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Johann Brehmer
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2020 – today
- 2024
- [c5]Pim de Haan, Taco Cohen, Johann Brehmer:
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers. AISTATS 2024: 3088-3096 - [i16]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) - [i15]Thomas Hehn, Markus Peschl, Tribhuvanesh Orekondy, Arash Behboodi, Johann Brehmer:
Probabilistic and Differentiable Wireless Simulation with Geometric Transformers. CoRR abs/2406.14995 (2024) - [i14]Johann Brehmer, Sönke Behrends, Pim de Haan, Taco Cohen:
Does equivariance matter at scale? CoRR abs/2410.23179 (2024) - 2023
- [j2]Ties van Rozendaal, Johann Brehmer, Yunfan Zhang, Reza Pourreza, Auke J. Wiggers, Taco Cohen:
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set. Trans. Mach. Learn. Res. 2023 (2023) - [c4]Johann Brehmer, Joey Bose, Pim de Haan, Taco S. Cohen:
EDGI: Equivariant Diffusion for Planning with Embodied Agents. NeurIPS 2023 - [c3]Johann Brehmer, Pim de Haan, Sönke Behrends, Taco S. Cohen:
Geometric Algebra Transformer. NeurIPS 2023 - [i13]Johann Brehmer, Joey Bose, Pim de Haan, Taco Cohen:
EDGI: Equivariant Diffusion for Planning with Embodied Agents. CoRR abs/2303.12410 (2023) - [i12]Johann Brehmer, Pim de Haan, Sönke Behrends, Taco Cohen:
Geometric Algebra Transformers. CoRR abs/2305.18415 (2023) - [i11]Pim de Haan, Taco Cohen, Johann Brehmer:
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers. CoRR abs/2311.04744 (2023) - [i10]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
- [c2]Johann Brehmer, Pim de Haan, Phillip Lippe, Taco S. Cohen:
Weakly supervised causal representation learning. NeurIPS 2022 - [i9]Johann Brehmer, Pim de Haan, Phillip Lippe
, Taco Cohen:
Weakly supervised causal representation learning. CoRR abs/2203.16437 (2022) - [i8]Risto Vuorio, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen, Pim de Haan:
Deconfounded Imitation Learning. CoRR abs/2211.02667 (2022) - 2021
- [i7]Ties van Rozendaal, Johann Brehmer, Yunfan Zhang, Reza Pourreza, Taco S. Cohen:
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set. CoRR abs/2111.10302 (2021) - [i6]Yunfan Zhang, Ties van Rozendaal, Johann Brehmer, Markus Nagel, Taco Cohen:
Implicit Neural Video Compression. CoRR abs/2112.11312 (2021) - 2020
- [j1]Johann Brehmer
MadMiner: Machine Learning-Based Inference for Particle Physics. Comput. Softw. Big Sci. 4(1) (2020) - [c1]Johann Brehmer, Kyle Cranmer:
Flows for simultaneous manifold learning and density estimation. NeurIPS 2020 - [i5]Johann Brehmer, Kyle Cranmer:
Flows for simultaneous manifold learning and density estimation. CoRR abs/2003.13913 (2020) - [i4]Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, Kyle Cranmer:
Hierarchical clustering in particle physics through reinforcement learning. CoRR abs/2011.08191 (2020)
2010 – 2019
- 2019
- [i3]Kyle Cranmer
The frontier of simulation-based inference. CoRR abs/1911.01429 (2019) - 2018
- [i2]Johann Brehmer
Mining gold from implicit models to improve likelihood-free inference. CoRR abs/1805.12244 (2018) - [i1]Markus Stoye, Johann Brehmer
Likelihood-free inference with an improved cross-entropy estimator. CoRR abs/1808.00973 (2018)
Coauthor Index
aka: Taco S. Cohen
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