1 citations · 1 across the 6 of their papers we have counts for
6 papers
Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems
Dmitry Bylinkin, Sergey Skorik, Dmitriy Bystrov +3
Heterogeneity within data distribution poses a challenge in many modern federated learning tasks. We formalize it as an optimization problem involving a computationally heavy compo…
Communication-Efficient Federated Learning with Adaptive Number of Participants
Sergey Skorik, Vladislav Dorofeev, Gleb Molodtsov +4
Rapid scaling of deep learning models has enabled performance gains across domains, yet it introduced several challenges. Federated Learning (FL) has emerged as a promising framewo…
Bant: Byzantine Antidote via Trial Function and Trust Scores
Gleb Molodtsov, Daniil Medyakov, Sergey Skorik +6
Recent advancements in machine learning have improved performance while also increasing computational demands. While federated and distributed setups address these issues, their st…
SSSD-ECG-nle: New Label Embeddings with Structured State-Space Models for ECG generation
Sergey Skorik, Aram Avetisyan
An electrocardiogram (ECG) is vital for identifying cardiac diseases, offering crucial insights for diagnosing heart conditions and informing potentially life-saving treatments. Ho…
Local Methods with Adaptivity via Scaling
Savelii Chezhegov, Sergey Skorik, Nikolas Khachaturov +5
The rapid development of machine learning and deep learning has introduced increasingly complex optimization challenges that must be addressed. Indeed, training modern, advanced mo…
Deep Neural Networks Generalization and Fine-Tuning for 12-lead ECG Classification
Aram Avetisyan, Shahane Tigranyan, Ariana Asatryan +4
Numerous studies are aimed at diagnosing heart diseases based on 12-lead electrocardiographic (ECG) records using deep learning methods. These studies usually use specific datasets…