A Free-Energy Principle for Representation Learning

ICML 2020

A Free-Energy Principle for Representation Learning

Jul 12, 2020
|
37 views
|
Details
This paper employs a formal connection of machine learning with thermodynamics to characterize the quality of learnt representations for transfer learning. We discuss how information-theoretic functional such as rate, distortion and classification loss of a model lie on a convex, so-called equilibrium surface.We prescribe dynamical processes to traverse this surface under constraints, e.g., an iso-classification process that trades off rate and distortion to keep the classification loss unchanged. We demonstrate how this process can be used for transferring representations from a source dataset to a target dataset while keeping the classification loss constant. Experimental validation of the theoretical results is provided on standard image-classification datasets. Speakers: Yansong Gao, Pratik Chaudhari

Comments
loading...