Spotlight

Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings

Jan Macdonald · Mathieu Besançon · Sebastian Pokutta

Room 318 - 320
[ Abstract ] [ Livestream: Visit Social Aspects/MISC ]
Wed 20 Jul 7:30 a.m. — 7:35 a.m. PDT
[ Slides [ Paper PDF

We study the effects of constrained optimization formulations and Frank-Wolfe algorithms for obtaining interpretable neural network predictions. Reformulating the Rate-Distortion Explanations (RDE) method for relevance attribution as a constrained optimization problem provides precise control over the sparsity of relevance maps. This enables a novel multi-rate as well as a relevance-ordering variant of RDE that both empirically outperform standard RDE and other baseline methods in a well-established comparison test. We showcase several deterministic and stochastic variants of the Frank-Wolfe algorithm and their effectiveness for RDE.

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