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The problem of explaining deep learning models, and model predictions generally, has attracted intensive interest recently.
Revisiting the importance of individual units in cnns via ablation
Bolei Zhou, Yiyou Sun, David Bau, , and Antonio Torralba · 1909
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MNIST dataset, 1998
Yann LeCun · 1998
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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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An efficient explanation of individual classifications using game theory
Erik Strumbelj and Igor Kononenko · 2010
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2015
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The mythos of model interpretability
Zachary C. Lipton · 2016
Earlier work this paper cites.
”why should I trust you?”: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
Learning how to explain neural networks: Patternnet and patternattribution
Pieter-Jan Kindermans, Kristof T. Schütt, Klaus-Robert Müller Maximilian Alber, Dumitru Erhan, Been Kim, and Sven Dahne · 2017
Earlier work this paper cites.
Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2017
Cited alongside, same era.
Ontology-based deep learning for human behavior prediction with explanations in health social networks
Nhathai Phan, Dejing Dou, Hao Wang, David Kil, and Brigitte Piniewski · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
Ruth Fong and Andrea Vedaldi · 2018
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Evaluating feature importance estimates
Sara Hooker, Dumitru Erhan, Pieter jan Kindermans, and Been Kim · 2018
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Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
Later among the works it cites.
Noise-adding methods of saliency map as series of higher order partial derivative
Junghoon Seo, Jeongyeol Choe, Jamyoung Koo, Seunghyeon Jeon, Beomsu Kim, and Taegyun Jeon · 2018
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Learning global additive explanations for neural nets using model distillation
Sarah Tan, Rich Caruana, Giles Hooker, Paul Koch, and Albert Gordo · 2018
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L-shapley and c-shapley: Efficient model interpretation for structured data
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Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
Cited alongside, same era.
Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Oztireli, and Markus Gross · 2018
Cited alongside, same era.
Jianbo Chen, Le Song, Martin J. Wainwright, and Michael I. Jordan · 2019
Closest in time.
What can AI do for me? evaluating machine learning interpretations in cooperative play
Shi Feng and Jordan Boyd-Graber · 2019
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Global explanations of neural networks: Mapping the landscape of predictions
Mark Ibrahim, Melissa Louie, Ceena Modarres, and John Paisley · 2019
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Explainable, normative, and justified agency
Pat Langley · 2019
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