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Gradients have been used to quantify feature importance in machine learning models.
Values of Non-Atomic Games
R. J. Aumann and L. S. Shapley · 1974
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Building a large annotated corpus of english: The penn treebank
Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz · 1993
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
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Axiomatic attribution for multilinear functions
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Quoc V. Le · 2013
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Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Bach, and Klaus-Robert Müller · 2015
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Understanding neural networks through deep visualization
Jason Yosinski, Jeff Clune, Anh Mai Nguyen, Thomas Fuchs, and Hod Lipson · 2015
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http://www.darpa.mil/attachments/DARPA-BAA-16-53.pdf , 2016
Explainable Artificial Intelligence · 2016
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Layer-wise relevance propagation for neural networks with local renormalization layers
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Matthew D. Zeiler and Rob Fergus · 2014
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Inverting visual representations with convolutional networks, 2015
Alexey Dosovitskiy and Thomas Brox · 2015
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https://www.tensorflow.org/
TensorFlow
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”why should I trust you?”: Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh 0001, and Carlos Guestrin
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Model-agnostic interpretability of machine learning
Marco Túlio Ribeiro, Sameer Singh 0001, and Carlos Guestrin
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Molecular graph convolutions: moving beyond fingerprints
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