Biblio
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Author Title Type [ Year
] Filters: Keyword is Inverse problems and Author is Ali Siahkoohi [Clear All Filters]
, “Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification”, in SPIE Medical Imaging Conference, 2023.
, “Reliable amortized variational inference with physics-based latent distribution correction”, Geophysics, vol. 88, 2023.
, “Adjoint operators as summary functions in amortized Bayesian inference frameworks”, ML4SEISMIC Partners Meeting. 2022.
, “Deep generative models for solving geophysical inverse problems”, Georgia Institute of Technology, Atlanta, 2022.
, “Low-cost uncertainty quantification for large-scale inverse problems”, ML4SEISMIC Partners Meeting. 2022.
, “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.
, “Deep Bayesian Inference for Task-based Seismic Imaging”, in KAUST, 2021.
, “Preconditioned training of normalizing flows for variational inference in inverse problems”, in 3rd Symposium on Advances in Approximate Bayesian Inference, 2021.