Automatic verification of finite-state concurrent systems using temporal logic specifications
ACM Transactions on Programming Languages and Systems (TOPLAS)
Multilayer feedforward networks are universal approximators
Neural Networks
Model checking
POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Specification and verification of concurrent systems in CESAR
Proceedings of the 5th Colloquium on International Symposium on Programming
High level formal verification of next-generation microprocessors
Proceedings of the 40th annual Design Automation Conference
Counterexample-guided abstraction refinement for symbolic model checking
Journal of the ACM (JACM)
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Developing artificial neural networks for safety critical systems
Neural Computing and Applications
Data Mining
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
LTL Model Checking for Security Protocols
CSF '07 Proceedings of the 20th IEEE Computer Security Foundations Symposium
Sketching concurrent data structures
Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation
The Journal of Machine Learning Research
Journal of Artificial Intelligence Research
Numerica: a modeling language for global optimization
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Abstraction-guided synthesis of synchronization
Proceedings of the 37th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
A population and interval constraint propagation algorithm
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
An abstraction-refinement approach to verification of artificial neural networks
CAV'10 Proceedings of the 22nd international conference on Computer Aided Verification
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Annals of Mathematics and Artificial Intelligence
Hi-index | 0.00 |
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 NeVer, our tool for checking safety of ANNs. 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 NeVer. We present also empirical results confirming the effectiveness of NeVer on realistic case studies.