Machine Learning Solutions for Inverse Problems: Part A - 9780443417894

Machine Learning Solutions for Inverse Problems: Part AAuthor(s): Michael Hintermller, Andreas Hauptmann, Bangti Jin, Carola-Bibiane Schnlieb\nFormat: Hardback\nPublisher: Elsevier Science Publishing Co Inc, United States\nImprint: Academic Press Inc\nISBN-13: 9780443417894, 978-0443417894\nSynopsis\nMachine Learning Solutions for Inverse Problems: Part A, Volume 26 in the Handbook of Numerical Analysis, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Data-Driven Approaches for Generalized Lasso Problems, Implicit Regularization of the Deep Inverse Prior via (Inertial) Gradient Flow, Generalized Hardness of Approximation, Hallucinations, and Trustworthiness in Machine Learning for Inverse Problems, Energy-Based Models for Inverse Imaging Problems, Regularization Theory of Stochastic Iterative Methods for Solving Inverse Problems, and more.\n\nOther sections cover Advances in Identifying Differential Ee