Title: MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis,
Abstract: Interpretability has emerged as a crucial aspect of machine learning, aimed
at providing insights into the working of complex neural networks. However,
existing solutions vary vastly based on the nature of the interpretability
task, with each use case requiring substantial time and effort. This paper
introduces MARGIN, a simple yet general approach to address a large set of
interpretability tasks ranging from identifying prototypes to explaining image
predictions. MARGIN exploits ideas rooted in graph signal analysis to determine
influential nodes in a graph, which are defined as those nodes that maximally
describe a function defined on the graph. By carefully defining task-specific
graphs and functions, we demonstrate that MARGIN outperforms existing
approaches in a number of disparate interpretability challenges.