Inverse Problems under a Learned Generative Prior - Lecture 1
Explore the theoretical foundations of inverse problems using learned generative priors in this 50-minute lecture by Paul Hand at the International Centre for Theoretical Sciences. Delve into examples of inverse problems, sparsity as a common prior, and how generative models can impressively sample from complex signal classes. Examine the use of generative models in inverse problems, their state-of-the-art performance, and deep compressive sensing. Investigate random generative priors, their recovery guarantees, and favorable geometry for optimization in compressive sensing. Learn about the deterministic conditions for recovery and the provably convergent subgradient descent algorithm. Discover why generative models can outperform sparsity models, especially in compressive phase retrieval. Gain insights into the new workflow for scientists, concrete steps taken, and further theory needed in this field. The lecture concludes with main takeaways and a Q&A session, providing a comprehensive overview of this cutting-edge topic in machine learning theory. Read more