| NeuralPDE: modelling dynamical systems from data A Dulny, A Hotho, A Krause German Conference on Artificial Intelligence (Künstliche Intelligenz), 75-89, 2022 | 9 | 2022 |
| Do different deep metric learning losses lead to similar learned features? K Kobs, M Steininger, A Dulny, A Hotho Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 9 | 2021 |
| Learning mathematical relations using deep tree models S Wankerl, A Dulny, G Götz, A Hotho 2021 20th IEEE International Conference on Machine Learning and Applications …, 2021 | 5 | 2021 |
| Evaluating the multi-task learning approach for land use regression modelling of air pollution A Dulny, M Steininger, F Lautenschlager, A Krause, A Hotho Journal of Physics: Conference Series 1834 (1), 012004, 2021 | 5 | 2021 |
| DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data A Dulny, A Hotho, A Krause arXiv preprint arXiv:2306.05805, 2023 | 2 | 2023 |
| GrINd: Grid Interpolation Network for Scattered Observations A Dulny, P Heinisch, A Hotho, A Krause arXiv preprint arXiv:2403.19570, 2024 | | 2024 |
| Can Neural Networks Distinguish High-school Level Mathematical Concepts? S Wankerl, A Dulny, G Götz, A Hotho 2023 IEEE International Conference on Data Mining (ICDM), 1397-1402, 2023 | | 2023 |
| TaylorPDENet: Learning PDEs from non-grid Data P Heinisch, A Dulny, A Krause, A Hotho arXiv preprint arXiv:2306.14511, 2023 | | 2023 |
| Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks M Schaller, M Steininger, A Dulny, D Schlör, A Hotho | | 2023 |
| Probabilistyczny algorytm obliczania indeksu Shapleya-Shubika A Dulny | | 2016 |
