On local entropy, stochastic control and deep neural networks

TL;DR

This paper explores the connections between local entropy, stochastic control, and deep neural networks, providing rigorous theoretical insights that could guide future machine learning models and understanding.

Contribution

It establishes rigorous links between smoothing energy landscapes, score-based generative models, and classical stochastic control theory, offering new theoretical frameworks.

Findings

Connections between energy landscape smoothing and stochastic control clarified

Rigorous representations provided for score-based generative models

Guidelines proposed for future learning model development

Abstract

In this paper, we connect some recent papers on smoothing of energy landscapes and scored-based generative models of machine learning to classical work in stochastic control. We clarify these connections providing rigorous statements and representations which may serve as guidelines for further learning models.