DRAFT
Caltech Logo

CMX Lunch Seminar

Wednesday, January 18, 2023
12:00pm to 1:00pm
Add to Cal
Annenberg 213
On the maximum and expected complexity of maxout networks
Guido Montúfar, Associate Professor of Mathematics and Statistics, Departments of Mathematics and Statistics, University of California Los Angeles,

Learning with artificial neural networks relies on the complexity of the representable functions and their parameters. In this talk I present results on the maximum and expected number of linear regions of the functions represented by neural networks with maxout units. In the first part, I discuss counting formulas and sharp upper bounds for the number of linear regions, with connections to Minkowski sums of polytopes. This is based on work with Yue Ren and Leon Zhang. In the second part, I discuss the behavior for generic parameters and present upper bounds on the expected number of regions given a probability distribution over the parameters and parameter initialization strategies. This is based on work with Hanna Tseran.

For more information, please contact Jolene Brink by phone at (626)395-2813 or by email at [email protected] or visit CMX Website.