This talk describes some of the work done in collaboration with Fede Lopez, Anna Wienhard, Bea Pozetti and Michel Strube at the University of Heidelberg on applying the geometry of symmetric spaces to graph embedding problems in machine learning.

Versions

I’ve given a version of this at the USF math colloquium and the San Jose State math colloquium

Slides