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Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges
Our paper provides a comprehensive survey of the state-of-the-art in the assurance of ML, i. e. in the generation of evidence that ML is sufficiently safe for its intended use.
Testing Deep Neural Networks
In this paper, inspired by the MC/DC coverage criterion, we propose a family of four novel test criteria that are tailored to structural features of DNNs and their semantics.
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