The Passive Symmetries of Machine Learning
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the concept of passive symmetries in machine learning through this insightful one-hour lecture by Soledad Villar, Assistant Professor at Johns Hopkins University. Delve into the implications of arbitrary investigator choices in data representation and their corresponding exact symmetries. Examine the role of passive symmetries in machine learning, including permutation symmetry in graph neural networks, and learn about best practices in implementation. Discover the conditions for implementing passive symmetries as group equivariances and understand their connections to causal modeling. Gain valuable insights on how passive symmetries can enhance out-of-sample generalization in learning problems. This talk, part of the Data-Driven Physical Simulations (DDPS) webinar series, offers a unique perspective on the intersection of mathematics, physics, and machine learning.