3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Enhancing Fractional Gradient Descent with Learned Optimizers
Jan Sobotka, Petr Šimánek, Pavel Kordík
Fractional Gradient Descent (FGD) offers a novel and promising way to accelerate optimization by incorporating fractional calculus into machine learning. Although FGD has shown enc…
cs.CV2022★ 3 cited
WeatherFusionNet: Predicting Precipitation from Satellite Data
Jiří Pihrt, Rudolf Raevskiy, Petr Šimánek +1
The short-term prediction of precipitation is critical in many areas of life. Recently, a large body of work was devoted to forecasting radar reflectivity images. The radar images…
cs.LG2022★ 1 cited
Learning to Optimize with Dynamic Mode Decomposition
Petr Šimánek, Daniel Vašata, Pavel Kordík
Designing faster optimization algorithms is of ever-growing interest. In recent years, learning to learn methods that learn how to optimize demonstrated very encouraging results. C…