3.2k citations · 7.4k across the 37 of their papers we have counts for
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Interpreting Black Box Predictions using Fisher Kernels
Rajiv Khanna, Been Kim, Joydeep Ghosh +1
Research in both machine learning and psychology suggests that salient examples can help humans to interpret learning models. To this end, we take a novel look at black box interpr…
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…
Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018)
Been Kim, Kush R. Varshney, Adrian Weller
This is the Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), which was held in Stockholm, Sweden, July 14, 2018. Invited speakers wer…
xGEMs: Generating Examplars to Explain Black-Box Models
Shalmali Joshi, Oluwasanmi Koyejo, Been Kim +1
This work proposes xGEMs or manifold guided exemplars, a framework to understand black-box classifier behavior by exploring the landscape of the underlying data manifold as data po…
A Benchmark for Interpretability Methods in Deep Neural Networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans +1
We propose an empirical measure of the approximate accuracy of feature importance estimates in deep neural networks. Our results across several large-scale image classification dat…