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Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Expressive classifiers such as neural networks are among the most accurate supervised learning methods in use today, but their opaque decision boundaries make them difficult to trust in critical applications. We propose a method to explain the predictions of any differentiable model via the gradient of the class label with respect to the input (which provides a normal to the decision boundary). Not only is this approach orders of magnitude faster at identifying input dimensions of high
doi:10.24963/ijcai.2017/371
dblp:conf/ijcai/RossHD17
fatcat:jqvkqmahtfftjk7b3janfdkglu