Industrial and Applied Math

Date: September 10, 2019

Time: 5:20PM - 6:20PM

Location: BLOC 220

Speaker: Boris Hanin, Texas A&M

Title: Hyperplane Arrangements in Deep Learning

Abstract: Deep learning is the study and application of neural networks. Each network is a non-linear family of functions, and such families form the backbone for many state of the art machine learning tasks ranging from computer vision to natural language processing. I will explain what a neural network is and will focus on a simple but important example called ReLU networks. The complexity of the function computed by a ReLU network is naturally captured by a generalization of a hyperplane arrangement. I will discuss this connection and will explain why the complexity of such arrangements is important for understanding how and why neural networks work in practice.