Big Data Inverse Problems - Promoting Sparsity and Learning to Regularize

Explore cutting-edge computational methods for solving inverse problems in data analytics, machine learning, and uncertainty quantification in this one-hour webinar. Delve into novel approaches for reconstructing quantities with sparse representation, focusing on L1 regularization techniques applicable to compressed sensing, dictionary learning, and imaging problems. Learn about a new method based on variable projection and discover how deep neural networks can be utilized to obtain regularization parameters for inverse problems. Engage in a discussion about the future of computational mathematics in the era of big data and machine learning, led by Associate Professor Matthias Chung from Emory University's Department of Mathematics. Gain insights into cross-disciplinary inverse problems, including scientific machine learning and iterative methods, and understand how challenges like ill-posedness, large-scale issues, and uncertainty estimates are addressed using advanced tools and techniques.

DDPS | Big Data Inverse Problems — Promoting Sparsity and Learning to Regularize by Mattias Chung