Insights into machine learning models from chemical physics: an energy landscapes approach (EL for ML)
Abstract
The study of energy landscapes as a conceptual framework, and a source of novel computational tools, is an active area of research in chemistry and physics. The energy landscape provides insight into structure, dynamics, and thermodynamics when combined with tools from statistical mechanics and unimolecular rate theory. This approach can also be applied to questions that arise in machine learning. Here, the loss landscape (LL) of a machine learning system is treated in the same way as the energy landscape for a molecular system. In this contribution we summarise and discuss applications of energy landscapes for machine learning (EL4ML). We will outline how various physical properties find analogues in machine learning systems, and show how these properties can be employed to both increase understanding of the machine learning ‘black-box’ and enhance the performance of machine learning models.
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