Jens Behrmann
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Invertible residual networks
J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen
Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019
Residual flows for invertible generative modeling
RTQ Chen, J Behrmann, DK Duvenaud, JH Jacobsen
Advances in Neural Information Processing Systems, 9916-9926, 2019
Excessive Invariance Causes Adversarial Vulnerability
JH Jacobsen, J Behrmann, R Zemel, M Bethge
International Conference on Learning Representations (ICLR), 2019
Deep learning for tumor classification in imaging mass spectrometry
J Behrmann, C Etmann, T Boskamp, R Casadonte, J Kriegsmann, P Maaβ
Bioinformatics 34 (7), 1215-1223, 2018
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
F Tramèr, J Behrmann, N Carlini, N Papernot, JH Jacobsen
arXiv preprint arXiv:2002.04599, 2020
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks
J Behrmann, S Dittmer, P Fernsel, P Maaß
arXiv preprint arXiv:1806.09730, 2018
Understanding and mitigating exploding inverses in invertible neural networks
J Behrmann, P Vicol, KC Wang, R Grosse, JH Jacobsen
arXiv preprint arXiv:2006.09347, 2020
Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction
A Denker, M Schmidt, J Leuschner, P Maass, J Behrmann
arXiv preprint arXiv:2006.06270, 2020
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data
C Etmann, M Schmidt, J Behrmann, T Boskamp, L Hauberg-Lotte, A Peter, ...
arXiv preprint arXiv:1912.05459, 2019
Principles of Neural Network Architecture Design: Invertibility and Domain Knowledge
J Behrmann
Universität Bremen, PhD thesis, 2019
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