| Analyzing inverse problems with invertible neural networks L Ardizzone, J Kruse, S Wirkert, D Rahner, EW Pellegrini, RS Klessen, ... arXiv preprint arXiv:1808.04730, 2018 | 465 | 2018 |
| Guided image generation with conditional invertible neural networks L Ardizzone, C Lüth, J Kruse, C Rother, U Köthe arXiv preprint arXiv:1907.02392, 2019 | 245 | 2019 |
| Learning to push the limits of efficient fft-based image deconvolution J Kruse, C Rother, U Schmidt Proceedings of the IEEE International Conference on Computer Vision, 4586-4594, 2017 | 95 | 2017 |
| Benchmarking invertible architectures on inverse problems J Kruse, L Ardizzone, C Rother, U Köthe arXiv preprint arXiv:2101.10763, 2021 | 47 | 2021 |
| HINT: Hierarchical invertible neural transport for density estimation and Bayesian inference J Kruse, G Detommaso, U Köthe, R Scheichl Proceedings of the AAAI Conference on Artificial Intelligence 35 (9), 8191-8199, 2021 | 37* | 2021 |
| Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks TJ Adler, L Ardizzone, A Vemuri, L Ayala, J Gröhl, T Kirchner, S Wirkert, ... International journal of computer assisted radiology and surgery, 1-11, 2019 | 29 | 2019 |
| Conditional invertible neural networks for diverse image-to-image translation L Ardizzone, J Kruse, C Lüth, N Bracher, C Rother, U Köthe Pattern Recognition: 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen …, 2021 | 26 | 2021 |
| Framework for Easily Invertible Architectures (FrEIA) L Ardizzone, J Kruse, SJ Wirkert, D Rahner, E Pellegrini, RS Klessen, ... Source code, 2018 | 10 | 2018 |
| Technical report: Training mixture density networks with full covariance matrices J Kruse arXiv preprint arXiv:2003.05739, 2020 | 8 | 2020 |
| Towards Learned Emulation of Interannual Water Isotopologue Variations in General Circulation Models J Wider, J Kruse, N Weitzel, JC Bühler, U Köthe, K Rehfeld arXiv preprint arXiv:2301.13462, 2023 | | 2023 |
| Conditional normalizing flow for predicting the occurrence of rare extreme events on long time scales J Kruse, B Ellerhoff, U Köthe, K Rehfeld EGU General Assembly Conference Abstracts, EGU22-8656, 2022 | | 2022 |
Ullrich KötheAdjunct Professor of Computer Science, University of HeidelbergVerified email at iwr.uni-heidelberg.de
