M. U. Khalid

A survey of theory-grounded interpretability for deep neural networks

Deep neural networks achieve state-of-the-art performance across computer vision, natural language processing, and scientific discovery, yet their internal decision-making mechanisms remain difficult to interpret. Post-hoc explanation methods such as SHAP, saliency maps, and attention visualization provide empirical in...