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Применение метода Transfer Learning к задаче машинного перевода для пары русско-хакасский
С. 460–471.
Лебедева А. Ю.
In book
Каз.: Издательство Академии наук Республики Татарстан, 2023.
D.D. Sukhoverkhova, L.N. Shchur, , in: Параллельные вычислительные технологии – XIX всероссийская конференция с международным участием, ПаВТ'2025. Короткие статьи и описания плакатов.: Издательский центр ЮУрГУ, 2025. P. 82–89.
We apply supervised deep machine learning techniques to extract properties of the anisotropic Ising model. We consider two cases of anisotropy: orthogonal and diagonal. From the predictions of the neural network, we obtained phase probability functions, from which we measured two quantities: the critical temperature and the critical exponent of the correlation length. We estimated ...
Added: December 4, 2025
Chertenkov V., Shchur L., Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 2025 Vol. 112 No. 3 Article 034104
The main question raised in the article is whether a neural network trained on a spin lattice model in one universality class can be used to test a model in another universality class. The quantities of interest are the critical phase transition temperature and the correlation length exponent. In other words, the question of ...
Added: August 12, 2025
Chertenkov V., Shchur L., Lecture Notes in Computer Science 2025 Vol. 15406 P. 434–449
Machine learning is a new tool for investigating physical models. One possible applications is the study of phase transitions analyzing the distribution of spins on regular lattices using supervised learning approach. A new question is the applicability of transfer learning, a network supervised on a particular model and used to infer information about another model.
The ...
Added: February 10, 2025
D. D. Sukhoverkhova, L. N. Shchur, Lobachevskii Journal of Mathematics 2025 Vol. 46 No. 1 P. 528–534
We investigate the possibility of extracting features of second-order phase transitions using transfer machine learning. We have performed supervised machine learning for binary classification of snapshots of the spin distribution of the isotropic Ising model. The binary classification is performed in ferromagnetic and paramagnetic phases using a known critical temperature. The trained network is used ...
Added: January 13, 2025
Sukhoverkhova D., Shchur L., Письма в Журнал экспериментальной и теоретической физики 2024 Т. 120 № 8 С. 644–649
In this paper, we applied a deep neural network to study the issue of knowledge transferability between statistical mechanics models. The following computer experiment was conducted. A convolutional neural network was trained to solve the problem of binary classification of snapshots of snapshots of the location of spins of the Ising model on a two-dimensional ...
Added: September 25, 2024
Sadrtdinov I., Dmitrii Pozdeev, Dmitry P Vetrov et al., , in: Advances in Neural Information Processing Systems 36 (NeurIPS 2023).: Curran Associates, Inc., 2023. P. 15936–15964.
Transfer learning and ensembling are two popular techniques for improving the performance and robustness of neural networks. Due to the high cost of pre-training, ensembles of models fine-tuned from a single pre-trained checkpoint are often used in practice. Such models end up in the same basin of the loss landscape, which we call the pre-train ...
Added: February 26, 2024
Pavel Latyshev, Fedor Pavlov, Herbert A. et al., , in: Proceedings of 11th Moscow Conference on Computational Molecular Biology MCCMB'23.: IITP RAS, 2023.
Added: December 1, 2023
Malik M. S., Nazarova A., Mona M. J. et al., Journal of King Saud University - Computer and Information Sciences 2023 Vol. 35 No. 8 Article 101736
Hope Speech Detection (HSD) from social media is a new direction for promoting and supporting positive content to encourage harmony and positivity in society. As users of social media belong to different linguistic communities, hope speech detection is rarely studied as a multilingual task considering low-resource languages. Moreover, prior studies explored only monolingual techniques, and the ...
Added: November 22, 2023
Magaj G., Soroka A., Studies in Computational Intelligence, 2023.
The basis of transfer learning methods is the ability of deep neural networks to use knowledge from one domain to learn in another domain. However, another important task is the analysis and explanation of the internal representations of deep neural networks models in the process of transfer learning. Some deep models are known to be ...
Added: October 25, 2023
Pavel Latyshev, Fedor Pavlov, Herbert A. et al., Frontiers in Big Data 2023 Vol. 6 Article 1140663
Due to advances in NGS technologies whole-genome maps of various functional genomic elements were generated for a dozen of species, however experiments are still expensive and are not available for many species of interest. Deep learning methods became the state-of-the-art computational methods to analyze the available data, but the focus is often only on the ...
Added: June 8, 2023
Zhang S., Zhang X., Chan J. et al., Information Processing and Management 2019 Vol. 56 No. 5 P. 1633–1644
Irony as a literary technique is widely used in online texts such as Twitter posts. Accurate irony detection is crucial for tasks such as effective sentiment analysis. A text's ironic intent is defined by its context incongruity. For example in the phrase "I love being ignored", the irony is defined by the incongruity between the ...
Added: October 29, 2020
Polyakov E. V., Polyakov S. V., Abramov P., , in: Proceedings of 2019 XVI International Symposium "Problems of Redundancy in Information and Control Systems" (REDUNDANCY).: IEEE, 2019. P. 159–164.
Determining the tonality of the text is a difficult task, the solution of which essentially depends on the context, the field of study and the amount of text data. The analysis shows that the authors in their works do not jointly use the full range of possible transformations on the data and their combinations. The ...
Added: September 20, 2020
Polyakov E. V., Voskov L., Abramov P. et al., Informatsionno-upravliaiushchie sistemy [Information and Control Systems] 2020 No. 1 P. 2–14
Introduction: Sentiment analysis is a complex problem whose solution essentially depends on the context, field of study and amount of text data. Analysis of publications shows that the authors often do not use the full range of possible data transformations and their combinations. Only a part of the transformations is used, limiting the ways to ...
Added: February 20, 2020