NeVer
NeVer: a tool for artificial neural networks verification The adoption of artificial neural networks (ANNs) in safety-related applications is often avoided because it is difficult to rule out possible misbehaviors with traditional analytical or probabilistic techniques. In this paper we present {sc NeVer}, our tool for checking safety of ANNs.{sc NeVer} encodes the problem of verifying safety of ANNs into the problem of satisfying corresponding Boolean combinations of linear arithmetic constraints. We describe the main verification algorithm and the structure of {sc NeVer}. We present also empirical results confirming the effectiveness of {sc NeVer} on realistic case studies.
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References in zbMATH (referenced in 2 articles , 1 standard article )
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Sorted by year (- Barrios Rolanía, Dolores; Delgado Martínez, Guillermo; Manrique, Daniel: Multilayered neural architectures evolution for computing sequences of orthogonal polynomials (2018)
- Pulina, Luca; Tacchella, Armando: \textscNeVer: a tool for artificial neural networks verification (2011)