Characterizing the Accuracy/Complexity Landscape of Explanations of Deep Networks through Knowledge Extraction

ICLR 2019 Simon OdenseArtur d'Avila Garcez

Knowledge extraction techniques are used to convert neural networks into symbolic descriptions with the objective of producing more comprehensible learning models. The central challenge is to find an explanation which is more comprehensible than the original model while still representing that model faithfully... (read more)

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