SL(2, Z)-Equivariant Machine Learning with Modular Forms - Theory and Applications

Explore the intersection of machine learning and modular forms theory in this 23-minute conference talk from GSI. Delve into the concept of SL(2, Z)-equivariant machine learning and its applications, gaining insights into how this mathematical approach can enhance AI models. Learn about the fundamental principles of modular forms and their relevance to equivariant neural networks. Discover potential use cases and practical implementations of this advanced technique in various fields of study and industry applications.

SL(2, Z)-Equivariant Machine Learning with Modular Forms Theory and Applications