Neural Networks for Algebraists

Time

-

Locations

RE 242

Speaker: 

Sara Jamshidi Zelenberg

Illinois Institute of Technology

Visiting Assistant Professor of Applied Mathematics

Description: 

Neural networks are often treated as black-box methods for solving unknown problems. In this talk, however, we will attempt to make them a little more transparent, especially from an algebraic perspective. We'll discuss some of the considerations when attempting to use these tools in the field of algebra. We will also discuss what precisely we are doing when we use these tools; specifically what are we constructing from a geometric perspective when we use ReLU activations. The talk will primarily focus on feed-forward setups, but, time permitting, some discussion will be given to other setups, such as recursive neural networks. (Note: this talk is adapted from one given an MPI.)

 

Event Topic

Nonlinear Algebra and Statistics (NLASTATS)

Getting to Campus