20 citations · 49 across the 15 of their papers we have counts for
6 papers · 1 filter
On the role of memorization in learned priors for geophysical inverse problems
Ali Siahkoohi, Davide Sabeddu
Learned priors based on deep generative models offer data-driven regularization for seismic inversion, but training them requires a dataset of representative subsurface models -- a…
Conditional neural control variates for variance reduction in Bayesian inverse problems
Ali Siahkoohi, Hyunwoo Oh
Bayesian inference for inverse problems involves computing expectations under posterior distributions--e.g., posterior means, variances, or predictive quantities--typically via Mon…
Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems
Lorenzo Baldassari, Ali Siahkoohi, Josselin Garnier +2
This work introduces a sampling method capable of solving Bayesian inverse problems in function space. It does not assume the log-concavity of the likelihood, meaning that it is co…
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…
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…
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…