| Causal confusion in imitation learning P De Haan, D Jayaraman, S Levine NeurIPS 2019, 2019 | 330 | 2019 |
| Gauge equivariant mesh cnns: Anisotropic convolutions on geometric graphs P De Haan, M Weiler, T Cohen, M Welling ICLR 2021, 2020 | 118 | 2020 |
| Explorations in Homeomorphic Variational Auto-Encoding L Falorsi, P de Haan, TR Davidson, N De Cao, M Weiler, P Forré, ... ICML 2018 workshop on Theoretical Foundations and Applications of Deep …, 2018 | 118 | 2018 |
| Weakly supervised causal representation learning J Brehmer*, P De Haan*, P Lippe, T Cohen NeurIPS 2022, 2022 | 96 | 2022 |
| Natural graph networks P de Haan, T Cohen, M Welling NeurIPS 2020, 2020 | 93 | 2020 |
| Reparameterizing Distributions on Lie Groups L Falorsi, P de Haan, TR Davidson, P Forré AISTATS 2019, 2019 | 84 | 2019 |
| Mesh neural networks for SE (3)-equivariant hemodynamics estimation on the artery wall J Suk, P de Haan, P Lippe, C Brune, JM Wolterink Computers in Biology and Medicine, 108328, 2024 | 35* | 2024 |
| Geometric Algebra Transformers J Brehmer*, P De Haan*, S Behrends, T Cohen NeurIPS 2023, 2023 | 23 | 2023 |
| Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows P de Haan, C Rainone, M Cheng, R Bondesan NeurIPS 2021 workshop on Machine Learning for Physical Systems, 2021 | 23 | 2021 |
| Covariance in physics and convolutional neural networks MCN Cheng, V Anagiannis, M Weiler, P de Haan, TS Cohen, M Welling arXiv preprint arXiv:1906.02481, 2019 | 18 | 2019 |
| Learning Lattice Quantum Field Theories with Equivariant Continuous Flows M Gerdes, P de Haan, C Rainone, R Bondesan, MCN Cheng SciPost Physics, 2023 | 15* | 2023 |
| Rigid body flows for sampling molecular crystal structures J Köhler, M Invernizzi, P de Haan, F Noé ICML 2023, 2023 | 15 | 2023 |
| EDGI: Equivariant Diffusion for Planning with Embodied Agents J Brehmer, J Bose, P De Haan, T Cohen NeurIPS 2023, 2023 | 14 | 2023 |
| Topological Constraints on Homeomorphic Auto-Encoding P de Haan, L Falorsi NeurIPS 2018 Workshop on Integration of Deep Learning Theories, 2018 | 8 | 2018 |
| Deconfounded imitation learning R Vuorio, J Brehmer, H Ackermann, D Dijkman, T Cohen, P de Haan arXiv preprint arXiv:2211.02667, 2022 | 6 | 2022 |
| Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers P De Haan, T Cohen, J Brehmer AISTATS 2024, 2023 | 5 | 2023 |
| Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics J Spinner, V Bresó, P de Haan, T Plehn, J Thaler, J Brehmer arXiv preprint arXiv:2405.14806, 2024 | 2 | 2024 |
| FoMo Rewards: Can we cast foundation models as reward functions? E Singh Lubana, J Brehmer, P de Haan, T Cohen arXiv e-prints, arXiv: 2312.03881, 2023 | 1* | 2023 |
| System and process for deconfounded imitation learning R Vuorio, DE Pim, JH Brehmer, H Ackermann, TS Cohen, DHF Dijkman US Patent App. 18/459,258, 2024 | | 2024 |
| Efficient machine learning message passing on point cloud data DE Pim, TS Cohen US Patent App. 18/326,800, 2024 | | 2024 |
