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.
@article{arxiv.2204.13049,
title = {On local entropy, stochastic control and deep neural networks},
author = {Michele Pavon},
journal= {arXiv preprint arXiv:2204.13049},
year = {2022}
}