Many-body approach to the dynamics of batch learning

TL;DR

This paper develops a theoretical framework using the cavity method and diagrammatic techniques to analyze the dynamics of batch learning, accounting for temporal correlations and applicable to various cost functions.

Contribution

It introduces a novel many-body approach to model batch learning dynamics, incorporating temporal correlations and general cost functions.

Findings

Effective modeling of batch learning dynamics

Inclusion of temporal correlations in analysis

Applicable to a wide range of cost functions

Abstract

Using the cavity method and diagrammatic methods, we model the dynamics of batch learning of restricted sets of examples, widely applicable to general learning cost functions, and fully taking into account the temporal correlations introduced by the recycling of the examples.