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Эмоциональный анализ постов в ВКонтакте: классификатор или регрессор
С. 311–322.
Kolmogorova A., Калинин А. А.
The article summarizes the results of two tasks in machine learning paradigm: the task of classification according
to the criterion of dominating emotion on the data of social networks posts in Russian and the regression task using
the same data. The experiments are conducted on the data set collected from VKontakte social network and consisted
of 3879 posts assessed by 2000 informants on Toloka crowd sourcing platform. The annotation procedure was based
on the original interface for non-discrete emotion assessment elaborated by researchers.
In book
Вып. 21. , Изд-во РГГУ, 2022.
Глазкова А. В., Смаль И. В., Lyashevskaya O. et al., Доклады Российской академии наук. Математика, информатика, процессы управления (ранее - Доклады Академии Наук. Математика) 2025 Т. 527 С. 146–155
This paper presents a study on the effectiveness of discriminative methods for abbreviation lemmatization in Russian texts. Unlike generative approaches, discriminative models select the optimal lemma from a fixed set of candidates, eliminating the risk of generating grammatically incorrect word forms. For the first time in Russian language processing, we conduct a comprehensive analysis of ...
Added: March 10, 2026
Glazkova A., Lyashevskaya O., Morozov D. et al., Journal of Mathematical Sciences 2025 Vol. 546 P. 32–47
This paper addresses the task of lemmatizing abbreviations in the Russian language. Abbreviation lemmatization is particularly challenging, as it involves not only transforming a word into its normal form but also correctly expanding the abbreviation. We explore two approaches to this task, both leveraging large pretrained language models. The first approach is generative, where the ...
Added: March 10, 2026
Котов Ф. И., Timokhin I., Ivanov F., , in: 2023 XVIII International Symposium Problems of Redundancy in Information and Control Systems (REDUNDANCY).: IEEE, 2023.
The Successive Cancellation List (SCL) algorithm is a widely used decoding technique in communication systems. However, constructing the critical set for SCL decoding is a challenging task, as it requires a large number of computations and can lead to significant decoding delays. In this paper, a new approach to critical set construction for SCL decoding ...
Added: January 26, 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
Artem B., Andreasyan A., Konovalov D. et al., Scientific Reports 2025 Vol. 15 Article 23119
G-quadruplexes (GQs) are non-canonical DNA structures encoded by G-flipons with potential roles in gene regulation and chromatin structure. Here, we explore the role of G-flipons in tissue specification. We present a deep learning-based framework for the genome-wide G-flipon predictions across 14 human tissue types. The model was trained using high-confidence experimental maps of GQ-forming sequences ...
Added: August 8, 2025
Shaitan A., Science China Information Sciences 2025 Vol. 68 No. 7 Article 170102
Artificial intelligence (AI) is revolutionizing the field of drug development, particularly in addressing key challenges such as drug response prediction, drug combination design, drug repositioning, and drug molecule generation. Traditional drug discovery is hindered by long timelines, high costs, and low success rates, necessitating innovative technologies to accelerate the process. AI technologies, such as deep ...
Added: June 25, 2025
Boldyrev A., Ratnikov F., Shevelev A., IEEE Access 2025 Vol. 13 P. 102390–102406
The rapid development of machine learning (ML) and artificial intelligence (AI) applications
requires the training of a large numbers of models. This growing demand highlights the importance of
training models without human supervision, while ensuring that their predictions are reliable. In response
to this need, we propose a novel approach for determining model robustness. This approach, supplemented
with a ...
Added: June 15, 2025
Podchufarov A., Galkina A. N., Ванина С. С. et al., Экономика и управление: проблемы, решения 2025 Т. 5 № 4 С. 61–74
Under modern conditions, the introduction of artificial intelligence technologies is becoming a significant factor in the development of high-tech industries. The article presents the results of a study of the prospects for the use of intelligent analytical systems in nuclear energy. The experience of foreign countries is analyzed and the features of successful projects using ...
Added: June 5, 2025
Ядринцев В. В., Соченков И. В., Ryzhova A., В кн.: ИТиС 2024: Сборник трудов 48-й междисциплинарной школы-конференции ИППИ РАН "Информационные технологии и системы 2024".: Институт проблем передачи информации им. А.А. Харкевича РАН, 2024. С. 52–63.
В статье представлена открытая библиотека тематической классификации текстов на основе гибридных методов объяснимого искусственного интеллекта ExTCL (Exactus eXplainable TextClassification Library). В библиотеке реализованы алгоритмы классификации на основе характеристики тематической значимости, алгоритмы на основе векторных языковых моделей Word2Vec, SentenceBERT и LASER, а также методы объяснения результатов их работы путём указания на ключевые слова и словосочетания с ...
Added: May 12, 2025
Ryzhova A., Sochenkov I., , in: Proceeding 2019 Ivannikov Ispras Open Conference (ISPRAS).: IEEE Computer Society, 2019. P. 60–67.
Added: May 1, 2025
Walton S., Klyukin V., Artemev M. et al., , in: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).: IEEE, 2025. P. 3328–3337.
Explicit density learners are becoming an increasingly popular technique for generative models because of their ability to better model probability distributions. They have advantages over Generative Adversarial Networks due to their ability to perform density estimation and having exact latent-variable inference. This has many advantages, including: being able to simply interpolate, calculate sample likelihood, and ...
Added: April 1, 2025
Derkach D., Artemev M., IEEE, 2025.
Added: April 1, 2025
Perelygin V., Kamelin A., Syzrantsev N. et al., Frontiers in Medicine 2025 Vol. 11 Article 1479717
Polygenic risk score (PRS) prediction is widely used to assess the risk of diagnosis and progression of many diseases. Routinely, the weights of individual SNPs are estimated by the linear regression model that assumes independent and linear contribution of each SNP to the phenotype. However, for complex multifactorial diseases such as Alzheimer’s disease, diabetes, cardiovascular ...
Added: March 4, 2025
Ivan Rubachev, Nikolay Kartashev, Gorishniy Y. et al., , in: Proceedings of the 13th International Conference on Learning Representations (ICLR 2025).: ICLR, 2025. P. 53831–53867.
Advances in machine learning research drive progress in real-world applications. To ensure this progress, it is important to understand the potential pitfalls on the way from a novel method's success on academic benchmarks to its practical deployment. In this work, we analyze existing tabular deep learning benchmarks and find two common characteristics of tabular data ...
Added: March 1, 2025
W. Joseph MacInnes, Zhozhikashvili N., Feurra M., , in: First International Conference, AIiH 2024, Swansea, UK, September 4–6, 2024, Proceedings, Part II. Artificial Intelligence in Healthcare. LNCS, volume 14976Vol. 14976.: Springer, 2024. P. 221–234.
Convolutional Neural Networks (CNNs) match human performance in many visual tasks like the classification of images, however they may not simulate the underlying biological processes. We implemented a CNN to try replicate results from an object inversion experiment with Transcranial Magnetic Stimulation (TMS). After training on upright faces, the CNN model went through three stages ...
Added: January 28, 2025
Yury Gorishniy, Ivan Rubachev, Nikolay Kartashev et al., , in: Proceedings of the 12th International Conference on Learning Representations (ICLR 2024).: ICLR, 2024.
Deep learning (DL) models for tabular data problems (e.g. classification, regression) are currently receiving increasingly more attention from researchers. However, despite the recent efforts, the non-DL algorithms based on gradient-boosted decision trees (GBDT) remain a strong go-to solution for these problems. One of the research directions aimed at improving the position of tabular DL involves ...
Added: January 22, 2025