TherML: Thermodynamics of Machine Learning

TL;DR

This paper introduces a thermodynamic framework for understanding various machine learning objectives, establishing a formal analogy that offers new insights into their properties and behaviors.

Contribution

It presents a novel thermodynamic perspective on machine learning objectives, linking concepts from physics to machine learning theory.

Findings

Provides a formal correspondence between thermodynamics and machine learning objectives

Offers new insights into the properties of learning objectives through thermodynamic analogy

Suggests potential implications for designing and analyzing machine learning algorithms

Abstract

In this work we offer a framework for reasoning about a wide class of existing objectives in machine learning. We develop a formal correspondence between this work and thermodynamics and discuss its implications.