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Introduction to Neural Network Verification
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Deep learning has transformed the way we think of software and what it can do. But deep neural networks are fragile and their behaviors are often surprising. In many settings, we need to provide formal guarantees on the safety, security, correctness, or robustness of neural networks. This book covers foundational ideas from formal verification and their adaptation to reasoning about neural networks and deep learning.
Forward citations
Cited by 2 Pith papers
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Ceci n'est pas une pipe: AI systems as semantic abstractions
AI systems are formalized as semantic abstractions whose claims are reliable only when supported by universal knowledge, source-derived knowledge, current effective knowledge, and explicit authority.
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The Role of Rigor in Artificial Intelligence
Modern AI's distinctive trajectory is explained by the primacy of operational rigor over conceptual and epistemic rigor across successive paradigms.
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