Why and When Deep is Better than Shallow: An Implementation-Agnostic State-Transition View of Depth Supremacy
NeutralArtificial Intelligence
This article explores the advantages of deep models over shallow ones in a framework that doesn't depend on specific network implementations. It discusses how deep models can be understood as abstract state-transition semigroups and presents a bias-variance decomposition that highlights the role of depth in determining variance.
— Curated by the World Pulse Now AI Editorial System
