Deep learning for tumor classification in imaging mass spectrometry. - Jens Behrmann; Christian Etmann; Tobias Boskamp; Rita Casadonte; Jörg Kriegsmann; Peter Maaß (Bioinform., 2018)

Analysis of Invariance and Robustness via Invertibility of ReLU-Networks. - Jens Behrmann; Sören Dittmer; Pascal Fernsel; Peter Maaß (CoRR, 2018)

Invertible Residual Networks. - Jens Behrmann; David Duvenaud; Jörn-Henrik Jacobsen (CoRR, 2018)

Excessive Invariance Causes Adversarial Vulnerability. - Jörn-Henrik Jacobsen; Jens Behrmann; Richard S. Zemel; Matthias Bethge (ICLR (Poster), 2019)

Invertible Residual Networks. - Jens Behrmann; Will Grathwohl; Ricky T. Q. Chen; David Duvenaud; Jörn-Henrik Jacobsen (ICML, 2019)

Residual Flows for Invertible Generative Modeling. - Tian Qi Chen; Jens Behrmann; David Duvenaud; Jörn-Henrik Jacobsen (NeurIPS, 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. - Ricky T. Q. Chen; Jens Behrmann; David Duvenaud; Jörn-Henrik Jacobsen (CoRR, 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)

Principles of Neural Network Architecture Design - Invertibility and Domain Knowledge. - Jens Behrmann (2019)

Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. - Florian Tramèr; Jens Behrmann; Nicholas Carlini; Nicolas Papernot; Jörn-Henrik Jacobsen (ICML, 2020)

Understanding and Mitigating Exploding Inverses in Invertible Neural Networks. - Jens Behrmann; Paul Vicol; Kuan-Chieh Wang; Roger B. Grosse; Jörn-Henrik Jacobsen (AISTATS, 2021)

Generalization of the Change of Variables Formula with Applications to Residual Flows. - Niklas Koenen; Marvin N. Wright; Peter Maaß; Jens Behrmann (CoRR, 2021)

Simulation-based Inference for Cardiovascular Models. - Antoine Wehenkel; Jens Behrmann; Andrew C. Miller; Guillermo Sapiro; Ozan Sener; Marco Cuturi; Jörn-Henrik Jacobsen (CoRR, 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)

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)

Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers. - Laura Manduchi; Antoine Wehenkel; Jens Behrmann; Luca Pegolotti; Andy C. Miller; Ozan Sener; Marco Cuturi; Guillermo Sapiro; Jörn-Henrik Jacobsen (CoRR, 2024)