3 papers
cs.LG2026
Correcting Sensor-Induced Distribution Drift with Wasserstein Adversarial Learning
Saraa Ali, Vladimir Bocharnikov, Fedor Ratnikov +3
The quality of recorded data depends on the stability of the sensor system that acquires it. Sensor motion and aging can degrade the performance and stability of downstream data-dr…
cs.LG2026
Physics-Constrained Neural Networks for Improved Short-Term Weather Forecasting: A Case Study over the South Pacific
Egor Bugaev, Fedor Buzaev, Dmitry Efremenko +2
This study introduces enhancements to physics-constrained neural networks (PCNNs) that improve the accuracy and stability of hybrid short-term weather forecasting models. Building…
cs.LG2025
Approach to Finding a Robust Deep Learning Model
Alexey Boldyrev, Fedor Ratnikov, Andrey Shevelev
The rapid development of machine learning (ML) and artificial intelligence (AI) applications requires the training of large numbers of models. This growing demand highlights the im…