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Subject
News
July 24, 2026
'Physics Is What the World Is Literally Built On'
Physicist Nina Dzhanayeva, recipient of a Vladimir Potanin Foundation scholarship, focuses her research on nanophotonics. In this interview for the HSE Young Scientists project, she discusses nanowells, scientific intuition, and how physics can help in making frangipane cream puffs.
July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.

 

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38th Conference on Neural Information Processing Systems (NeurIPS 2024)

2024.
Under the general editorship: A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, C. Zhang

Proceedings of the international conference "Neural Information Processing Systems 2024." (NeurIPS 2024)

Chapters
Challenges of Generating Structurally Diverse Graphs
Velikonivtsev F., Mironov M., Prokhorenkova L., , in: 38th Conference on Neural Information Processing Systems (NeurIPS 2024).: [б.и.], 2024. P. 57993–58022.
For many graph-related problems, it can be essential to have a set of structurally diverse graphs. For instance, such graphs can be used for testing graph algorithms or their neural approximations. However, to the best of our knowledge, the problem of generating structurally diverse graphs has not been explored in the literature. In this paper, ...
Added: October 15, 2024
EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas
Mozikov M., Severin N., Bodishtianu V. et al., , in: 38th Conference on Neural Information Processing Systems (NeurIPS 2024).: [б.и.], 2024. P. 13927–13981.
Added: November 22, 2024
Group and Shuffle: Efficient Structured Orthogonal Parametrization
Gorbunov M., Yudin N., Soboleva V. et al., , in: 38th Conference on Neural Information Processing Systems (NeurIPS 2024).: [б.и.], 2024. P. 68713–68739.
Added: November 26, 2024
Interaction-Force Transport Gradient Flows
Gladin E., Dvurechensky P., Mielke A. et al., , in: 38th Conference on Neural Information Processing Systems (NeurIPS 2024).: [б.и.], 2024. P. 14484–14508.
Added: November 28, 2024
TabGraphs: A Benchmark and Strong Baselines for Learning on Graphs with Tabular Node Features
Bazhenov G., Platonov O., Prokhorenkova L., , in: 38th Conference on Neural Information Processing Systems (NeurIPS 2024).: [б.и.], 2024. P. 1–26.
Tabular machine learning is an important field for industry and science. In this f ield, table rows are typically treated as independent data samples, but additional information about the relations between these samples is sometimes available and can be used to improve predictive performance. Such information can be naturally modeled with a graph, hence tabular ...
Added: December 17, 2024
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
Samsonov S., Moulines E., Shao Q. et al., , in: 38th Conference on Neural Information Processing Systems (NeurIPS 2024).: [б.и.], 2024. P. 12408–12460.
In this paper, we obtain the Berry–Esseen bound for multivariate normal approximation for the Polyak-Ruppert averaged iterates of the linear stochastic approximation (LSA) algorithm with decreasing step size. Our findings reveal that the fastest rate of normal approximation is achieved when setting the most aggressive step size αk ≍ k −1/2 . Moreover, we prove ...
Added: February 7, 2025
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
Mangold P., Samsonov S., Labbi S. et al., , in: 38th Conference on Neural Information Processing Systems (NeurIPS 2024).: [б.и.], 2024. Ch. 37 P. 13927–13981.
In this paper, we analyze the sample and communication complexity of the federated linear stochastic approximation (FedLSA) algorithm. We explicitly quantify the effects of local training with agent heterogeneity. We show that the communication complexity of FedLSA scales polynomially with the inverse of the desired accuracy ϵ. To overcome this, we propose SCAFFLSA a new ...
Added: February 11, 2025
HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach
Maxim Nikolaev, Mikhail Kuznetsov, Dmitry P. Vetrov et al., , in: 38th Conference on Neural Information Processing Systems (NeurIPS 2024).: [б.и.], 2024. P. 45600–45635.
Added: February 17, 2025
Where Do Large Learning Rates Lead Us?
Sadrtdinov I., Kodryan M., Pokonechny E. et al., , in: 38th Conference on Neural Information Processing Systems (NeurIPS 2024).: [б.и.], 2024. P. 58445–58479.
Added: February 19, 2025
Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
Agafonov A., Petr Ostroukhov, Mozhaev R. et al., , in: 38th Conference on Neural Information Processing Systems (NeurIPS 2024).: [б.и.], 2024. P. 115816–115860.
Variational inequalities represent a broad class of problems, including minimization and min-max problems, commonly found in machine learning. Existing second-order and high-order methods for variational inequalities require precise computation of derivatives, often resulting in prohibitively high iteration costs. In this work, we study the impact of Jacobian inaccuracy on second-order methods. For the smooth and ...
Added: July 15, 2025
Lower bounds and optimal algorithms for non-smooth convex decentralized optimization over time-varying networks
Kovalev D., Ekaterina Borodich, Alexander Gasnikov et al., , in: 38th Conference on Neural Information Processing Systems (NeurIPS 2024).: [б.и.], 2024. P. 96566–96606.
We consider the task of minimizing the sum of smooth and strongly convex functions stored in a decentralized manner across the nodes of a communication network whose links are allowed to change in time. We solve two fundamental problems for this task. First, we establish {\em the first lower bounds} on the number of decentralized ...
Added: November 18, 2025
Invertible Consistency Distillation for Text-Guided Image Editing in Around 7 Steps
Nikita Starodubcev, Mikhail Khoroshikh, Babenko A. et al., , in: 38th Conference on Neural Information Processing Systems (NeurIPS 2024).: [б.и.], 2024. P. 12496–12527.
Diffusion distillation represents a highly promising direction for achieving faithful text-to-image generation in a few sampling steps. However, despite recent successes, existing distilled models still do not provide the full spectrum of diffusion abilities, such as real image inversion, which enables many precise image manipulation methods. This work aims to enrich distilled text-to-image diffusion models ...
Added: February 17, 2026
Research target: Computer Science
Language: English
Text on another site
Keywords: machine learning
38th Conference on Neural Information Processing Systems (NeurIPS 2024)
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Local Fault-Tolerant Routing in 3D Mesh NoCs using Single-Hop Rollback
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This work presents a hierarchy of strictly local fault-tolerant routing algorithms for 3D mesh networks-on-chip, culminating in an algorithm that combines a live-neighbor selection rule with a bounded single-hop rollback mechanism. The proposed algorithms operate exclusively on immediate neighbor information, maintain O(1) per hop complexity, and require no global topology knowledge, additional virtual channels, or ...
Added: July 23, 2026
Библиометрия фольклора: русские пословицы в научных журналах
Pislyakov V., Вестник Томского государственного университета. Филология 2026 № 101 С. 175–192
This article examines the use of proverbs in academic texts—specifically, articles published in Russian research journals. For the experiment, ten proverbs were selected as the intersection of two fundamentally different paremiological surveys aimed at compiling lists of popular or common Russian proverbs. One of these surveys was conducted by the classic of paremiology, G.L. Permyakov, ...
Added: July 22, 2026
Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants
Korogod D., Shapeev A., Novikov I., Physical Review B: Condensed Matter and Materials Physics 2026 Vol. 114 No. 2 Article 024104
We present two models with explicit long-range electrostatics in the form of Coulomb interactions. Both models include point charges depending on their local atomic environments, and the second model also conserves a total charge of an atomic system. We combine the proposed long-range models with the local moment tensor potential (MTP) and demonstrate that they ...
Added: July 22, 2026
Global optimization of atomic clusters via physically constrained tensor train decomposition
Sozykin K., Rybin N., Chertkov A. et al., Physical Review B: Condensed Matter and Materials Physics 2026 Vol. 113 No. 22 Article 224111
The global optimization of atomic clusters represents a fundamental challenge in computational chemistry and materials science due to the exponential growth of local minima with system size (i.e., the curse of dimensionality). We introduce a framework that overcomes this limitation by exploiting the low-rank structure of potential energy surfaces through tensor train (TT) decomposition. Our ...
Added: July 22, 2026
WSI-GT: Pseudo-Label Guided Graph Transformer for Whole-Slide Histology
Михайлов И. А., Machine Learning and Knowledge Extraction 2026 Vol. 8 No. 1 Article 8
Whole-slide histology images (WSIs) can exceed 100 k × 100 k pixels, making direct pixel-level segmentation infeasible and requiring patch-level classification as a practical alternative for downstream WSI segmentation. However, most approaches either treat patches independently, ignoring spatial and biological context, or rely on deep graph models prone to oversmoothing and loss of local tissue ...
Added: July 16, 2026
On the construction of Barnes–Wall lattices and their application in cryptography
Kuninets A., Malygina E., Leevik A. G. et al., Journal of Computer Virology and Hacking Techniques 2026 No. 22 Article 62
In this work, we investigate the application of Barnes–Wall lattices in post-quantum cryptographic schemes. We survey and analyze several constructions of Barnes–Wall lattices, including subgroup chains, the generalized k-ing construction, and connections with Reed-Muller codes, highlighting their equivalence over both Z[i] and Z. Building on these structural insights, we introduce a new algorithm for efficient ...
Added: July 16, 2026
Tencent и Open Source. Как относится к открытому ПО самый дорогой бренд Китая?
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В предыдущей статье про Open Source в КНР [1] мы рассказали про Alibaba – крупную корпорацию, занимающую тридцатое место в рейтинге самых значимых мировых брэндов за 2025 год [2]. Место почетное, но не первое среди китайских компаний – на тринадцатом месте расположилась Tencent, разработчик WeChat и ряда других продуктов, широко используемых нашими восточными соседями. Tencent ...
Added: July 14, 2026
2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
IEEE, 2026.
Added: July 13, 2026
Mathematical Optimization Theory and Operations Research, 25th International Conference, MOTOR 2026 Irkutsk, Russia, July 6–11, 2026 Proceedings
Switzerland: Springer, 2026.
This volume contains the refereed proceedings of the 25th International Conference on Mathematical Optimization Theory and Operations Research (MOTOR 2026) 1 held during July 6–11 in a picturesque place near Lake Baikal, Irkutsk, Russia. The MOTOR conference is a direct successor and scientific inheritor of several prominent events on mathematical programming, combinatorial and stochastic optimization, ...
Added: July 12, 2026
Задачи бесконечной регулярной реализуемости
Шиманогов И. Н., Vyalyi M., Дискретный анализ и исследование операций 2025 Т. 32 № 4(166) С. 213–230
A well-studied class of algorithmic problems is that of regular realizability: checking the non-emptiness of the intersection of a regular language with a given language. This problem has a natural algebraic interpretation: verifying whether an element of a Boolean algebra belongs to the kernel of a certain homomorphism. This motivates the consideration of an analogous ...
Added: July 12, 2026
Improving Differential Equation Solving in Compact Language Models via Activation Steering and Reinforcement Learning
Surkov A., Ignatenko V., Koltcov Sergei, Computers, Materials and Continua 2026
Large language models have recently demonstrated promising capabilities in mathematical reasoning; however, their performance on tasks requiring strict symbolic manipulation, such as solving differential equations, remains limited, especially for compact models. In this work, we investigate whether activation steering combined with reinforcement learning can improve the quality of solutions generated by pretrained language models without ...
Added: July 8, 2026
Computational Science and Its Applications – ICCSA 2026 Workshops
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The series Lecture Notes in Computer Science (LNCS), including its subseries Lecture Notes in Artificial Intelligence (LNAI) and Lecture Notes in Bioinformatics (LNBI), has established itself as a medium for the publication of new developments in computer science and information technology research, teaching, and education. LNCS enjoys close cooperation with the computer science R & ...
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Моделирование специализированных алгоритмов маршрутизации в сетях на кристалле, представленных сериями семейств циркулянтных топологий
Маликов М. А., Монахова Э. А., Rzaev E. et al., Ученые записки Казанского университета. Серия: Физико-математические науки 2026 Т. 168 № 2 С. 269–286
This article examines series of families of two-dimensional circulant networks with rectangular L -shapes, optimal in diameter, as network-on-chip topologies with a minimal number of crossings between the links and a bounded length of the maximum link that does not depend on the network size. New network-on-chip routing algorithms, which use the coordinates of three adjacent zeros in the ...
Added: July 8, 2026
Algorithmic overlaps as thermodynamic variables: From local to cluster Monte Carlo dynamics in critical phenomena
Pilé I., Deng Y., Shchur L., Physical Review B: Condensed Matter and Materials Physics 2026 Vol. 114 No. 1 Article 014101
We investigate the spatial overlap of successive spin configurations in Markov chain Monte Carlo simulations using the local Metropolis algorithm and the Swendsen-Wang and Wolff cluster algorithms. We examine the dynamics of these algorithms for models in different universality classes: Ising model, Potts model with three components, and four-state Potts model. The overlap of two ...
Added: July 6, 2026
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Added: July 3, 2026
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Introduction: In many communication systems under construction and those to be created power control and channel estimation techniques developed for the previous generation communication systems fail to provide desired precision. One way to solve this problem is to use order-statistics-based reception techniques that do not need channel estimation or power control. To ensure the desired ...
Added: July 3, 2026
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Added: April 13, 2026
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