Aggregating explainability methods for neural networks stabilizes explanations

By Laura Rieger, Lars Kai Hansen. 2019

12 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Chandan Singh
2. Bin Yu
3. W. James Murdoch
4. Xisen Jin
5. John C. Duchi
6. Francis R. Bach
7. Martin J. Wainwright
8. Michael I. Jordan

Citations in the corpus

• Interpretations are useful: penalizing explanations to align neural networks with prior knowledge (2019, background): “They can be categorized as gradient-based (Springenberg et al., 2014; Sundararajan et al., 2017; Selvaraju et al., 2016; Baehrens et al., 2010; Rieger & Hansen, 2019), decomposition-based (Murdoch & Szlam, 2017; Shrikumar et al., 2016; Bach et al., 2015) and others (Dabkowski & Gal, 2017; Fong &…”