Trainable shrinkage splines: inverse problems meet deep learning
Ha Nguyen, EPFL STI LIB

Meeting • 28 June 2016 • BM 4 233

Abstract
In this talk, I will briefly review the recent approach in inverse problems in which not only the dictionaries but also the proximal mappings (shrinkage functions) are learned from the data. I'll discuss about the similarity between this approach and deep neural nets. I'll also share with you some of my theoretical observations about the proximal mappings and explain why splines provide good representations for such mappings. Finally, I'll present some of my initial experiments to give you an idea of what a learned shrinkage spline looks like.