Pim de Haan

According to our database, Pim de Haan authored at least 23 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Does equivariance matter at scale?

Johann Brehmer, Sönke Behrends, Pim de Haan, Taco Cohen

CoRR, 2024

Continuous normalizing flows for lattice gauge theories.

Mathis Gerdes, Pim de Haan, Roberto Bondesan, Miranda C. N. Cheng

CoRR, 2024

Noether's razor: Learning Conserved Quantities.

Tycho F. A. van der Ouderaa, Mark van der Wilk, Pim de Haan

CoRR, 2024

Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics.

Jonas Spinner, Victor Bresó, Pim de Haan, Tilman Plehn, Jesse Thaler, Johann Brehmer

CoRR, 2024

Mesh neural networks for SE(3)-equivariant hemodynamics estimation on the artery wall.

Julian Suk, Pim de Haan, Phillip Lippe, Christoph Brune, Jelmer M. Wolterink

Comput. Biol. Medicine, 2024

Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers.

Pim de Haan, Taco Cohen, Johann Brehmer

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

FoMo Rewards: Can we cast foundation models as reward functions?

Ekdeep Singh Lubana, Johann Brehmer, Pim de Haan, Taco Cohen

CoRR, 2023

Geometric Algebra Transformers.

Johann Brehmer, Pim de Haan, Sönke Behrends, Taco Cohen

CoRR, 2023

Geometric Algebra Transformer.

Johann Brehmer, Pim de Haan, Sönke Behrends, Taco S. Cohen

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

EDGI: Equivariant Diffusion for Planning with Embodied Agents.

Johann Brehmer, Joey Bose, Pim de Haan, Taco S. Cohen

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Rigid Body Flows for Sampling Molecular Crystal Structures.

Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé

Proceedings of the International Conference on Machine Learning, 2023

2022

Deconfounded Imitation Learning.

Risto Vuorio, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen, Pim de Haan

CoRR, 2022

Learning Lattice Quantum Field Theories with Equivariant Continuous Flows.

Mathis Gerdes, Pim de Haan, Corrado Rainone, Roberto Bondesan, Miranda C. N. Cheng

CoRR, 2022

Weakly supervised causal representation learning.

Johann Brehmer, Pim de Haan, Phillip Lippe, Taco S. Cohen

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows.

Pim de Haan, Corrado Rainone, Miranda C. N. Cheng, Roberto Bondesan

CoRR, 2021

Mesh Convolutional Neural Networks for Wall Shear Stress Estimation in 3D Artery Models.

Julian Suk, Pim de Haan, Phillip Lippe, Christoph Brune, Jelmer M. Wolterink

Proceedings of the Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge, 2021

Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs.

Pim de Haan, Maurice Weiler, Taco Cohen, Max Welling

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Natural Graph Networks.

Pim de Haan, Taco S. Cohen, Max Welling

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Covariance in Physics and Convolutional Neural Networks.

Miranda C. N. Cheng, Vassilis Anagiannis, Maurice Weiler, Pim de Haan, Taco S. Cohen, Max Welling

CoRR, 2019

Causal Confusion in Imitation Learning.

Pim de Haan, Dinesh Jayaraman, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Reparameterizing Distributions on Lie Groups.

Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Topological Constraints on Homeomorphic Auto-Encoding.

Pim de Haan, Luca Falorsi

CoRR, 2018

Explorations in Homeomorphic Variational Auto-Encoding.

Luca Falorsi, Pim de Haan, Tim R. Davidson, Nicola De Cao, Maurice Weiler, Patrick Forré, Taco S. Cohen

CoRR, 2018