| Causal confusion in imitation learning P De Haan, D Jayaraman, S Levine NeurIPS 2019, 2019 | 358 | 2019 |
| Gauge equivariant mesh cnns: Anisotropic convolutions on geometric graphs P De Haan, M Weiler, T Cohen, M Welling ICLR 2021, 2020 | 128 | 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 | 122 | 2018 |
| Weakly supervised causal representation learning J Brehmer*, P De Haan*, P Lippe, T Cohen NeurIPS 2022, 2022 | 111 | 2022 |
| Natural graph networks P de Haan, T Cohen, M Welling NeurIPS 2020, 2020 | 95 | 2020 |
| Reparameterizing Distributions on Lie Groups L Falorsi, P de Haan, TR Davidson, P Forré AISTATS 2019, 2019 | 88 | 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 | 44* | 2024 |
| Geometric Algebra Transformers J Brehmer*, P De Haan*, S Behrends, T Cohen NeurIPS 2023, 2023 | 38 | 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 | 25 | 2021 |
| EDGI: Equivariant Diffusion for Planning with Embodied Agents J Brehmer, J Bose, P De Haan, T Cohen NeurIPS 2023, 2023 | 24 | 2023 |
| 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 | 19 | 2019 |
| Learning Lattice Quantum Field Theories with Equivariant Continuous Flows M Gerdes*, P de Haan*, C Rainone, R Bondesan, MCN Cheng SciPost Physics, 2023 | 18* | 2023 |
| Rigid body flows for sampling molecular crystal structures J Köhler, M Invernizzi, P de Haan, F Noé ICML 2023, 2023 | 18 | 2023 |
| Topological Constraints on Homeomorphic Auto-Encoding P de Haan, L Falorsi NeurIPS 2018 Workshop on Integration of Deep Learning Theories, 2018 | 10 | 2018 |
| Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers P De Haan, T Cohen, J Brehmer AISTATS 2024, 2023 | 7 | 2023 |
| Deconfounding Imitation Learning with Variational Inference R Vuorio*, P De Haan*, J Brehmer, H Ackermann, D Dijkman, T Cohen Transactions on Machine Learning Research, 2024 | 6* | 2024 |
| 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 | 2* | 2023 |
| Geometric algebra transformers for large 3D meshes via cross-attention J Suk, P De Haan, B Imre, JM Wolterink ICML 2024 Workshop on Geometry-grounded Representation Learning and …, 2024 | 1 | 2024 |
| Continuous normalizing flows for lattice gauge theories M Gerdes, P de Haan, R Bondesan, MCN Cheng arXiv preprint arXiv:2410.13161, 2024 | | 2024 |
