Biblio

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Author Title [ Type(Desc)] Year
Filters: Keyword is Inverse problems  [Clear All Filters]
Conference
Rafael Orozco, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, and Felix J. Herrmann, Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification, in SPIE Medical Imaging Conference, 2023.
Ali Siahkoohi, Gabrio Rizzuti, Mathias Louboutin, Philipp A. Witte, and Felix J. Herrmann, Deep Bayesian Inference for Task-based Seismic Imaging, in KAUST, 2021.
Felix J. Herrmann and Curt Da Silva, Domain-specific abstractions for full-waveform inversion, in SIAM Conference on Computational Science and Engineering, 2017.
Bas Peters and Felix J. Herrmann, Generalized Minkowski sets for the regularization of inverse problems, in SIAM Conference on Mathematical and Computational Issues in the Geosciences, 2019.
Bas Peters and Felix J. Herrmann, Intersections and sums of sets for the regularization of inverse problems, in Canadian Mathematical Society Winter Meeting, 2018.
Ali Siahkoohi, Gabrio Rizzuti, Mathias Louboutin, Philipp A. Witte, and Felix J. Herrmann, Preconditioned training of normalizing flows for variational inference in inverse problems, in 3rd Symposium on Advances in Approximate Bayesian Inference, 2021.
Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco, and Felix J. Herrmann, Reliable amortized variational inference with conditional normalizing flows via physics-based latent distribution correction, in IMAGE Workshop on Subsurface Uncertainty Description and Estimation - Moving Away from Single Prediction with Distribution Learning, 2022.