Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation

Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation

Classification approaches that allow to extract logical rules such as decision trees are often considered to be more interpretable than neural networks. Also, logical rules are comparatively easy to verify with any possible input. This is an important part in systems that aim to ensure correct opera...

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Bibliographic Details
Main Authors: Sophie Burkhardt, Jannis Brugger, Nicolas Wagner, Zahra Ahmadi, Kristian Kersting, Stefan Kramer
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-07-01
Series:Frontiers in Artificial Intelligence
Subjects:
k-term DNF
stochastic local search
convolutional neural networks
logical rules
rule extraction
interpretability
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2021.642263/full

Internet

https://www.frontiersin.org/articles/10.3389/frai.2021.642263/full

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