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Specifically, we\ndevelop coverage-guided fuzzing (CGF) methods for neural networks. In CGF,\nrandom mutations of inputs to a neural network are guided by a coverage metric\ntoward the goal of satisfying user-specified constraints. We describe how fast\napproximate nearest neighbor algorithms can provide this coverage metric. We\nthen discuss the application of CGF to the following goals: finding numerical\nerrors in trained neural networks, generating disagreements between neural\nnetworks and quantized versions of those networks, and surfacing undesirable\nbehavior in character level language models. 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