20 citations · 68 across the 18 of their papers we have counts for
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stat.ML2021★ 20 cited
Preconditioned training of normalizing flows for variational inference in inverse problems
Ali Siahkoohi, Gabrio Rizzuti, Mathias Louboutin +2
Obtaining samples from the posterior distribution of inverse problems with expensive forward operators is challenging especially when the unknowns involve the strongly heterogeneou…
stat.ML2020★ 16 cited
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows
Ali Siahkoohi, Gabrio Rizzuti, Philipp A. Witte +1
In inverse problems, we often have access to data consisting of paired samples where are partial observations of a physical system, and represents…
stat.ML2020
A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification
Ali Siahkoohi, Gabrio Rizzuti, Felix J. Herrmann
Uncertainty quantification is essential when dealing with ill-conditioned inverse problems due to the inherent nonuniqueness of the solution. Bayesian approaches allow us to determ…