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Multi-user facial emotion recognition in video based on user-dependent neural network adaptation
P. 1–5.
In this paper, the multi-user video-based facial emotion recognition is examined in the presence of a small data set with the emotions of end users. By using the idea of speaker-dependent speech recognition, we propose a novel approach to solve this task if labeled video data from end users is available. During the training stage, a deep convolutional neural network is trained for user-independent emotion classification. Next, this classifier is adapted (fine-tuned) on the emotional video of a concrete person. During the recognition stage, the user is identified based on face recognition techniques, and an emotional model of the recognized user is applied. It is experimentally shown that this approach improves the accuracy of emotion recognition by more than 20% for the RAVDESS dataset.
Marinin N., Getman A., В кн.: Труды Института системного программирования РАНЧ. 2. Т. 38. Вып. 4.: ИСП РАН, 2026.
This paper investigates a method for detecting DNS tunnels in network traffic using a neural network.
To achieve this, an analysis of current detection approaches was conducted, and a dataset for neural network
training was prepared. The proposed model takes as input a sequence of characters extracted from DNS
responses. The trained model demonstrated an F1-score close to ...
Added: September 23, 2026
Vladimir Bogachev, Aletov V., Alexander Molozhavenko et al., , in: The Fourteenth International Conference on Learning Representations (ICLR 2026).: ICLR, 2026. Ch. 20503 P. 1–26.
This work presents a novel, fully Riemannian framework for Low-Rank Adaptation (LoRA) that geometrically treats low-rank adapters by optimizing them directly on the fixed-rank manifold. This formulation eliminates the parametrization ambiguity present in standard Euclidean optimizers. Our framework integrates three key components to achieve this: (1) we derive Riemannion, a new Riemannian optimizer on the fixed-rank ...
Added: April 29, 2026
Kotov F., 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
Derkacheva A., Frost G., Epstein H. et al., Journal of Ecology 2025 Vol. 113 No. 10 P. 2813–2831
Tundra shrub expansion is a central form of change in warming Arctic ecosystems, but the pace of shrubification varies across spatial scales, complicating efforts to understand its drivers and consequences. Here, we apply convolutional neural networks (CNNs) to very-high resolution satellite image pairs acquired 10–15 years apart (circa 2005–2019) to identify spatio-temporal patterns of tall shrub ...
Added: August 4, 2025
E.V. Surikova, E.A. Sabidaeva, , in: Параллельные вычислительные технологии – XIX всероссийская конференция с международным участием, ПаВТ'2025, г. Москва, 8–10 апреля 2025 г. Короткие статьи и описания плакатов.: Челябинск: Издательский центр ЮУрГУ, 2025. P. 90–98.
Added: July 3, 2025
Shaitan A., Qi R., Liu S. et al., 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
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