Do Different Deep Metric Learning Losses Lead to Similar Learned Features? - Konstantin Kobs; Michael Steininger; Andrzej Dulny; Andreas Hotho (ICCV, 2021)

Learning Mathematical Relations Using Deep Tree Models. - Sebastian Wankerl; Andrzej Dulny; Gerhard Götz; Andreas Hotho (ICMLA, 2021)

NeuralPDE: Modelling Dynamical Systems from Data. - Andrzej Dulny; Andreas Hotho; Anna Krause (KI, 2022)

Can Neural Networks Distinguish High-school Level Mathematical Concepts? - Sebastian Wankerl; Andrzej Dulny; Gerhard Götz; Andreas Hotho (ICDM, 2023)

Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks. - Melanie Schaller; Michael Steininger; Andrzej Dulny; Daniel Schlör; Andreas Hotho (LWDA, 2023)

DynaBench: A Benchmark Dataset for Learning Dynamical Systems from Low-Resolution Data. - Andrzej Dulny; Andreas Hotho; Anna Krause (ECML/PKDD (1), 2023)

TaylorPDENet: Learning PDEs from non-grid Data. - Paul Heinisch; Andrzej Dulny; Anna Krause; Andreas Hotho (CoRR, 2023)

GrINd: Grid Interpolation Network for Scattered Observations. - Andrzej Dulny; Paul Heinisch; Andreas Hotho; Anna Krause (ECML/PKDD (7), 2024)

Identifying Axiomatic Mathematical Transformation Steps using Tree-Structured Pointer Networks. - Sebastian Wankerl; Jan Pfister; Andrzej Dulny; Gerhard Götz; Andreas Hotho (Trans. Mach. Learn. Res., 2025)

Physical knowledge improves prediction of EM Fields. - Andrzej Dulny; Farzad Jabbarigargari; Andreas Hotho; Laura Maria Schreiber; Maxim Terekhov; Anna Krause (CoRR, 2025)