Structuring Neural Networks for More Explainable Predictions

By Laura Rieger, Pattarawat Chormai, Grgoire Montavon, Lars Kai Hansen, K. Müller. 2018

15 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “We will not attempt to cover all the related works many of which are already presented in the research and survey works we cite [1] [2] [9] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23].”
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021, background): “This can be explained by VGG-16 having a more explicit structure (standard pooling operations for VGG-16 versus strided convolution for ResNet-50), which better supports the process of relevance propagation (see also [149] for a discussion of the effect of structure on the performance of explanation”
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond (2020, background): “the process of relevance propagation (see also [117] for a discussion of the effect of structure on the performance of explanation methods).”
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (2019, methodology): “b); however, this observation had not been fully justified [42].”
Aggregating explanation methods for stable and robust explainability. (2019, result): “The open problem of explainability is reflected in a lot of recent work (Kindermans et al., 2017; Selvaraju et al., 2017; Bach et al., 2015; Zhang et al., 2018a; Zhou et al., 2016; Ancona et al., 2018; Ribeiro et al., 2016; Rieger et al., 2018; Kim et al., 2018; Lundberg & Lee, 2017; Zintgraf et al.”
Aggregating explainability methods for neural networks stabilizes explanations (2019, background): “The new-found interest into explainability is reflected in a lot of recent work into the problem (Kindermans et al., 2017; Selvaraju et al., 2017; Bach et al., 2015; Zhang et al., 2018; Zhou et al., 2016; Ancona et al., 2018; Ribeiro et al., 2016; Rieger et al., 2018; Kim et al., 2018; Lundberg & Le”