de Haan, Pim

20 publications

TMLR 2025 Does Equivariance Matter at Scale? Johann Brehmer, Sönke Behrends, Pim De Haan, Taco Cohen

TMLR 2024 Deconfounding Imitation Learning with Variational Inference Risto Vuorio, Pim De Haan, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen

NeurIPSW 2024 Does Equivariance Matter at Scale? Johann Brehmer, Sönke Behrends, Pim De Haan, Taco Cohen

ICMLW 2024 Geometric Algebra Transformers for Large 3D Meshes via Cross-Attention Julian Suk, Pim De Haan, Baris Imre, Jelmer M. Wolterink

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 Noether's Razor: Learning Conserved Quantities Tycho F. A. van der Ouderaa, Mark van der Wilk, Pim de Haan

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

ICLRW 2023 EDGI: Equivariant Diffusion for Planning with Embodied Agents Johann Brehmer, Joey Bose, Pim De Haan, Taco Cohen

NeurIPSW 2023 Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers Pim De Haan, Taco Cohen, Johann Brehmer

NeurIPSW 2023 FoMo Rewards: Can We Cast Foundation Models as Reward Functions? Ekdeep Singh Lubana, Johann Brehmer, Pim De Haan, Taco Cohen

NeurIPS 2023 Geometric Algebra Transformer Johann Brehmer, Pim de Haan, Sönke Behrends, Taco S Cohen

ICMLW 2023 Geometric Algebra Transformers Johann Brehmer, Pim De Haan, Sönke Behrends, Taco Cohen

ICML 2023 Rigid Body Flows for Sampling Molecular Crystal Structures Jonas Köhler, Michele Invernizzi, Pim De Haan, Frank Noe

NeurIPSW 2022 Deconfounded Imitation Learning Risto Vuorio, Pim De Haan, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen

NeurIPS 2022 Weakly Supervised Causal Representation Learning Johann Brehmer, Pim de Haan, Phillip Lippe, Taco S Cohen

ICLRW 2022 Weakly Supervised Causal Representation Learning Johann Brehmer, Pim De Haan, Phillip Lippe, Taco Cohen

ICLR 2021 Gauge Equivariant Mesh CNNs: Anisotropic Convolutions on Geometric Graphs Pim De Haan, Maurice Weiler, Taco Cohen, Max Welling

NeurIPS 2020 Natural Graph Networks Pim de Haan, Taco S Cohen, Max Welling

NeurIPS 2019 Causal Confusion in Imitation Learning Pim de Haan, Dinesh Jayaraman, Sergey Levine

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