MDS20 Minitutorial: Data-Driven Methods for Inverse Problems by Ozan Öktem

/ May 19, 2020/

Abstract: The mini-tutorial aims to provide a survey of different data-driven approaches to solve inverse problems with emphasis on deep learning based approaches that have gained widespread interest the last 2-3 years. The presentation will start out from the Bayesian approach to regularisation where a reconstruction method is represented by an estimator (decision rule). The various deep learning based approaches for solving inverse problems can now be seen as different ways to approximate estimators related to the posterior (deep direct estimation). Alternatively, one may also use trained deep neural networks to sample from the posterior (deep posterior sampling). The mini-tutorial will show examples of these methods in the context of tomographic imaging.

Ozan Öktem, KTH Stockholm, Sweden, [email protected]

This is one of six minitutorial talks organized by Carola-Bibiane Schönlieb (University of Cambridge, United Kingdom) and Ozan Öktem (KTH Stockholm, Sweden) under the title “Deep Learning for Inverse Problems and Partial Differential Equations” as part of the 2020 SIAM Conference on Mathematics of Data Science. For more information, visit https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=68214.