a problemas de transporte
Sánchez Ansola E., Rosete Suárez A., S. González L. et al., Anales de la Academia de Ciencias de Cuba 2024 Vol. 14 No. 2 Article e1596
Added: September 18, 2026
The relevance of lead prioritization: a B2B lead scoring model based on machine learning
González-Flores L., Rubiano-Moreno J., Sosa Gómez G., Frontiers in Artificial Intelligence 2025 Vol. 8 Article 1554325
Added: September 18, 2026
Использование методов машинного обучения для повышения эффективности систем противодействия многоэтапных кибератак
Lebedev O. B., Левченко Д. Д., Черкасов Р. И., Инженерный вестник Дона 2026 № 2(134) Статья 7
This article analyzes the impact of artificial intelligence (AI) and machine learning technologies on the development and transformation of cyberthreats and the creation of highly effective cyberdefense systems. Key trends in AI evolution are discussed, including data-, model-, application-, and human-centric approaches, and their role in shaping both defensive and offensive capabilities. It is shown ...
Added: September 12, 2026
Модель глубокого обучения для автоматизированной интерпретации медицинских электрофизиологических данных
Lebedev O. B., Шмелева А. Г., Гежа Н. С., Информатика и автоматизация (Труды СПИИРАН) 2026 Т. 25 № 3 С. 720–750
This paper describes the development of a neural network model for automated analysis of medical data in electrophysiology based on deep learning methods. The relevance of this work stems from the growing need to improve the objectivity, speed, and accuracy of processing complex spatiotemporal signals, such as ECG or EEG. Convolutional neural networks (CNNs), which ...
Added: September 10, 2026
Анализ согласованности голосования стран ЕАЭС и ОДКБ в ГА ООН с помощью иерархической кластеризации
Вохминцев И. В., Вестник международных организаций: образование, наука, новая экономика 2026 Т. 21 № 2
The EAEU and the CSTO are Russia’s principal regional international organisations. Understanding, assessing, and analysing the foreign-policy positions of the countries that belong to them is a matter of the state’s national interests. This determines the purpose of the study: to identify the level and the form of cohesion in the voting of EAEU and ...
Added: September 7, 2026
Pupillometry and autonomic nervous system responses to cognitive load and false feedback: an unsupervised machine learning approach
Alshanskaia E., Portnova G., Liaukovich K. et al., Frontiers in Neuroscience 2024 Vol. 18
Added: September 7, 2026
Is social media news more subjective?😱 A comparative study of British quality and popular news sources’ adaptation to Facebook
Savinova E., Hoek J., Digital Journalism 2025 Vol. 13 No. 7 P. 1291–1310
Added: September 3, 2026
Volatility Forecasting From Econometrics To Artificial Intelligence: A Four-Stage Review Of The Evidence Pipeline From Forecast Accuracy To Portfolio Performance
Nikita I. Lysenok, International Journal of Computer Information Systems and Industrial Management Applications 2026 Vol. 18 No. 18s P. 1406–1430
A volatility forecast becomes useful only after it has passed through four stages: a model produces it, a trading rule consumes it, a portfolio aggregates the result, and an investor evaluates that result against a utility function. Each stage has its own mature evaluation apparatus, and each is studied in isolation. This review traces the ...
Added: August 23, 2026
К ранжированию значимости факторов дестабилизации в странах Азии и Африки методами машинного обучения
Korotayev A., Chernomorchenko I., Медведев И. А., Восток. Афро-азиатские общества: история и современность 2026 № 3 С. 117–130
This study employs machine learning methods to rank factors contributing to large-scale armed and unarmed destabilization across Asian and African countries. Analysis reveals that African nations demonstrate greater vulnerability to armed destabilization (up to full-scale civil wars), whereas Asian countries are more prone to less violent unarmed forms (mass antigovernment demonstrations, riots, general strikes and ...
Added: June 21, 2026
Artificial intelligence and digital twins for failure prediction in data center cooling systems: a comprehensive literature review (2018–2026)
Butorova A., Bobakov V., Sergeev A. et al., European Physical Journal: Special Topics 2026 P. 1–19
This paper presents a review of artificial intelligence (AI) methods for failure prediction in data center cooling systems, with a focus on the integration of digital twins (DTs), physics-informed learning, and graph-based models. Positioned within complex network science, this review addresses a limitation of conventional graph approaches—their reliance on pairwise connectivity—whereas real-world failures often arise ...
Added: June 10, 2026
Proceedings of the 43rd International Conference on Machine Learning (ICML 2026)
Seul: PMLR, 2026.
Added: June 4, 2026
Towards the Ranking of the Importance of Revolutionary Destabilization Factors in Asian and African Countries Using Machine Learning Methods
Chernomorchenko I., Ilya Medvedev, Korotayev A., Cross-Cultural Research 2026 Vol. 60 No. 4 P. 375–423
This study investigates which structural factors most strongly predict armed and unarmed revolutionary destabilization across Sub-Saharan Africa (SSA), the Middle East and North Africa (MENA), as well as Asia using country–year data for 1950–2022 and a set of economic, demographic, political, and climatic indicators. It employs an interpretable machine learning framework (CatBoost with SHAP values ...
Added: June 1, 2026
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 – XIX всероссийская конференция с международным участием, ПаВТ'2025, г. Москва, 8–10 апреля 2025 г. Короткие статьи и описания плакатов.: Челябинск: Издательский центр ЮУрГУ, 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
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