The popularity of neural networks has grown significantly in various domains, however their use in safety-critical areas has been restricted due to reliability concerns. The AIDOaRt project, an H2020-ECSEL European initiative, aims to develop dependable neural networks for safety-critical contexts. This work investigates the application of Satisfiability Modulo Theory technologies to verify neural networks with non-linear activation functions in computer vision tasks.
Verifying Neural Networks with SMT: An Experimental Evaluation / Guidotti, D.; Pandolfo, L.; Pulina, L.. - (2023). (Intervento presentato al convegno 19th IEEE International Conference on e-Science, e-Science 2023 tenutosi a cyp nel 2023) [10.1109/e-Science58273.2023.10254877].
Verifying Neural Networks with SMT: An Experimental Evaluation
Guidotti D.;Pandolfo L.;Pulina L.
2023-01-01
Abstract
The popularity of neural networks has grown significantly in various domains, however their use in safety-critical areas has been restricted due to reliability concerns. The AIDOaRt project, an H2020-ECSEL European initiative, aims to develop dependable neural networks for safety-critical contexts. This work investigates the application of Satisfiability Modulo Theory technologies to verify neural networks with non-linear activation functions in computer vision tasks.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
