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Pim de Haan
CuspAI
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Causal confusion in imitation learning
P De Haan, D Jayaraman, S Levine
NeurIPS 2019, 2019
3652019
Gauge equivariant mesh cnns: Anisotropic convolutions on geometric graphs
P De Haan, M Weiler, T Cohen, M Welling
ICLR 2021, 2020
1322020
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
1252018
Weakly supervised causal representation learning
J Brehmer*, P De Haan*, P Lippe, T Cohen
NeurIPS 2022, 2022
1242022
Natural graph networks
P de Haan, T Cohen, M Welling
NeurIPS 2020, 2020
1002020
Reparameterizing Distributions on Lie Groups
L Falorsi, P de Haan, TR Davidson, P Forré
AISTATS 2019, 2019
892019
Geometric Algebra Transformers
J Brehmer*, P De Haan*, S Behrends, T Cohen
NeurIPS 2023, 2023
472023
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
46*2024
EDGI: Equivariant Diffusion for Planning with Embodied Agents
J Brehmer, J Bose, P De Haan, T Cohen
NeurIPS 2023, 2023
252023
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
252021
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
M Gerdes*, P de Haan*, C Rainone, R Bondesan, MCN Cheng
SciPost Physics, 2023
22*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
192019
Rigid body flows for sampling molecular crystal structures
J Köhler, M Invernizzi, P de Haan, F Noé
ICML 2023, 2023
182023
Topological Constraints on Homeomorphic Auto-Encoding
P de Haan, L Falorsi
NeurIPS 2018 Workshop on Integration of Deep Learning Theories, 2018
102018
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers
P De Haan, T Cohen, J Brehmer
AISTATS 2024, 2023
92023
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
7*2024
Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
J Spinner, V Bresó, P de Haan, T Plehn, J Thaler, J Brehmer
NeurIPS 2024, 2024
62024
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
3*2023
A Lorentz-Equivariant Transformer for All of the LHC
J Brehmer, V Bresó, P de Haan, T Plehn, H Qu, J Spinner, J Thaler
arXiv preprint arXiv:2411.00446, 2024
12024
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
12024
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