7 papers
The Kuramoto Neural Operator: Learning to Solve PDEs via Coupled Oscillator Dynamics
Petr Badolia, Leonid Obukhov, Dmitry Bylinkin +1
Operator learning is a rapidly advancing area of computational science. It is particularly well suited to problems where a partial differential equation (PDE) must be solved repeat…
Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning
Dmitriy Bystrov, Daniil Medyakov, Dmitry Bylinkin +1
Fine-tuning large language models (LLMs) has become a central application of modern optimization, enabling pretrained models to adapt to diverse downstream tasks and domain-specifi…
Scalable Knowledge Editing for Mixture-of-Experts LLMs via Tensor-Structured Updates
Roman Maksimov, Vladimir Aletov, Dmitry Bylinkin +3
Knowledge editing (KE) provides a lightweight alternative to repeated fine-tuning of LLMs. However, most existing KE methods target dense feed-forward layers, while modern LLMs inc…
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…
Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation
Dmitry Bylinkin, Mikhail Aleksandrov, Savelii Chezhegov +1
Physics-informed neural networks (PINNs) have gained prominence in recent years and are now effectively used in a number of applications. However, their performance remains unstabl…
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…