Hierarchical Symbolic Reasoning in Hyperbolic Space for Deep Discriminative Models

Understand

Explanations for \emph{black-box} models help us understand model decisions as well as provide information on model biases and inconsistencies.

• Most of the current explainability techniques provide a single level of explanation, often in terms of feature importance scores or feature attention maps in input space.
• Our focus is on explaining deep discriminative models at \emph{multiple levels of abstraction}, from fine-grained to fully abstract explanations.
• We achieve this by using the natural properties of \emph{hyperbolic geometry} to more efficiently model a hierarchy of symbolic features and generate \emph{hierarchical symbolic rules} as part of our explanations.