Bridging Control, Inference, Transport, and Thermodynamics: From Theory to Applications in Learning

Paper recorded by Signals 4 on 2026-09-14 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-14 on arXiv · recorded by Signals 4 on 2026-09-15

Category: cs.LG · 机器学习 · first seen 2026-09-15

Abstract

The last decade has seen the development of powerful methods for learning complex structure from high-dimensional data. These advances have brought to the foreground fundamental connections between subdisciplines of physics, applied mathematics, and machine learning. In this review, we bring together some of these ideas, often expressed in different languages, to highlight a conceptual thread that

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