| On the spectral bias of neural networks N Rahaman, A Baratin, D Arpit, F Draxler, M Lin, F Hamprecht, Y Bengio, ... International conference on machine learning, 5301-5310, 2019 | 1310 | 2019 |
| Essentially no barriers in neural network energy landscape F Draxler, K Veschgini, M Salmhofer, F Hamprecht International conference on machine learning, 1309-1318, 2018 | 411 | 2018 |
| Framework for easily invertible architectures (FrEIA) L Ardizzone, T Bungert, F Draxler, U Köthe, J Kruse, R Schmier, ... Source code, 2018 | 21 | 2018 |
| Whitening convergence rate of coupling-based normalizing flows F Draxler, C Schnörr, U Köthe Advances in Neural Information Processing Systems 35, 37241-37253, 2022 | 9 | 2022 |
| Lifting architectural constraints of injective flows P Sorrenson, F Draxler, A Rousselot, S Hummerich, L Zimmermann, ... The Twelfth International Conference on Learning Representations, 2024 | 5* | 2024 |
| Free-form flows: Make any architecture a normalizing flow F Draxler, P Sorrenson, L Zimmermann, A Rousselot, U Köthe International Conference on Artificial Intelligence and Statistics, 2197-2205, 2024 | 4 | 2024 |
| On the convergence rate of gaussianization with random rotations F Draxler, L Kühmichel, A Rousselot, J Müller, C Schnörr, U Köthe International Conference on Machine Learning, 8449-8468, 2023 | 3 | 2023 |
| Characterizing the Role of a Single Coupling Layer in Affine Normalizing Flows F Draxler, J Schwarz, C Schnörr, U Köthe DAGM German Conference on Pattern Recognition, 1-14, 2020 | 3 | 2020 |
| On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows F Draxler, S Wahl, C Schnoerr, U Koethe Forty-first International Conference on Machine Learning, 0 | 3* | |
| Finding competence regions in domain generalization J Müller, ST Radev, R Schmier, F Draxler, C Rother, U Köthe arXiv preprint arXiv:2303.09989, 2023 | 2 | 2023 |
| Riemannian SOS-Polynomial Normalizing Flows J Schwarz, F Draxler, U Köthe, C Schnörr Pattern Recognition: 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen …, 2021 | 1 | 2021 |
| Bose-Einstein condensate experiment as a nonlinear block of a machine learning pipeline M Hans, E Kath, M Sparn, N Liebster, H Strobel, MK Oberthaler, F Draxler, ... Physical Review Research 6 (1), 013122, 2024 | | 2024 |
| Learning Distributions on Manifolds with Free-form Flows P Sorrenson, F Draxler, A Rousselot, S Hummerich, U Köthe arXiv preprint arXiv:2312.09852, 2023 | | 2023 |
| Bose Einstein condensate as nonlinear block of a Machine Learning pipeline M Hans, E Kath, M Sparn, N Liebster, F Draxler, C Schnörr, H Strobel, ... arXiv preprint arXiv:2304.14905, 2023 | | 2023 |
