3 citations · 5 across the 6 of their papers we have counts for
6 papers
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…
Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models
Jan Martinů, Petr Šimánek
This study investigates the application of deep-learning diffusion models for the super-resolution of weather data, a novel approach aimed at enhancing the spatial resolution and d…
Investigation into the Training Dynamics of Learned Optimizers
Jan Sobotka, Petr Šimánek, Daniel Vašata
Optimization is an integral part of modern deep learning. Recently, the concept of learned optimizers has emerged as a way to accelerate this optimization process by replacing trad…
Improving deep learning precipitation nowcasting by using prior knowledge
Matej Choma, Petr Šimánek, Jakub Bartel
Deep learning methods dominate short-term high-resolution precipitation nowcasting in terms of prediction error. However, their operational usability is limited by difficulties exp…
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…
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…