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physics.data-an2026
Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
Rikab Gambhir, Luisa Lucie-Smith, Jesse Thaler
We review the concepts of interpretability and explainability as they apply to machine learning in physics. We define interpretability as concerning the structural transparency of…
physics.data-an2022★ 24 cited
A robust estimator of mutual information for deep learning interpretability
Davide Piras, Hiranya V. Peiris, Andrew Pontzen +3
We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning models. To accurately estimate MI f…