Deep learning over the moduli space of quiver representations

Abstract: It is interesting to observe that neural network in machine learning has a similar basic setup as quiver representation theory. In this talk, I will build an algebro-geometric formulation of a computing machine, which is well-defined over the moduli space of representations. I will also explain a uniformization between spherical, Euclidean and hyperbolic moduli of framed quiver representations, and construct a learning algorithm over these moduli spaces.

machine learningalgebraic geometrynumber theory

Audience: researchers in the topic

DANGER: Data, Numbers, and Geometry