Bayesian Inference with Plug-and-Play Priors - Imaging and Inverse Problems Seminar
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore cutting-edge Plug & Play (PnP) methods in Bayesian imaging through this 53-minute seminar presented by Marcelo Pereyra from Heriot-Watt University. Delve into the theory, methods, and provably convergent algorithms for performing Bayesian inference with PnP priors. Learn about two innovative algorithms: PnP Unadjusted Langevin Algorithm for Monte Carlo sampling and MMSE inference, and PnP Play Stochastic Gradient Descent for MAP inference. Discover how these algorithms target a decision-theoretically optimal Bayesian model and their applications in canonical problems. Gain insights into convergence guarantees, denoising operators, and deep neural networks in imaging inverse problems. Engage with topics such as variational approaches, infinite dimensions, basin frameworks, oracle models, and gradient algorithms through practical illustrations and examples.