| Invertible residual networks J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019 | 732 | 2019 |
| Residual flows for invertible generative modeling RTQ Chen, J Behrmann, DK Duvenaud, JH Jacobsen Advances in Neural Information Processing Systems, 9916-9926, 2019 | 429 | 2019 |
| Excessive Invariance Causes Adversarial Vulnerability JH Jacobsen, J Behrmann, R Zemel, M Bethge International Conference on Learning Representations (ICLR), 2019 | 198 | 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 | 147* | 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 | 143 | 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 | 128 | 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 | 22 | 2020 |
| Robust Hybrid Learning With Expert Augmentation A Wehenkel, J Behrmann, H Hsu, G Sapiro, G Louppe, JH Jacobsen arXiv preprint arXiv:2202.03881, 2022 | 14 | 2022 |
| 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 | 13 | 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 | 12 | 2023 |
| Analysis of Invariance and Robustness via Invertibility of ReLU-Networks J Behrmann, S Dittmer, P Fernsel, P Maaß arXiv preprint arXiv:1806.09730, 2018 | 12 | 2018 |
| 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 | 8 | 2024 |
| 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 | 4 | 2019 |
| 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 | 3 | 2022 |
| 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 | 2 | 2021 |
| 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 | 2 | |
| Improved Mass Calibration in MALDI MSI Using Neural Network-Based Recalibration A Denker, J Behrmann, T Boskamp Analytical Chemistry 96 (19), 7542-7549, 2024 | 1 | 2024 |
| Principles of Neural Network Architecture Design: Invertibility and Domain Knowledge J Behrmann Universität Bremen, PhD thesis, 2019 | 1 | 2019 |
| Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers L Manduchi, A Wehenkel, J Behrmann, L Pegolotti, AC Miller, O Sener, ... arXiv preprint arXiv:2412.17542, 2024 | | 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 | | 2022 |
Jörn-Henrik JacobsenApple AI/ML Research在 vectorinstitute.ai 的電子郵件地址已通過驗證