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Literature Notes/Formal verification of neural network controlled autonomous systems.md
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@ -79,13 +79,13 @@ Published: 2019-04
>[!example]
>we utilize a Satisfiability Modulo Convex (SMC) encoding to enumerate all the possible assignments of different ReLUs.
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>[!example]
>Therefore, recent works focused en-tirely on verifying neural networks against simple input-output specifications [28–33]. Such input-output techniques compute aguaranteed range for the output of a deep neural network givena set of inputs represented as a convex polyhedron.
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>[!example]
>For example, by using binary variables to en-code piecewise linear functions, the constraints of ReLU functions are encoded as a Mixed-Integer Linear Programming (MILP). Com-bining output specifications that are expressed in terms of Linear Programming (LP), the verification problem eventually turns to aMILP feasibility problem [32, 34].
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