6 citations · 8 across the 10 of their papers we have counts for
3 papers · 1 filter
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…
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…
Planning in a recurrent neural network that plays Sokoban
Mohammad Taufeeque, Philip Quirke, Maximilian Li +4
Planning is essential for solving complex tasks, yet the internal mechanisms underlying planning in neural networks remain poorly understood. Building on prior work, we analyze a r…