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• W2783991250 abstract "The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability. Formal verification can address these concerns by guaranteeing that a deep learning system operates as intended, but the state-of-the-art is limited to small systems. In this work-in-progress report we give an overview of our work on mitigating this difficulty, by pursuing two complementary directions: devising scalable verification techniques, and identifying design choices that result in deep learning systems that are more amenable to verification." @default.
• W2783991250 created "2018-01-26" @default.
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• W2783991250 date "2018-01-18" @default.
• W2783991250 modified "2023-09-27" @default.
• W2783991250 title "Toward Scalable Verification for Safety-Critical Deep Networks" @default.
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• W2783991250 hasPublicationYear "2018" @default.
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