ILASP approximates neural networks for recipe preference learning as both global and local models, using weak constraints and PCA to maintain fidelity and interpretability.
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Explaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approach
ILASP approximates neural networks for recipe preference learning as both global and local models, using weak constraints and PCA to maintain fidelity and interpretability.