Jens Behrmann

According to our database, Jens Behrmann authored at least 16 papers between 2018 and 2024.

Collaborative distances:

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

2024

Addressing Misspecification in Simulation-based Inference through Data-driven Calibration.

Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Marco Cuturi, Jörn-Henrik Jacobsen

CoRR, 2024

2023

Robust Hybrid Learning With Expert Augmentation.

Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Jörn-Henrik Jacobsen

Trans. Mach. Learn. Res., 2023

Simulation-based Inference for Cardiovascular Models.

Antoine Wehenkel, Jens Behrmann, Andrew C. Miller, Guillermo Sapiro, Ozan Sener, Marco Cuturi, Jörn-Henrik Jacobsen

CoRR, 2023

2021

Generalization of the Change of Variables Formula with Applications to Residual Flows.

Niklas Koenen, Marvin N. Wright, Peter Maaß, Jens Behrmann

CoRR, 2021

Understanding and Mitigating Exploding Inverses in Invertible Neural Networks.

Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B. Grosse, Jörn-Henrik Jacobsen

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations.

Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Principles of Neural Network Architecture Design - Invertibility and Domain Knowledge.

Jens Behrmann

PhD thesis, 2019

Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data.

Christian Etmann, Maximilian Schmidt, Jens Behrmann, Tobias Boskamp, Lena Hauberg-Lotte, Annette Peter, Rita Casadonte, Jörg Kriegsmann, Peter Maass

CoRR, 2019

Residual Flows for Invertible Generative Modeling.

Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen

CoRR, 2019

Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness.

Jörn-Henrik Jacobsen, Jens Behrmann, Nicholas Carlini, Florian Tramèr, Nicolas Papernot

CoRR, 2019

Residual Flows for Invertible Generative Modeling.

Tian Qi Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen

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

Invertible Residual Networks.

Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen

Proceedings of the 36th International Conference on Machine Learning, 2019

Excessive Invariance Causes Adversarial Vulnerability.

Jörn-Henrik Jacobsen, Jens Behrmann, Richard S. Zemel, Matthias Bethge

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Invertible Residual Networks.

Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen

CoRR, 2018

Analysis of Invariance and Robustness via Invertibility of ReLU-Networks.

Jens Behrmann, Sören Dittmer, Pascal Fernsel, Peter Maaß

CoRR, 2018

Deep learning for tumor classification in imaging mass spectrometry.

Jens Behrmann, Christian Etmann, Tobias Boskamp, Rita Casadonte, Jörg Kriegsmann, Peter Maaß

Bioinform., 2018