A self-explainable operator learning method reformulates operators as decomposable integral equations to reveal spatial input contributions to predictions in blood flow and aerodynamics problems.
arXiv preprint arXiv:1901.04592 (2019)
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GALE aggregates local explanations to reveal global model behavior, showing that LIME's global importance measure is unreliable while the proposed aggregations better capture how features affect predictions.
Explainability techniques applied to LundNet show that assigned node importance correlates with classical jet substructure observables such as N-subjettiness ratios and energy correlation functions, with shifts across transverse-momentum regimes.
citing papers explorer
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Self-explainable Operator Learning for Discovering Spatial Patterns in Functional Data
A self-explainable operator learning method reformulates operators as decomposable integral equations to reveal spatial input contributions to predictions in blood flow and aerodynamics problems.
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Global Aggregations of Local Explanations for Black Box models
GALE aggregates local explanations to reveal global model behavior, showing that LIME's global importance measure is unreliable while the proposed aggregations better capture how features affect predictions.
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Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane
Explainability techniques applied to LundNet show that assigned node importance correlates with classical jet substructure observables such as N-subjettiness ratios and energy correlation functions, with shifts across transverse-momentum regimes.