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

Jan Macdonald; Mathieu Besançon; Sebastian Pokutta

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arXiv · 2110.08105

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

Jan Macdonald·Mathieu Besançon·Sebastian Pokutta

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Abstract

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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Jan Macdonald, Mathieu Besançon, Sebastian Pokutta. 2021-10-15. Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings. https://arxiv.org/abs/2110.08105

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