Description
Chair: Shirley Ho
An outstanding issue is to understand how deep networks circumvent the curse of dimensionality to generate or classify data. Inspired by the renormalization group in physics, we explain how deep networks can separate phenomena which appear at different scales, and capture scale interactions. It provides high-dimensional model, which approximate the probability distribution of complex physical fields such as turbulences or structured images. For classification, learning becomes similar to a compressed sensing problem, where low-dimensional discriminative structures are identified with random projections. I will introduce a multiscale random model of deep networks for classification, and its numerical validation.
