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The core idea is to drive testing of DNNs from rules abstracting the network behavior. These rules are automatically extracted from a trained model based on monitoring its neuron values when running on a set of labeled data, and are validated on a separate test set.
Dec 4, 2023 · The core idea is to drive testing of DNNs from rules abstracting the network behavior. These rules are automatically extracted from a trained ...
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In this paper we propose a network architecture that combines a rule-based approach with that of the neural network paradigm.
Jan 28, 2022 · We propose DeepCTRL, a new methodology used to incorporate rules into data-learned DNNs. DeepCTRL enables controllability of rule strength at inference without ...
An RB/ANN integrated approach is proposed to facilitate the development of an expert system which provides a “high-performance” knowledge-based network.
Missing: Testing | Show results with:Testing
Jan 8, 2019 · Rule-based systems rely on explicitly stated and static models of a domain. Learning systems create their own models.
Rule-based surrogate models are an effective and interpretable way to approxi- mate a Deep Neural Network's (DNN) decision boundaries, allowing humans to.
Apr 11, 2023 · Rule-based surrogate models are an effective and interpretable way to approximate a Deep Neural Network's (DNN) decision boundaries, allowing ...
Missing: Testing | Show results with:Testing
The method has two phases: in the first phase, an attempt is made to locate the optimal interval, and in the second phase, the artificial neural network is ...
Using rules that abstract input-output behavior to drive testing the model can translate into adequate coverage of the rules, which have clear semantic meaning ...