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Reinforcement Procedure for Randomized Machine Learning
Mathematics. 2023. Vol. 11. No. 17. Article 3651.
This paper is devoted to problem-oriented reinforcement methods for the numerical implementation of Randomized Machine Learning. We have developed a scheme of the reinforcement procedure based on the agent approach and Bellman’s optimality principle. This procedure ensures strictly monotonic properties of a sequence of local records in the iterative computational procedure of the learning process. The dependences of the dimensions of the neighborhood of the global minimum and the probability of its achievement on the parameters of the algorithm are determined. The convergence of the algorithm with the indicated probability to the neighborhood of the global minimum is proved.
Springer, 2026.
Two volumes of the SPECOM 2026 proceedings contain a collection of submitted papers presented at SPECOM 2026, which were thoroughly reviewed by members of the Program Committee and additional reviewers consisting of almost 80 experts in the conference topic areas. In total, 65 regular full papers out of 99 submissions made via the EasyChair electronic ...
Added: September 20, 2026
Medvedev V., Annals of Global Analysis and Geometry 2026 Vol. 70 No. 2 P. 8–23
This paper studies three-dimensional compact static manifolds with boundary and positive scalar curvature. We prove that, under a suitable bound on the Ricci curvature, the orientable quotient of the Nariai static manifold with boundary is the only such manifold with connected boundary, provided that the zero-level set of the potential is connected and does not intersect ...
Added: September 19, 2026
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Pattern Recognition and Image Analysis 2026 Vol. 36 No. 2 P. 323–334
In this paper, an improved approach for automatic wildlife detection in natural environments based on the integration of a neural network architecture with a two-stream attention mechanism and a novel preclassification step based on infrared data has been presented. The proposed method addresses one of the key challenges in environmental monitoring: the need for scalable ...
Added: September 19, 2026
Aleksei Samarin, Nazarenko A., Kotenko E. et al., Proceedings of the ACM on Management of Data, USA 2026 Vol. 4 No. 1 P. 1–28
Modern knowledge and large volumes of data are increasingly encoded within neural networks, making the task of simplifying their structures and reducing the number of parameters especially relevant, both to improve efficiency and to facilitate deployment in resource-constrained environments. This paper presents a novel approach to neural network compression that addresses redundancy at both the ...
Added: September 19, 2026
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Pattern Recognition and Image Analysis 2025 Vol. 35 No. 2 P. 148–158
This paper describes our research on creating classifiers for microbial images (micrococci microscopy images) obtained from pictures of unfixed microscopic scenes. In our work, we propose an AutoML approach based on the automatic generation and analysis of the feature space for constructing the most optimal descriptors of microorganism images for subsequent classification. This makes it ...
Added: September 19, 2026
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Pattern Recognition and Image Analysis 2026 Vol. 36 No. 2 P. 302–312
The lack of annotated microscopic datasets remains a major obstacle to training robust deep learning models for microbial classification. In this paper, a novel data augmentation pipeline that uses visual–linguistic large-scale models to generate synthetic microscopic images of six different bacterial and nonbacterial classes has been proposed. Synthetic samples have gradually been added to the ...
Added: September 19, 2026
Springer, Cham, 2026.
computer vision ...
Added: September 19, 2026
Springer, Cham, 2026.
Added: September 19, 2026
FRUCT Oy, 2024.
Added: September 19, 2026
FRUCT Oy, 2024.
Added: September 19, 2026
FRUCT Oy, 2025.
Added: September 19, 2026
FRUCT Oy, 2026.
Added: September 19, 2026
Kychkin A., Chernitsin I., Прикладная информатика 2026 № 1(121) С. 40–58
The results of the development of a software microservice embedded in atmospheric air quality monitoring systems to support the identification of industrial pollution sources are presented. The emission and subsequent spread of harmful substances in the lower layers of the atmosphere is dynamic and characterized by high uncertainty due to the specific features of technological ...
Added: April 23, 2026
Cham: Springer, 2025.
This book constitutes the refereed proceedings of 34th International Workshops which were held in conjunction with the 34th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2025, held in Kaunas, Lithuania, September 9–12, 2025.
The 20 full papers and 8 abstracts included in this workshop volume were carefully reviewed and selected from 42 submissions. ...
Added: September 29, 2025
Delev A., Semakov S., , in: 2025 8th International Conference on Artificial Intelligence and Big Data (ICAIBD).: IEEE, 2025. P. 318–322.
Profit is one of the most important economic indicators of a company’s performance, and for every company it is necessary to allocate resources in such a way as to obtain the maximum possible profit. The profit maximization problem is usually a dynamic optimization problem. This article discusses an approach to solving the production expansion problem ...
Added: August 25, 2025
Pastushkov A., Boulatov A., Finance Research Letters 2025 Vol. 83 Article 107671
Recent studies have increasingly explored whether reinforcement learning algorithms can give rise to cooperative behavior that results in non-competitive pricing across various market settings. In financial markets, Cartea et al. (2022) show that market makers using multi-armed bandit (MAB) algorithms generally converge to competitive pricing in quote-driven over-the-counter (OTC) markets, barring some unlikely exceptions where ...
Added: June 19, 2025
Rozhkov M., Alyamovskaya N., Zakhodiakin G., International Journal of Production Research 2025 Vol. 63 No. 18 P. 6630–6647
This article investigates the application of reinforcement learning (RL) methods to optimise a four-echelon linear supply chain model with stochastic demand. The proposed supply chain configuration is largely based on the production-distribution supply chain of the MIT Supply Chain Beer Game. We show that RL can significantly improve ordering efficiency and overall supply chain performance. ...
Added: March 24, 2025
Blokhin A., Kalev V., Pusev R. et al., , in: 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON).: Novosibirsk: IEEE, 2024. P. 25–30.
Congestion control is one of the key mechanisms of communication in QUIC protocol which controls how much data and at which rate can be send to an endpoint at particular moment of time for better use of shared network resources and avoids moving into congestive collapse state. In this work we tackle the problem of ...
Added: December 18, 2024
Tiapkin D., Morozov N., Naumov A. et al., , in: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), 2-4 May 2024, Palau de Congressos, Valencia, Spain. PMLR: Volume 238Vol. 238.: Valencia: PMLR, 2024. P. 4213–4221.
The recently proposed generative flow networks (GFlowNets) are a method of training a policy to sample compositional discrete objects with probabilities proportional to a given reward via a sequence of actions. GFlowNets exploit the sequential nature of the problem, drawing parallels with reinforcement learning (RL). Our work extends the connection between RL and GFlowNets to ...
Added: June 22, 2024
Tiapkin D., Belomestny D., Calandriello D. et al., , in: Advances in Neural Information Processing Systems 36 (NeurIPS 2023).: Curran Associates, Inc., 2023. P. 73719–73774.
Added: February 17, 2024
Yu. A. Dubnov, A. Yu. Popkov, Polishchuk V. Y. et al., Automation and Remote Control 2023 Vol. 84 No. 1 P. 64–81
Randomized machine learning focuses on problems with considerable uncertainty in data and models. Machine learning algorithms are formulated in terms of a functional entropylinear programming problem. We adapt these algorithms to forecasting problems on an example of the evolution of thermokarst lakes area in permafrost zones. Thermokarst lakes generate methane, a greenhouse gas affecting climate ...
Added: February 5, 2024
Tiapkin D., Belomestny D., Calandriello D. et al., , in: Proceedings of the 40th International Conference on Machine Learning: Volume 202: International Conference on Machine Learning, 23-29 July 2023, Honolulu, Hawaii, USAVol. 202: International Conference on Machine Learning, 23-29 July 2023, Honolulu, Hawaii, USA.: PMLR, 2023. P. 34161–34221.
Added: December 1, 2023
Tiapkin D., Belomestny D., Naumov A. et al., Working papers by Cornell University. Series math "arxiv.org" 2023 Article 2304.03056
In this work, we derive sharp non-asymptotic deviation bounds for weighted sums of Dirichlet random variables. These bounds are based on a novel integral representation of the density of a weighted Dirichlet sum. This representation allows us to obtain a Gaussian-like approximation for the sum distribution using geometry and complex analysis methods. Our results generalize ...
Added: June 28, 2023
Belomestny D., Kaledin M., Golubev A., /. 2022.
Policy-gradient methods in Reinforcement Learning(RL) are very universal and widely applied in practice but their performance suffers from the high variance of the gradient estimate. Several procedures were proposed to reduce it including actor-critic(AC) and advantage actor-critic(A2C) methods. Recently the approaches have got new perspective due to the introduction of Deep RL: both new control ...
Added: April 14, 2023