Abstract
In this letter, 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.
Original language | English (US) |
---|---|
Pages (from-to) | 437-441 |
Number of pages | 5 |
Journal | IEEE Control Systems Letters |
Volume | 7 |
DOIs | |
State | Published - 2023 |
Keywords
- machine learning
- neural networks
- Stochastic optimal control
ASJC Scopus subject areas
- Control and Systems Engineering
- Control and Optimization