Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows

15 July 2020

Papers citing "Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows"

7 / 7 papers shown

Title
Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview Alexander Auras Kanchana Vaishnavi Gandikota Hannah Droege Michael Moeller AAML 29 0 0 19 Feb 2024
Globally injective and bijective neural operators Takashi Furuya Michael Puthawala Matti Lassas Maarten V. de Hoop 25 11 0 06 Jun 2023
Deep Variational Inverse Scattering AmirEhsan Khorashadizadeh A. Aghababaei Tin Vlavsić Hieu Nguyen Ivan Dokmanić BDL UQCV 33 3 0 08 Dec 2022
Deep Invertible Approximation of Topologically Rich Maps between Manifolds Michael Puthawala Matti Lassas Ivan Dokmanić Pekka Pankka Maarten V. de Hoop MedIm 20 0 0 02 Oct 2022
Deep Bayesian inference for seismic imaging with tasks Ali Siahkoohi G. Rizzuti Felix J. Herrmann BDL UQCV 30 21 0 10 Oct 2021
Learning by example: fast reliability-aware seismic imaging with normalizing flows Ali Siahkoohi Felix J. Herrmann OOD 27 13 0 13 Apr 2021
Preconditioned training of normalizing flows for variational inference in inverse problems Ali Siahkoohi G. Rizzuti M. Louboutin Philipp A. Witte Felix J. Herrmann 67 32 0 11 Jan 2021