ReLU Neural Network

Sharp Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces

Assistant Professor Jonathan Siegel, Texas A and M University

Nov 22, 15:30 - 17:00

B1 L3 R3119

ReLU Neural Network Sobolev Spaces

Sobolev spaces are centrally important objects in PDE theory. Consequently, to understand how deep neural networks can be used to numerically solve PDEs a necessary first step is to determine now efficiently they can approximate Sobolev functions. In this talk we consider this problem for deep ReLU neural networks, which are the most important class of neural networks in practical applications.