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cs.LG2024
An Overview of Prototype Formulations for Interpretable Deep Learning
Maximilian Xiling Li, Korbinian Franz Rudolf, Paul Mattes +2
Prototypical part networks offer interpretable alternatives to black-box deep learning models by learning visual prototypes for classification. This work provides a comprehensive a…
cs.LG2024★ 2 cited
Optimal ablation for interpretability
Maximilian Li, Lucas Janson
Interpretability studies often involve tracing the flow of information through machine learning models to identify specific model components that perform relevant computations for…