Case-Based Explanation for Artificial Neural Nets

17 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.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “For example, an opaque neural network can be combined with a transparent Case Based Reasoning (CBR) system [316,317] .”
What Do Compressed Deep Neural Networks Forget (2019, methodology): “…(Leibig et al., 2017; Zhang, 1992), to choose not to classify certain examples when the model is uncertain (Bartlett & Wegkamp, 2008; Cortes et al., 2016), or to aid interpretability as a case based reasoning tool to explain model behavior (Kim et al., 2016; Caruana, 2000; Hooker et al., 2019).”
Selective Brain Damage: Measuring the Disparate Impact of Model Pruning (2019, background): “4.”