Pulina, Luca and Tacchella, Armando (2012) Challenging SMT solvers to verify neural networks. AI Communications, Vol. 25 (2), p. 117-135. eISSN 1875-8452. Article.
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In recent years, Satisfiability Modulo Theory (SMT) solvers are becoming increasingly popular in the Computer Aided Verification and Reasoning community. Used natively or as back-engines, they are accumulating a record of success stories and, as witnessed by the annual SMT competition, their performances and capacity are also increasing steadily. Introduced in previous contributions of ours, a new application domain providing an outstanding challenge for SMT solvers is represented by verification of Multi-Layer Perceptrons (MLPs) a widely-adopted kind of artificial neural network. In this paper we present an extensive evaluation of the current state-of-the-art SMT solvers and assess their potential in the promising domain of MLP verification.
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