The Taylor Decomposition: A Unified Generalization of the Oaxaca Method to Nonlinear Models

By S. Bazen, Xavier Joutard. 2013

29 citations recorded by the source corpus.

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Methods for interpreting and understanding deep neural networks (2017, methodology): “The Taylor decomposition [9, 5] is a method that explains the model’s decision by decomposing the function value f(x) as a sum of relevance scores.”
Explaining nonlinear classification decisions with deep Taylor decomposition (2015, methodology): “The approach was described by Bazen and Joutard [38] as a nonlinear generalization of the Oaxaca method in econometrics [21].”
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond (2020, methodology): “This includes the basic Taylor decomposition procedure [13], [17] and as well as an extension of it, the Deep Taylor Decomposition [99].”