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Understanding neural networks is becoming increasingly important.
Kernel analysis of deep networks
Gregoire Montavon, Mikio L Braun, and Klaus-Robert Müller · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
On the interpretation of weight vectors of linear models in multivariate neuroimaging
Stefan Haufe, Frank Meinecke, Kai Görgen, Sven Dähne, John-Dylan Haynes, Benjamin Blankertz, and Felix Bießmann · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Cited alongside, same era.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
Later among the works it cites.
Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Bach, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2015
Later among the works it cites.
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