Introduction to Equivariant Machine Learning - IPAM at UCLA

Introduction to Equivariant Machine Learning - IPAM at UCLA

Explore the fundamentals of equivariant machine learning in this 46-minute lecture presented by Soledad Villar from Johns Hopkins University at IPAM's Mathematical Advances for Multi-Dimensional Microscopy Workshop. Delve into the concept of machine learning models that respect the fundamental symmetries of physical descriptions. Gain insights into the implementation of these models and understand their significance in the field. Recorded on October 27, 2022, at the Institute for Pure & Applied Mathematics (IPAM) at UCLA, this talk provides a comprehensive introduction to the principles and applications of equivariant machine learning.