![Bites of FM4S: [1] Physics-inspired representations](/event/1473554/logo-3452117835.png){kind=logo}
Speaker
Philip Coleman Harris
(Massachusetts Inst. of Technology (US))
Description
With large amounts of data, a Higgs boson discovery, and world-leading constraints on an enormous amount of parameters and interactions, the Large Hadron Collider has been a phenomenal tool. We show new results built on contrastive learning and semi-supervised learning strategies where, through physics-motivated choices, we teach an AI to visualize many processes simultaneously, allowing it to solve a variety of downstream tasks in one algorithm. The implications are far-reaching. We discuss how this style of learning broadly allows us to simplify problems, and we point to new directions in representation learning that aim to further unite physics with ML.
