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2020 – today
- 2024
[i14]Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Marco Cuturi, Jörn-Henrik Jacobsen:
Addressing Misspecification in Simulation-based Inference through Data-driven Calibration. CoRR abs/2405.08719 (2024)- [i13]Laura Manduchi, Antoine Wehenkel, Jens Behrmann, Luca Pegolotti, Andy C. Miller, Ozan Sener, Marco Cuturi, Guillermo Sapiro, Jörn-Henrik Jacobsen:
Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers. CoRR abs/2412.17542 (2024) - 2023
- [j2]Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Jörn-Henrik Jacobsen:
Robust Hybrid Learning With Expert Augmentation. Trans. Mach. Learn. Res. 2023 (2023) - [i12]Antoine Wehenkel, Jens Behrmann, Andrew C. Miller, Guillermo Sapiro, Ozan Sener, Marco Cuturi, Jörn-Henrik Jacobsen:
Simulation-based Inference for Cardiovascular Models. CoRR abs/2307.13918 (2023) - 2022
- [i11]Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Jörn-Henrik Jacobsen:
Robust Hybrid Learning With Expert Augmentation. CoRR abs/2202.03881 (2022) - 2021
- [c5]Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B. Grosse, Jörn-Henrik Jacobsen:
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks. AISTATS 2021: 1792-1800 - [i10]Niklas Koenen, Marvin N. Wright, Peter Maaß, Jens Behrmann:
Generalization of the Change of Variables Formula with Applications to Residual Flows. CoRR abs/2107.04346 (2021) - 2020
- [c4]Florian Tramèr
, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen:
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. ICML 2020: 9561-9571 - [i9]Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen:
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. CoRR abs/2002.04599 (2020) - [i8]Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B. Grosse, Jörn-Henrik Jacobsen:
Understanding and mitigating exploding inverses in invertible neural networks. CoRR abs/2006.09347 (2020)
2010 – 2019
- 2019
- [b1]Jens Behrmann:
Principles of Neural Network Architecture Design - Invertibility and Domain Knowledge. University of Bremen, Germany, 2019 - [c3]Jörn-Henrik Jacobsen, Jens Behrmann, Richard S. Zemel, Matthias Bethge:
Excessive Invariance Causes Adversarial Vulnerability. ICLR (Poster) 2019 - [c2]Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen:
Invertible Residual Networks. ICML 2019: 573-582 - [c1]Tian Qi Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen:
Residual Flows for Invertible Generative Modeling. NeurIPS 2019: 9913-9923 - [i7]Jörn-Henrik Jacobsen, Jens Behrmann, Nicholas Carlini, Florian Tramèr, Nicolas Papernot:
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness. CoRR abs/1903.10484 (2019) - [i6]Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen:
Residual Flows for Invertible Generative Modeling. CoRR abs/1906.02735 (2019) - [i5]Christian Etmann, Maximilian Schmidt, Jens Behrmann, Tobias Boskamp, Lena Hauberg-Lotte, Annette Peter, Rita Casadonte, Jörg Kriegsmann, Peter Maass:
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data. CoRR abs/1912.05459 (2019) - 2018
- [j1]Jens Behrmann, Christian Etmann, Tobias Boskamp, Rita Casadonte, Jörg Kriegsmann, Peter Maaß:
Deep learning for tumor classification in imaging mass spectrometry. Bioinform. 34(7): 1215-1223 (2018) - [i4]Jens Behrmann, Sören Dittmer, Pascal Fernsel, Peter Maaß:
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks. CoRR abs/1806.09730 (2018) - [i3]Jörn-Henrik Jacobsen, Jens Behrmann, Richard S. Zemel, Matthias Bethge:
Excessive Invariance Causes Adversarial Vulnerability. CoRR abs/1811.00401 (2018) - [i2]Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen:
Invertible Residual Networks. CoRR abs/1811.00995 (2018) - 2017
- [i1]Jens Behrmann, Christian Etmann, Tobias Boskamp, Rita Casadonte, Jörg Kriegsmann, Peter Maass:
Deep Learning for Tumor Classification in Imaging Mass Spectrometry. CoRR abs/1705.01015 (2017)
Coauthor Index
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