TherML: Thermodynamics of Machine Learning
arXiv:1807.04162v315.135 citations
Originality Incremental advance
AI Analysis
This work offers a foundational perspective for researchers in machine learning, potentially impacting how objectives are designed and analyzed across the field.
The authors introduced TherML, a framework that establishes a formal correspondence between thermodynamics and a broad class of machine learning objectives, aiming to provide a unified way to reason about these objectives.
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.