Pim de Haan
Pim de Haan
Qualcomm AI Research, University of Amsterdam
Verified email at - Homepage
Cited by
Cited by
Causal confusion in imitation learning
P De Haan, D Jayaraman, S Levine
NeurIPS 2019, 2019
Gauge equivariant mesh cnns: Anisotropic convolutions on geometric graphs
P De Haan, M Weiler, T Cohen, M Welling
ICLR 2021, 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
Weakly supervised causal representation learning
J Brehmer*, P De Haan*, P Lippe, T Cohen
NeurIPS 2022, 2022
Natural graph networks
P de Haan, T Cohen, M Welling
NeurIPS 2020, 2020
Reparameterizing Distributions on Lie Groups
L Falorsi, P de Haan, TR Davidson, P Forré
AISTATS 2019, 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
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
Geometric Algebra Transformers
J Brehmer*, P De Haan*, S Behrends, T Cohen
NeurIPS 2023, 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
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
M Gerdes, P de Haan, C Rainone, R Bondesan, MCN Cheng
SciPost Physics, 2023
Rigid body flows for sampling molecular crystal structures
J Köhler, M Invernizzi, P de Haan, F Noé
ICML 2023, 2023
EDGI: Equivariant Diffusion for Planning with Embodied Agents
J Brehmer, J Bose, P De Haan, T Cohen
NeurIPS 2023, 2023
Topological Constraints on Homeomorphic Auto-Encoding
P de Haan, L Falorsi
NeurIPS 2018 Workshop on Integration of Deep Learning Theories, 2018
Deconfounded imitation learning
R Vuorio, J Brehmer, H Ackermann, D Dijkman, T Cohen, P de Haan
arXiv preprint arXiv:2211.02667, 2022
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers
P De Haan, T Cohen, J Brehmer
AISTATS 2024, 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
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
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
Efficient machine learning message passing on point cloud data
DE Pim, TS Cohen
US Patent App. 18/326,800, 2024
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