Explorations in Homeomorphic Variational Auto-Encoding. - Luca Falorsi; Pim de Haan; Tim R. Davidson; Nicola De Cao; Maurice Weiler; Patrick Forré; Taco S. Cohen (CoRR, 2018)

Topological Constraints on Homeomorphic Auto-Encoding. - Pim de Haan; Luca Falorsi (CoRR, 2018)

Reparameterizing Distributions on Lie Groups. - Luca Falorsi; Pim de Haan; Tim R. Davidson; Patrick Forré (AISTATS, 2019)

Causal Confusion in Imitation Learning. - Pim de Haan; Dinesh Jayaraman; Sergey Levine (NeurIPS, 2019)

Covariance in Physics and Convolutional Neural Networks. - Miranda C. N. Cheng; Vassilis Anagiannis; Maurice Weiler; Pim de Haan; Taco S. Cohen; Max Welling (CoRR, 2019)

Natural Graph Networks. - Pim de Haan; Taco S. Cohen; Max Welling (NeurIPS, 2020)

Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs. - Pim de Haan; Maurice Weiler; Taco Cohen; Max Welling (ICLR, 2021)

Mesh Convolutional Neural Networks for Wall Shear Stress Estimation in 3D Artery Models. - Julian Suk; Pim de Haan; Phillip Lippe; Christoph Brune; Jelmer M. Wolterink (STACOM@MICCAI, 2021)

Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows. - Pim de Haan; Corrado Rainone; Miranda C. N. Cheng; Roberto Bondesan (CoRR, 2021)

Weakly supervised causal representation learning. - Johann Brehmer; Pim de Haan; Phillip Lippe; Taco S. Cohen (NeurIPS, 2022)

Learning Lattice Quantum Field Theories with Equivariant Continuous Flows. - Mathis Gerdes; Pim de Haan; Corrado Rainone; Roberto Bondesan; Miranda C. N. Cheng (CoRR, 2022)

Deconfounded Imitation Learning. - Risto Vuorio; Johann Brehmer; Hanno Ackermann; Daniel Dijkman; Taco Cohen; Pim de Haan (CoRR, 2022)

Rigid Body Flows for Sampling Molecular Crystal Structures. - Jonas Köhler 001; Michele Invernizzi; Pim de Haan; Frank Noé (ICML, 2023)

EDGI: Equivariant Diffusion for Planning with Embodied Agents. - Johann Brehmer; Joey Bose; Pim de Haan; Taco S. Cohen (NeurIPS, 2023)

Geometric Algebra Transformer. - Johann Brehmer; Pim de Haan; Sönke Behrends; Taco S. Cohen (NeurIPS, 2023)

Geometric Algebra Transformers. - Johann Brehmer; Pim de Haan; Sönke Behrends; Taco Cohen (CoRR, 2023)

FoMo Rewards: Can we cast foundation models as reward functions? - Ekdeep Singh Lubana; Johann Brehmer; Pim de Haan; Taco Cohen (CoRR, 2023)

Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers. - Pim de Haan; Taco Cohen; Johann Brehmer (AISTATS, 2024)

Noether's Razor: Learning Conserved Quantities. - Tycho F. A. van der Ouderaa; Mark van der Wilk; Pim de Haan (NeurIPS, 2024)

Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics. - Jonas Spinner; Victor Bresó; Pim de Haan; Tilman Plehn; Jesse Thaler; Johann Brehmer (NeurIPS, 2024)

Mesh neural networks for SE(3)-equivariant hemodynamics estimation on the artery wall. - Julian Suk; Pim de Haan; Phillip Lippe; Christoph Brune; Jelmer M. Wolterink (Comput. Biol. Medicine, 2024)

Continuous normalizing flows for lattice gauge theories. - Mathis Gerdes; Pim de Haan; Roberto Bondesan; Miranda C. N. Cheng (CoRR, 2024)

A Lorentz-Equivariant Transformer for All of the LHC. - Johann Brehmer; Victor Bresó; Pim de Haan; Tilman Plehn; Huilin Qu; Jonas Spinner; Jesse Thaler (CoRR, 2024)

Deconfounding Imitation Learning with Variational Inference. - Risto Vuorio; Pim de Haan; Johann Brehmer; Hanno Ackermann; Daniel Dijkman; Taco Cohen (Trans. Mach. Learn. Res., 2024)

Does equivariance matter at scale? - Johann Brehmer; Sönke Behrends; Pim de Haan; Taco Cohen (Trans. Mach. Learn. Res., 2025)

BLIPs: Bayesian Learned Interatomic Potentials. - Dario Coscia; Pim de Haan; Max Welling (CoRR, 2025)