Bayesian inversion for tomography through machine learning

The talk will outline recent approaches for using (deep) convolutional neural networks to solve a wide range of inverse problems, such as tomographic image reconstruction. Emphasis is on learned iterative schemes that use a neural network architecture for reconstruction that includes physics based models for how data is generated. The talk will also discuss recent developments in using generative adversarial networks for uncertainty quantification in inverse problems.

• Speaker: Ozan Öktem (KTH Stockholm and Alan Turing Institute)
• Thursday 07 March 2019, 15:00–16:00
• Venue: MR 14.
• Series: Applied and Computational Analysis; organiser: Carola-Bibiane Schoenlieb.