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Explaining the output of a complicated machine learning model like a deep neural network (DNN) is a central challenge in machine learning.
How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert MÞller · 2010
Earlier work this paper cites.
On random weights and unsupervised feature learning
Andrew M Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, and Andrew Y Ng · 2011
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Making machine learning models interpretable
Alfredo Vellido, José David Martín-Guerrero, and Paulo JG Lisboa · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
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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.
Grad-cam: Why did you say that?
Ramprasaath R Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2016
Earlier work this paper cites.
Not just a black box: Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje · 2016
Cited alongside, same era.
A unified view of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2017
Cited alongside, same era.
Real time image saliency for black box classifiers
Piotr Dabkowski and Yarin Gal · 2017
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
Cited alongside, same era.
Accountability of ai under the law: The role of explanation
Finale Doshi-Velez, Mason Kortz, Ryan Budish, Chris Bavitz, Sam Gershman, David O’Brien, Stuart Schieber, James Waldo, David Weinberger, and Alexandra Wood · 2017
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Later among the works it cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2017
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Adrian Weller · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
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Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2017
Cited alongside, same era.
Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks
Paras Lakhani and Baskaran Sundaram · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling · 2017
Later among the works it cites.
Learning how to explain neural networks: Patternnet and patternattribution
Maximilian Alber Klaus-Robert Müller Dumitru Erhan Been Kim Sven Dähne Pieter-Jan Kindermans, Kristof T. Schütt · 2018
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Scalable and accurate deep learning for electronic health records
Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M Dai, Nissan Hajaj, Peter J Liu, Xiaobing Liu, Mimi Sun, Patrik Sundberg, Hector Yee, et al · 2018
Closest in time.
Mukund Sundararajan and Ankur Taly · 2018
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