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Found 2 papers, 1 papers with code
Efficient Decompositional Rule Extraction for Deep Neural Networks
In recent years, there has been significant work on increasing both interpretability and debuggability of a Deep Neural Network (DNN) by extracting a rule-based model that approximates its decision boundary.
On The Quality Assurance Of Concept-Based Representations
Recent work on Explainable AI has focused on concept-based explanations, where deep learning models are explained in terms of high-level units of information, referred to as concepts.
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