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Pim de Haan
Pim de Haan
Qualcomm AI Research, University of Amsterdam
Bestätigte E-Mail-Adresse bei uva.nl - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Causal confusion in imitation learning
P De Haan, D Jayaraman, S Levine
NeurIPS 2019, 2019
2902019
Gauge equivariant mesh cnns: Anisotropic convolutions on geometric graphs
P De Haan, M Weiler, T Cohen, M Welling
ICLR 2021, 2020
1062020
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
832018
Natural graph networks
P de Haan, T Cohen, M Welling
NeurIPS 2020, 2020
792020
Reparameterizing Distributions on Lie Groups
L Falorsi, P de Haan, TR Davidson, P Forré
AISTATS 2019, 2019
792019
Weakly supervised causal representation learning
J Brehmer*, P De Haan*, P Lippe, T Cohen
NeurIPS 2022, 2022
722022
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
192021
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
152019
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
142021
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
M Gerdes, P de Haan, C Rainone, R Bondesan, MCN Cheng
SciPost Physics, 2023
9*2023
Geometric Algebra Transformers
J Brehmer, P De Haan, S Behrends, T Cohen
NeurIPS 2023, 2023
92023
EDGI: Equivariant Diffusion for Planning with Embodied Agents
J Brehmer, J Bose, P De Haan, T Cohen
NeurIPS 2023, 2023
82023
Topological Constraints on Homeomorphic Auto-Encoding
P de Haan, L Falorsi
NeurIPS 2018 Workshop on Integration of Deep Learning Theories, 2018
82018
Mesh Neural Networks for SE (3)-Equivariant Hemodynamics Estimation on the Artery Wall
J Suk, P de Haan, P Lippe, C Brune, JM Wolterink
arXiv preprint arXiv:2212.05023, 2022
72022
Scaling up machine learning for quantum field theory with equivariant continuous flows
P de Haan, C Rainone, MCN Cheng, R Bondesan
arXiv preprint arXiv:2110.02673, 0
7
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
62021
Rigid body flows for sampling molecular crystal structures
J Köhler, M Invernizzi, P de Haan, F Noé
ICML 2023, 2023
42023
Deconfounded Imitation Learning
R Vuorio, J Brehmer, H Ackermann, D Dijkman, T Cohen, P de Haan
arXiv preprint arXiv:2211.02667, 2022
32022
Efficient machine learning message passing on point cloud data
DE Pim, TS Cohen
US Patent App. 18/326,800, 2024
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
2023
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