Johann Brehmer

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 22

Top Topics

Geometric Algebra

Weakly Supervised

Reward Function

Top Venues

Comput. Softw. Big Sci.

Publications

Jonas Spinner, Victor Bresó, Pim de Haan, Tilman Plehn, Jesse Thaler, Johann Brehmer
Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics. CoRR (2024)
Pim de Haan, Taco Cohen, Johann Brehmer
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers. AISTATS (2024)
Thomas Hehn, Markus Peschl, Tribhuvanesh Orekondy, Arash Behboodi, Johann Brehmer
Probabilistic and Differentiable Wireless Simulation with Geometric Transformers. CoRR (2024)
Pim de Haan, Taco Cohen, Johann Brehmer
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers. CoRR (2023)
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)
Johann Brehmer, Joey Bose, Pim de Haan, Taco S. Cohen
EDGI: Equivariant Diffusion for Planning with Embodied Agents. NeurIPS (2023)
Johann Brehmer, Joey Bose, Pim de Haan, Taco Cohen
EDGI: Equivariant Diffusion for Planning with Embodied Agents. CoRR (2023)
Ekdeep Singh Lubana, Johann Brehmer, Pim de Haan, Taco Cohen
FoMo Rewards: Can we cast foundation models as reward functions? CoRR (2023)
Johann Brehmer, Pim de Haan, Sönke Behrends, Taco S. Cohen
Geometric Algebra Transformer. NeurIPS (2023)
Johann Brehmer, Pim de Haan, Sönke Behrends, Taco Cohen
Geometric Algebra Transformers. CoRR (2023)
Johann Brehmer, Pim de Haan, Phillip Lippe, Taco Cohen
Weakly supervised causal representation learning. CoRR (2022)
Risto Vuorio, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen, Pim de Haan
Deconfounded Imitation Learning. CoRR (2022)
Johann Brehmer, Pim de Haan, Phillip Lippe, Taco S. Cohen
Weakly supervised causal representation learning. NeurIPS (2022)
Yunfan Zhang, Ties van Rozendaal, Johann Brehmer, Markus Nagel, Taco Cohen
Implicit Neural Video Compression. CoRR (2021)
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 (2021)
Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer
MadMiner: Machine Learning-Based Inference for Particle Physics. Comput. Softw. Big Sci. 4 (1) (2020)
Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, Kyle Cranmer
Hierarchical clustering in particle physics through reinforcement learning. CoRR (2020)
Johann Brehmer, Kyle Cranmer
Flows for simultaneous manifold learning and density estimation. NeurIPS (2020)
Johann Brehmer, Kyle Cranmer
Flows for simultaneous manifold learning and density estimation. CoRR (2020)
Kyle Cranmer, Johann Brehmer, Gilles Louppe
The frontier of simulation-based inference. CoRR (2019)
Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer
Likelihood-free inference with an improved cross-entropy estimator. CoRR (2018)
Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer
Mining gold from implicit models to improve likelihood-free inference. CoRR (2018)