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Debugging Tests for Model Explanations
Julius Adebayo, Michael Muelly, Ilaria Liccardi +1
We investigate whether post-hoc model explanations are effective for diagnosing model errors--model debugging. In response to the challenge of explaining a model's prediction, a va…
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values
Julius Adebayo, Justin Gilmer, Ian Goodfellow +1
Explaining the output of a complicated machine learning model like a deep neural network (DNN) is a central challenge in machine learning. Several proposed local explanation method…
Sanity Checks for Saliency Maps
Julius Adebayo, Justin Gilmer, Michael Muelly +3
Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model. Several saliency methods have been proposed…