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
Advances in Neural Information Processing Systems 32, 2019
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
Gauge equivariant mesh cnns: Anisotropic convolutions on geometric graphs
P De Haan, M Weiler, T Cohen, M Welling
ICLR 2021, 2020
Reparameterizing Distributions on Lie Groups
L Falorsi, P de Haan, TR Davidson, P Forré
AISTATS 2019, 2019
Natural graph networks
P de Haan, T Cohen, M Welling
NeurIPS 2020, 2020
Mesh convolutional neural networks for wall shear stress estimation in 3D artery models
J Suk, P Haan, P Lippe, C Brune, JM Wolterink
International Workshop on Statistical Atlases and Computational Models of …, 2021
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
Topological Constraints on Homeomorphic Auto-Encoding
P de Haan, L Falorsi
NeurIPS 2018 Workshop on Integration of Deep Learning Theories, 2018
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
Weakly supervised causal representation learning
J Brehmer, P De Haan, P Lippe, T Cohen
ICLR 2022 Workshop on Objects, Structure and Causality, 2022
Equivariant graph neural networks as surrogate for computational fluid dynamics in 3d artery models
J Suk, P de Haan, P Lippe, C Brune, JM Wolterink
Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021), 2021
Deconfounded Imitation Learning
R Vuorio, J Brehmer, H Ackermann, D Dijkman, T Cohen, P de Haan
arXiv preprint arXiv:2211.02667, 2022
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
M Gerdes, P de Haan, C Rainone, R Bondesan, MCN Cheng
-equivariant hemodynamics estimation on arterial surface meshes using graph convolutional networks
J Suk, P De Haan, P Lippe, C Brune, JM Wolterink
Geometric Deep Learning in Medical Image Analysis (Extended abstracts), 2022
Gauge equivariant geometric graph convolutional neural network
DE Pim, M Weiler, TS Cohen, M Welling
US Patent App. 17/169,338, 2021
Gauge Equivariant Spherical CNNs
B Kicanaoglu, P de Haan, T Cohen
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