Invertible neural networks versus MCMC for posterior reconstruction in grazing incidence X-ray fluorescence

DOI10.1007/978-3-030-75549-2_42OpenAlexW3164142506MaRDI QIDQ826224

Nando Farchmin, Victor Soltwisch, Paul Hagemann, Anna Andrle, Sebastian Heidenreich, Gabriele Drauschke

Publication date: 20 December 2021

Full work available at URL: https://arxiv.org/abs/2102.03189

zbMATH Keywords

inverse problemMCMCBayesian inversionGIXRFinvertible neural networkstransport maps

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Monte Carlo methods (65C05) Computer science (68-XX)

Related Items (3)

Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint ⋮ WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution ⋮ A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows

Cites Work

• Unnamed Item
• An introduction to MCMC for machine learning
• Stabilizing Invertible Neural Networks Using Mixture Models

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