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This arXiv preprint introduces GRALIS, a unified mathematical framework using Riesz Representation Theory to formalize and compare linear attribution methods like SHAP, LIME, and Integrated Gradients.
This paper introduces gammaILP, a fully differentiable framework for learning first-order rules directly from image data without label leakage, addressing challenges in symbol grounding and predicate invention.