Követés
Jakob Kruse
Jakob Kruse
Visual Learning Lab, Heidelberg University (HCI/IWR)
E-mail megerősítve itt: iwr.uni-heidelberg.de - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
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
5292018
Guided image generation with conditional invertible neural networks
L Ardizzone, C Lüth, J Kruse, C Rother, U Köthe
arXiv preprint arXiv:1907.02392, 2019
2782019
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
1032017
Benchmarking invertible architectures on inverse problems
J Kruse, L Ardizzone, C Rother, U Köthe
arXiv preprint arXiv:2101.10763, 2021
552021
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
42*2021
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
352021
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
332019
Framework for easily invertible architectures (FrEIA)
L Ardizzone, T Bungert, F Draxler, U Köthe, J Kruse, R Schmier, ...
Source code, 2018
182018
Technical report: Training mixture density networks with full covariance matrices
J Kruse
arXiv preprint arXiv:2003.05739, 2020
102020
Towards learned emulation of interannual water isotopologue variations in General Circulation Models
J Wider, J Kruse, N Weitzel, JC Bühler, U Köthe, K Rehfeld
Environmental Data Science 2, e35, 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
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