Speaker
David Gleich
(Purdue)
Description
It is now standard practice across science to use models that have been trained, fit, or learned based on a set of data. Many of these models involve a large number of parameters that make direct interpretation of the model challenging and a near black-box model view appropriate. We explore the possibilities of using ideas based on topological analysis methods to understand and evaluate these AI and ML-based functions. These show a surprising ability to generate easy to understand insights into these black-boxes.
