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RecVAE: A new variational autoencoder for top-n recommendations with implicit feedback
P. 528–536.
In book
Association for Computing Machinery (ACM), 2020.
Makarov N., Savchenko A., Zemtsova I. et al., Scientific Reports 2025 Vol. 15 Article 26641
The grey wolf (Canis lupus) is a pivotal species for ecological studies. As a key participant in ecosystem
processes, it also serves as a model for investigating social structure formation and ecological
adaptation. However, the species’ complex social behavior, spatial dynamics, and expansive habitats
make monitoring and population assessments across large areas particularly challenging. In recent
years, audio traps ...
Added: June 16, 2026
Vasilev R., Savchenko A., Blinov P. et al., Frontiers in Medicine 2026 Vol. 13 Article 1778404
Automated disease screening systems face challenges when applied to multi-class medical image analysis, particularly under severe class imbalance inherent in clinical datasets. Retinal fundus imaging enables non-invasive screening for multiple ocular and systemic diseases simultaneously, yet current automated systems typically assess risk for only a single pathology or a limited disease range. We developed a ...
Added: June 16, 2026
Hushchyn M., Arzymatov K., Derkach D., Machine Learning 2026 Vol. 115 Article 56
Moments when a time series changes its behavior are called change points. Occurrence of change point implies that the state of the system is altered and its timely detection might help to prevent unwanted consequences. In this paper, we present two change-point detection approaches based on neural networks and online learning. These algorithms demonstrate linear ...
Added: March 6, 2026
Kseniia Prokudina, Mikhail Skriplyonok, Alexander Vostrikov, , in: 2026 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).: IEEE, 2026. P. 842–847.
This article analyzes the evolution of digital image manipulation detection methods over the 2016–2026 decade. It examines the transition from classic passive methods (ELA, metadata analysis, and noise pattern analysis) to deep neural network architectures (VGG16+U-Net, ManTra-Net, SPAN, and RDS-YOLOv5) and then to multimodal systems based on large language models (ForgeryGPT and FakeShield), which provide ...
Added: February 25, 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
Yusupov V., Rakhuba M., Frolov E., , in: CIKM '25: Proceedings of the 34rd ACM International Conference on Information and Knowledge Management.: ACM, 2025. Ch. 1 P. 5469–5473.
In this work, we present a fast and effective Linear approach for updating recommendations in a scalable graph-based recommender system UltraGCN. Solving this task is extremely important to maintain the relevance of the recommendations under the conditions of a large amount of new data and changing user preferences. To address this issue, we adapt the ...
Added: October 3, 2025
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
Fedotov G., Международный вестник криминалистики 2024 № 92 С. 77–83
В последние годы наблюдается значительный прогресс в качестве синтетически сгенерированного контента. Кроме того, регулярно появляются инструменты, с помощью которых обычный пользователь персонального компьютера может создать реалистичный поддельный контент. В работе исследуется развитие генеративных моделей в задаче Face Synthesis, а также способы обнаружения дипфейков, созданных с помощью моделей этого класса. Представленные в работе подходы показали хорошую ...
Added: September 24, 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
Teplova T., Fayzulin M., Kurkin A., Socio-Economic Planning Sciences 2025 No. 101 Article 102292
This research is devoted to the development and evaluation of the effectiveness of machine learning and deep learning models for forecasting crisis phenomena in the Russian stock market. The work covers the period from the beginning of 2014 to June 2024, using the IMOEX index as the main indicator of the market condition. Special attention ...
Added: August 2, 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
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