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2020 – today
- 2024
[c7]Andrzej Dulny, Paul Heinisch, Andreas Hotho, Anna Krause:
GrINd: Grid Interpolation Network for Scattered Observations. ECML/PKDD (7) 2024: 177-193- [i5]Andrzej Dulny, Paul Heinisch, Andreas Hotho, Anna Krause:
GrINd: Grid Interpolation Network for Scattered Observations. CoRR abs/2403.19570 (2024) - 2023
- [c6]Sebastian Wankerl, Andrzej Dulny, Gerhard Götz
, Andreas Hotho:
Can Neural Networks Distinguish High-school Level Mathematical Concepts? ICDM 2023: 1397-1402 - [c5]Melanie Schaller, Michael Steininger, Andrzej Dulny, Daniel Schlör, Andreas Hotho:
Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks. LWDA 2023: 454-469 - [c4]Andrzej Dulny, Andreas Hotho, Anna Krause:
DynaBench: A Benchmark Dataset for Learning Dynamical Systems from Low-Resolution Data. ECML/PKDD (1) 2023: 438-455 - [i4]Andrzej Dulny, Andreas Hotho, Anna Krause:
DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data. CoRR abs/2306.05805 (2023) - [i3]Paul Heinisch, Andrzej Dulny, Anna Krause, Andreas Hotho:
TaylorPDENet: Learning PDEs from non-grid Data. CoRR abs/2306.14511 (2023) - 2022
- [c3]Andrzej Dulny, Andreas Hotho, Anna Krause:
NeuralPDE: Modelling Dynamical Systems from Data. KI 2022: 75-89 - [i2]Konstantin Kobs, Michael Steininger, Andrzej Dulny, Andreas Hotho:
Do Different Deep Metric Learning Losses Lead to Similar Learned Features? CoRR abs/2205.02698 (2022) - 2021
- [c2]Konstantin Kobs, Michael Steininger, Andrzej Dulny, Andreas Hotho:
Do Different Deep Metric Learning Losses Lead to Similar Learned Features? ICCV 2021: 10624-10634 - [c1]Sebastian Wankerl, Andrzej Dulny, Gerhard Götz, Andreas Hotho:
Learning Mathematical Relations Using Deep Tree Models. ICMLA 2021: 1681-1687 - [i1]Andrzej Dulny, Andreas Hotho, Anna Krause:
NeuralPDE: Modelling Dynamical Systems from Data. CoRR abs/2111.07671 (2021)
Coauthor Index
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