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2020 – today
- 2024
[j1]Julian Suk
, Pim de Haan, Phillip Lippe, Christoph Brune, Jelmer M. Wolterink:
Mesh neural networks for SE(3)-equivariant hemodynamics estimation on the artery wall. Comput. Biol. Medicine 173: 108328 (2024)- [c10]Pim de Haan, Taco Cohen, Johann Brehmer:
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers. AISTATS 2024: 3088-3096 - [i19]Jonas Spinner, Victor Bresó, Pim de Haan, Tilman Plehn, Jesse Thaler, Johann Brehmer:
Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics. CoRR abs/2405.14806 (2024) - 2023
- [c9]Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé:
Rigid Body Flows for Sampling Molecular Crystal Structures. ICML 2023: 17301-17326 - [c8]Johann Brehmer, Joey Bose, Pim de Haan, Taco S. Cohen:
EDGI: Equivariant Diffusion for Planning with Embodied Agents. NeurIPS 2023 - [c7]Johann Brehmer, Pim de Haan, Sönke Behrends, Taco S. Cohen:
Geometric Algebra Transformer. NeurIPS 2023 - [i18]Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé:
Rigid body flows for sampling molecular crystal structures. CoRR abs/2301.11355 (2023) - [i17]Johann Brehmer, Joey Bose, Pim de Haan, Taco Cohen:
EDGI: Equivariant Diffusion for Planning with Embodied Agents. CoRR abs/2303.12410 (2023) - [i16]Johann Brehmer, Pim de Haan, Sönke Behrends, Taco Cohen:
Geometric Algebra Transformers. CoRR abs/2305.18415 (2023) - [i15]Pim de Haan, Taco Cohen, Johann Brehmer:
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers. CoRR abs/2311.04744 (2023) - [i14]Ekdeep Singh Lubana, Johann Brehmer, Pim de Haan, Taco Cohen:
FoMo Rewards: Can we cast foundation models as reward functions? CoRR abs/2312.03881 (2023) - 2022
- [c6]Johann Brehmer, Pim de Haan, Phillip Lippe, Taco S. Cohen:
Weakly supervised causal representation learning. NeurIPS 2022 - [i13]Johann Brehmer, Pim de Haan, Phillip Lippe, Taco Cohen:
Weakly supervised causal representation learning. CoRR abs/2203.16437 (2022) - [i12]Mathis Gerdes, Pim de Haan, Corrado Rainone, Roberto Bondesan, Miranda C. N. Cheng:
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows. CoRR abs/2207.00283 (2022) - [i11]Risto Vuorio, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen, Pim de Haan:
Deconfounded Imitation Learning. CoRR abs/2211.02667 (2022) - [i10]Julian Suk, Pim de Haan, Phillip Lippe, Christoph Brune, Jelmer M. Wolterink:
Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall. CoRR abs/2212.05023 (2022) - 2021
- [c5]Pim de Haan, Maurice Weiler, Taco Cohen, Max Welling:
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs. ICLR 2021 - [c4]Julian Suk, Pim de Haan, Phillip Lippe, Christoph Brune, Jelmer M. Wolterink:
Mesh Convolutional Neural Networks for Wall Shear Stress Estimation in 3D Artery Models. STACOM@MICCAI 2021: 93-102 - [i9]Julian Suk, Pim de Haan, Phillip Lippe, Christoph Brune, Jelmer M. Wolterink:
Mesh convolutional neural networks for wall shear stress estimation in 3D artery models. CoRR abs/2109.04797 (2021) - [i8]Pim de Haan, Corrado Rainone, Miranda C. N. Cheng, Roberto Bondesan:
Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows. CoRR abs/2110.02673 (2021) - 2020
- [c3]Pim de Haan, Taco S. Cohen, Max Welling:
Natural Graph Networks. NeurIPS 2020 - [i7]Pim de Haan, Maurice Weiler, Taco Cohen, Max Welling:
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs. CoRR abs/2003.05425 (2020) - [i6]Pim de Haan, Taco Cohen, Max Welling:
Natural Graph Networks. CoRR abs/2007.08349 (2020)
2010 – 2019
- 2019
- [c2]Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré:
Reparameterizing Distributions on Lie Groups. AISTATS 2019: 3244-3253 - [c1]Pim de Haan, Dinesh Jayaraman, Sergey Levine:
Causal Confusion in Imitation Learning. NeurIPS 2019: 11693-11704 - [i5]Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré:
Reparameterizing Distributions on Lie Groups. CoRR abs/1903.02958 (2019) - [i4]Pim de Haan, Dinesh Jayaraman, Sergey Levine:
Causal Confusion in Imitation Learning. CoRR abs/1905.11979 (2019) - [i3]Miranda C. N. Cheng, Vassilis Anagiannis, Maurice Weiler, Pim de Haan, Taco S. Cohen, Max Welling:
Covariance in Physics and Convolutional Neural Networks. CoRR abs/1906.02481 (2019) - 2018
- [i2]Luca Falorsi, Pim de Haan, Tim R. Davidson, Nicola De Cao, Maurice Weiler, Patrick Forré, Taco S. Cohen:
Explorations in Homeomorphic Variational Auto-Encoding. CoRR abs/1807.04689 (2018) - [i1]Pim de Haan, Luca Falorsi:
Topological Constraints on Homeomorphic Auto-Encoding. CoRR abs/1812.10783 (2018)
Coauthor Index
aka: Taco S. Cohen
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