| Backpack: Packing more into backprop F Dangel, F Kunstner, P Hennig 8th International Conference on Learning Representations (ICLR), 2019 | 78 | 2019 |
| Topological invariants in dissipative extensions of the Su-Schrieffer-Heeger model F Dangel, M Wagner, H Cartarius, J Main, G Wunner Physical Review A 98 (1), 013628, 2018 | 74 | 2018 |
| Numerical calculation of the complex berry phase in non-Hermitian systems M Wagner, F Dangel, H Cartarius, J Main, G Wunner arXiv preprint arXiv:1708.03230, 2017 | 22 | 2017 |
| Modular block-diagonal curvature approximations for feedforward architectures F Dangel, S Harmeling, P Hennig International Conference on Artificial Intelligence and Statistics, 799-808, 2020 | 16* | 2020 |
| ViViT: Curvature access through the generalized Gauss-Newton's low-rank structure F Dangel, L Tatzel, P Hennig Transactions on Machine Learning Research, 2022 | 4 | 2022 |
| Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks F Schneider, F Dangel, P Hennig Advances in Neural Information Processing Systems 34, 20825-20837, 2021 | 4 | 2021 |
| Mikroskopische Beschreibung eines Einkoppelprozesses für PT-symmetrische Bose-Einstein-Kondensate F Dangel | 2 | 2015 |
| Bosonic many-body systems with topologically nontrivial phases subject to gain and loss F Dangel | 1 | 2017 |
| The Geometry of Neural Nets' Parameter Spaces Under Reparametrization A Kristiadi, F Dangel, P Hennig arXiv preprint arXiv:2302.07384, 2023 | | 2023 |
| Backpropagation Beyond the Gradient F Dangel University of Tübingen, 2023 | | 2023 |
Philipp HennigUniversity of Tübingen & Max Planck Institute for Intelligent SystemsVerified email at tue.mpg.de
