PHYSICS-INSPIRED INTERPRETABILITY OF MACHINE LEARNING MODELS

Maximilian Paul Niroomand, David John Wales

Keywords: interpretability, physics, machine learning, neural networks

TL;DR: We use methods from the energy landscapes field to provide a new approach towards model interpretability by looking at the loss function landscape and show on 2 examples how it works.

Abstract: The ability to explain decisions made by machine learning models remains one of the most significant hurdles towards widespread adoption of AI in highly sensitive areas such as medicine, cybersecurity or autonomous driving. Great interest exists in understanding which features of the input data prompt model decision making. In this contribution, we propose a novel approach to identify relevant features of the input data, inspired by methods from the energy landscapes field, developed in the physical sciences. By identifying conserved weights within groups of minima of the loss function landscapes, we can identify the drivers of model decision making. Analogues to this idea exist in the molecular sciences, where coordinate invariants or order parameters are employed to identify critical features of a molecule. However, no such approach exists for machine learning loss function landscapes. We will demonstrate the applicability of energy landscape methods to machine learning models and give examples, both synthetic and from the real world, for how these methods can help to make models more interpretable.