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Challenging SMT solvers to verify neural networks

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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DOI: 10.3233/AIC-2012-0525

Abstract

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.

Item Type:Article
ID Code:7969
Status:Published
Refereed:Yes
Uncontrolled Keywords:Empirical evaluation of SMT solvers, applications of automated reasoning, formal methods for adaptive systems
Subjects:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 Sistemi di elaborazione delle informazioni
Divisions:001 Università di Sassari > 01-a Nuovi Dipartimenti dal 2012 > Scienze Politiche, Scienze della Comunicazione e Ingegneria dell'Informazione
Publisher:IOS Press
eISSN:1875-8452
Deposited On:19 Sep 2012 17:15

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