| Disentanglement by nonlinear ICA with general incompressible-flow networks (GIN) P Sorrenson, C Rother, U Köthe arXiv preprint arXiv:2001.04872, 2020 | 131 | 2020 |
| Symmetries, safety, and self-supervision BM Dillon, G Kasieczka, H Olischlager, T Plehn, P Sorrenson, L Vogel SciPost Physics 12 (6), 188, 2022 | 61 | 2022 |
| Better latent spaces for better autoencoders BM Dillon, T Plehn, C Sauer, P Sorrenson SciPost Physics 11 (3), 061, 2021 | 59 | 2021 |
| Framework for easily invertible architectures (FrEIA) L Ardizzone, T Bungert, F Draxler, U Köthe, J Kruse, R Schmier, ... Source code, 2018 | 33* | 2018 |
| Jet Diffusion versus JetGPT--Modern Networks for the LHC A Butter, N Huetsch, SP Schweitzer, T Plehn, P Sorrenson, J Spinner arXiv preprint arXiv:2305.10475, 2023 | 29 | 2023 |
| A normalized autoencoder for LHC triggers BM Dillon, L Favaro, T Plehn, P Sorrenson, M Krämer SciPost Physics Core 6 (4), 074, 2023 | 24 | 2023 |
| 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 |
| Learning Distances from Data with Normalizing Flows and Score Matching P Sorrenson, D Behrend-Uriarte, C Schnörr, U Köthe arXiv preprint arXiv:2407.09297, 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 |
| Symmetries and self-supervision in particle physics BM Dillon, G Kasieczka, H Olischläger, T Plehn, P Sorrenson, L Vogel NeurIPS: Machine Learning and the Physical Sciences Workshop, 2021 | | 2021 |
Ullrich KötheAdjunct Professor of Computer Science, University of Heidelberg在 iwr.uni-heidelberg.de 的电子邮件经过验证
