Jens Behrmann
Cited by
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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
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
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
Understanding and mitigating exploding inverses in invertible neural networks
J Behrmann, P Vicol, KC Wang, R Grosse, JH Jacobsen
International Conference on Artificial Intelligence and Statistics, 1792-1800, 2021
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
Robust subtyping of non‐small cell lung cancer whole sections through MALDI mass spectrometry imaging
C Janßen, T Boskamp, L Hauberg‐Lotte, J Behrmann, SO Deininger, ...
PROTEOMICS–Clinical Applications, 2100068, 2022
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks
J Behrmann, S Dittmer, P Fernsel, P Maaß
arXiv preprint arXiv:1806.09730, 2018
Robust Hybrid Learning With Expert Augmentation
A Wehenkel, J Behrmann, H Hsu, G Sapiro, G Louppe, JH Jacobsen
arXiv preprint arXiv:2202.03881, 2022
Simulation-based Inference for Cardiovascular Models
A Wehenkel, J Behrmann, AC Miller, G Sapiro, O Sener, M Cuturi, ...
arXiv preprint arXiv:2307.13918, 2023
Generalization of the change of variables formula with applications to residual flows
N Koenen, MN Wright, P Maaß, J Behrmann
arXiv preprint arXiv:2107.04346, 2021
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
Improved Mass Calibration in MALDI MSI Using Neural Network-Based Recalibration
A Denker, J Behrmann, T Boskamp
Analytical Chemistry 96 (19), 7542-7549, 2024
Improving Generalization with Physical Equations
A Wehenkel, J Behrmann, H Hsu, G Sapiro, G Louppe, JH Jacobsen
Machine Learning and the Physical Sciences: Workshop at the 36th Conference …, 2022
Principles of Neural Network Architecture Design: Invertibility and Domain Knowledge
J Behrmann
Universität Bremen, PhD thesis, 2019
Inferring Cardiovascular Biomarkers with Hybrid Model Learning
O Senouf, J Behrmann, JH Jacobsen, P Frossard, E Abbe, A Wehenkel
NeurIPS 2023 Workshop on Deep Learning and Inverse Problems, 0
Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
A Wehenkel, JL Gamella, O Sener, J Behrmann, G Sapiro, M Cuturi, ...
arXiv preprint arXiv:2405.08719, 2024
Purity Assessment of Pellets using Deep Learning
J Behrmann, M Schmidt, J Wildner, P Maass
German Success Stories in Industrial Mathematics, Mathematics in Industry 35 …, 2022
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