Johann Brehmer

According to our database, Johann Brehmer authored at least 19 papers between 2018 and 2024.

Collaborative distances:

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

2024

Does equivariance matter at scale?

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

CoRR, 2024

Probabilistic and Differentiable Wireless Simulation with Geometric Transformers.

Thomas Hehn, Markus Peschl, Tribhuvanesh Orekondy, Arash Behboodi, Johann Brehmer

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

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

Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set.

Ties van Rozendaal, Johann Brehmer, Yunfan Zhang, Reza Pourreza, Auke J. Wiggers, Taco Cohen

Trans. Mach. Learn. Res., 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

2022

Deconfounded Imitation Learning.

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

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

Implicit Neural Video Compression.

Yunfan Zhang, Ties van Rozendaal, Johann Brehmer, Markus Nagel, Taco Cohen

CoRR, 2021

Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set.

Ties van Rozendaal, Johann Brehmer, Yunfan Zhang, Reza Pourreza, Taco S. Cohen

CoRR, 2021

2020

MadMiner: Machine Learning-Based Inference for Particle Physics.

Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer

Comput. Softw. Big Sci., December, 2020

Hierarchical clustering in particle physics through reinforcement learning.

Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, Kyle Cranmer

CoRR, 2020

Flows for simultaneous manifold learning and density estimation.

Johann Brehmer, Kyle Cranmer

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

2019

The frontier of simulation-based inference.

Kyle Cranmer, Johann Brehmer, Gilles Louppe

CoRR, 2019

2018

Likelihood-free inference with an improved cross-entropy estimator.

Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

CoRR, 2018

Mining gold from implicit models to improve likelihood-free inference.

Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

CoRR, 2018