Extracting Boolean and probabilistic rules from trained neural networks

DOI10.1016/j.neunet.2020.03.024zbMath1468.68160OpenAlexW3014628001WikidataQ91825469 ScholiaQ91825469MaRDI QIDQ1980413

Yanyan Li

Publication date: 8 September 2021

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2020.03.024

zbMATH Keywords

dynamic programmingneural networksBoolean functionsrule extraction

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Optimal control of multi-task Boolean control networks via temporal logic

Uses Software

• UCI-ml
• PyTorch

Cites Work

• Neural network explanation using inversion
• Nested canalyzing, unate cascade, and polynomial functions
• Discrete Mathematics of Neural Networks
• Analysis of Boolean Functions
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