| Deep learning driven self-adaptive hp finite element method M Paszyński, R Grzeszczuk, D Pardo, L Demkowicz International Conference on Computational Science, 114-121, 2021 | 28 | 2021 |
| Quasi-optimal hp-finite element refinements towards singularities via deep neural network prediction T Służalec, R Grzeszczuk, S Rojas, W Dzwinel, M Paszyński Computers & Mathematics with Applications 142, 157-174, 2023 | 8 | 2023 |
| Approach to classifying data with highly localized unmarked features using neural networks R Grzeszczuk Computer Science 20, 329-342, 2019 | 2 | 2019 |
| Prediction of the facial growth direction: regression perspective S Kaźmierczak, Z Juszka, R Grzeszczuk, M Kurdziel, ... International Conference on Neural Information Processing, 395-407, 2022 | 1 | 2022 |
| Monte Carlo Winning Tickets R Grzeszczuk, M Kurdziel International Conference on Computational Science, 133-139, 2021 | | 2021 |
| Zastosowanie metod uczenia maszynowego do budowy binarnych reprezentacji tekstu R Grzeszczuk | | |
| Approach to classifying data with highly localized unmarked features using neural networks............................................. 329 Mateusz Różański, Leszek Siwik … PA Nerurkar, M Chandane, S Bhirud, M Lawnik, A Kapczyński, Ö Çoban, ... | | |
| Deep Neural Network-driven hp-adaptive Finite Element Method in three dimensions M Paszyński, R Grzeszczuk, W Dzwinel, D Pardo | | |
Marcin KurdzielAGH University of Science and TechnologyE-mailová adresa ověřena na: agh.edu.pl
