Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint

23 September 2021

Papers citing "Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint"

7 / 7 papers shown

Title
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction Moritz Piening Fabian Altekrüger J. Hertrich Paul Hagemann Andrea Walther Gabriele Steidl 19 5 0 27 Dec 2023
A Review of Change of Variable Formulas for Generative Modeling Ullrich Kothe 11 6 0 04 Aug 2023
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems Fabian Altekrüger Paul Hagemann Gabriele Steidl TPM 16 9 0 28 Mar 2023
A theory of continuous generative flow networks Salem Lahlou T. Deleu Pablo Lemos Dinghuai Zhang Alexandra Volokhova Alex Hernández-García Léna Néhale Ezzine Yoshua Bengio Nikolay Malkin AI4CE 19 79 0 30 Jan 2023
Proximal Residual Flows for Bayesian Inverse Problems J. Hertrich BDL TPM 10 4 0 30 Nov 2022
Generalized Normalizing Flows via Markov Chains Paul Hagemann J. Hertrich Gabriele Steidl BDL DiffM AI4CE 14 22 0 24 Nov 2021
Invertible Neural Networks versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence A. Andrle N. Farchmin Paul Hagemann Sebastian Heidenreich V. Soltwisch Gabriele Steidl 58 16 0 05 Feb 2021