H.B. Keller Colloquium

Monday February 22, 2021 4:00 PM

On Learning Kernels for Numerical Approximation and Learning

Speaker: Houman Owhadi, Computing and Mathematical Sciences, California Institute of Technology
Location: Online Event

There is a growing interest in solving numerical approximation problems as learning problems. Popular approaches can be divided into (1) Kernel methods, and (2) methods based on variants of Artificial Neural Networks. We illustrate the importance of using adapted kernels in kernel methods and discuss strategies for learning kernels from data.  We show how ANN methods can be formulated and analyzed as (1) kernel methods with warping kernels learned from data (2) discretized solvers for a generalization of image registration algorithms in which images are replaced by high dimensional shapes

Series H. B. Keller Colloquium Series

Contact: Diana Bohler at 626-232-6138 dbohler@caltech.edu