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Masking some input variables of a deep neural network (DNN) and computing output changes on the masked input sample represent a typical way to compute attributions of input variables in the sample.
A value for n-person games
Lloyd S Shapley · 1953
Earlier work this paper cites.
Introduction to bivariate and multivariate analysis
Pranab Kumar Sen, Richard H. Lindeman, Peter F. Merenda, and Ruth Z. Gold · 1981
Earlier work this paper cites.
A Simplified Bargaining Model for the n-Person Cooperative Game , pages 44–70
John C. Harsanyi · 1982
Earlier work this paper cites.
Probabilistic values for games , page 101–120
Robert James Weber · 1988
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
An axiomatic approach to the concept of interaction among players in cooperative games
Michel Grabisch and Marc Roubens · 1999
Earlier work this paper cites.
Protein interaction networks from yeast to human
Peer Bork, Lars J Jensen, Christian von Mering, Arun K Ramani, Insuk Lee, and Edward M Marcotte · 2004
Earlier work this paper cites.
Fair attribution of functional contribution in artificial and biological networks
Alon Keinan, Ben Sandbank, Claus C. Hilgetag, Isaac Meilijson, and Eytan Ruppin · 2004
Earlier work this paper cites.
Estimators of relative importance in linear regression based on variance decomposition
Ulrike Grömping · 2007
Earlier work this paper cites.
Polynomial calculation of the shapley value based on sampling
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Earlier work this paper cites.
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Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, and Bernhard Schölkopf · 2008
Earlier work this paper cites.
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Daria Sorokina, Rich Caruana, Mirek Riedewald, and Daniel Fink · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Causal inference in statistics: An overview
Judea Pearl · 2009
Earlier work this paper cites.
Explaining instance classifications with interactions of subsets of feature values
Erik Štrumbelj, Igor Kononenko, and M Robnik Šikonja · 2009
Earlier work this paper cites.
Feature removal is a unifying principle for model explanation methods
Ian Covert, Scott Lundberg, and Su-In Lee · 2011
Earlier work this paper cites.
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Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
Earlier work this paper cites.
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Xi Song, Tianfu Wu, Yunde Jia, and Song-Chun Zhu · 2013
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Earlier work this paper cites.
Explaining prediction models and individual predictions with feature contributions
Erik Štrumbelj and Igo Kononenko · 2014
Earlier work this paper cites.
Values of Non-Atomic Games
Robert J. Aumann and Lloyd S. Shapley · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
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Towards hierarchical importance attribution: Explaining compositional semantics for neural sequence models
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UCI machine learning repository, 2017
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