Abstract

Insights and tools developed by physicists to describe systems that evolve within complex, high-dimensional landscapes have led to the formulation of novel algorithms for a variety of problems in computer science. An especially tantalizing prospect is the possibility that a similar analogy could be developed to understand deep learning. This conference brings together computer scientists, probabilists and mathematicians as well as statistical physicists to document progress and identify opportunities at this interface.