Rafael Orozco, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, Felix J. Herrmann. Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification. In Olivier Colliot, Ivana Isgum, editors, Medical Imaging 2023: Image Processing, San Diego, CA, USA, February 19-23, 2023. Volume 12464 of SPIE Proceedings, SPIE, 2023. [doi]
@inproceedings{OrozcoSRLH23,
title = {Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification},
author = {Rafael Orozco and Ali Siahkoohi and Gabrio Rizzuti and Tristan van Leeuwen and Felix J. Herrmann},
year = {2023},
doi = {10.1117/12.2651691},
url = {https://doi.org/10.1117/12.2651691},
researchr = {https://researchr.org/publication/OrozcoSRLH23},
cites = {0},
citedby = {0},
booktitle = {Medical Imaging 2023: Image Processing, San Diego, CA, USA, February 19-23, 2023},
editor = {Olivier Colliot and Ivana Isgum},
volume = {12464},
series = {SPIE Proceedings},
publisher = {SPIE},
isbn = {9781510660342},
}