Software verification and formal methods for ML-enabled autonomous systems : 5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Haifa, Israel, July 31 - August 1, and August 11, 2022, Proceedings /
Software verification and formal methods for ML-enabled autonomous systems : 5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Haifa, Israel, July 31 - August 1, and August 11, 2022, Proceedings /
FoMLAS 2022 NSV 2022
Omri Isac [and more] (eds.).
- Cham : Springer, 2023.
- 1 online resource (213 p.).
- Lecture Notes in Computer Science ; 13466 .
- Lecture notes in computer science ; 13466. .
Includes author index.
FoMLAS 2022 -- VPN: Verification of Poisoning in Neural Networks -- A Cascade of Checkers for Run-time Certification of Local Robustness -- CEG4N: Counter-Example Guided Neural Network Quantization Refinement -- Minimal Multi-Layer Modifications of Deep Neural Networks -- Differentiable Logics for Neural Network Training and Verification -- Neural Networks in Imandra: Matrix Representation as a Verification Choice -- Self-Correcting Neural Networks For Safe Classification -- Self-Correcting Neural Networks For Safe Classification -- NSV 2022 -- Verified Numerical Methods for Ordinary Differential Equations -- Neural Network Precision Tuning Using Stochastic Arithmetic -- MLTL Multi-type (MLTLM): A Logic for Reasoning about Signals of Different Types.
This book constitutes the refereed proceedings of the 5th International Workshop on Software Verification and Formal Methods for ML-Enables Autonomous Systems, FoMLAS 2022, and the 15th International Workshop on Numerical Software Verification, NSV 2022, which took place in Haifa, Israel, in July/August 2022. The volume contains 8 full papers from the FoMLAS 2022 workshop and 3 full papers from the NSV 2022 workshop. The FoMLAS workshop is dedicated to the development of novel formal methods techniques to discussing on how formal methods can be used to increase predictability, explainability, and accountability of ML-enabled autonomous systems. NSV 2022 is focusing on the challenges of the verification of cyber-physical systems with machine learning components.
9783031212222 3031212223
10.1007/978-3-031-21222-2 doi
Computer software--Verification--Congresses.
Software engineering--Congresses.
Formal methods (Computer science)--Congresses.
Machine learning--Congresses.
Logiciels--Vérification--Congrès.
Génie logiciel--Congrès.
Méthodes formelles (Informatique)--Congrès.
Apprentissage automatique--Congrès.
Computer software--Verification
Formal methods (Computer science)
Machine learning
Software engineering
Electronic books.
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
QA76.76.V47
005.14
Includes author index.
FoMLAS 2022 -- VPN: Verification of Poisoning in Neural Networks -- A Cascade of Checkers for Run-time Certification of Local Robustness -- CEG4N: Counter-Example Guided Neural Network Quantization Refinement -- Minimal Multi-Layer Modifications of Deep Neural Networks -- Differentiable Logics for Neural Network Training and Verification -- Neural Networks in Imandra: Matrix Representation as a Verification Choice -- Self-Correcting Neural Networks For Safe Classification -- Self-Correcting Neural Networks For Safe Classification -- NSV 2022 -- Verified Numerical Methods for Ordinary Differential Equations -- Neural Network Precision Tuning Using Stochastic Arithmetic -- MLTL Multi-type (MLTLM): A Logic for Reasoning about Signals of Different Types.
This book constitutes the refereed proceedings of the 5th International Workshop on Software Verification and Formal Methods for ML-Enables Autonomous Systems, FoMLAS 2022, and the 15th International Workshop on Numerical Software Verification, NSV 2022, which took place in Haifa, Israel, in July/August 2022. The volume contains 8 full papers from the FoMLAS 2022 workshop and 3 full papers from the NSV 2022 workshop. The FoMLAS workshop is dedicated to the development of novel formal methods techniques to discussing on how formal methods can be used to increase predictability, explainability, and accountability of ML-enabled autonomous systems. NSV 2022 is focusing on the challenges of the verification of cyber-physical systems with machine learning components.
9783031212222 3031212223
10.1007/978-3-031-21222-2 doi
Computer software--Verification--Congresses.
Software engineering--Congresses.
Formal methods (Computer science)--Congresses.
Machine learning--Congresses.
Logiciels--Vérification--Congrès.
Génie logiciel--Congrès.
Méthodes formelles (Informatique)--Congrès.
Apprentissage automatique--Congrès.
Computer software--Verification
Formal methods (Computer science)
Machine learning
Software engineering
Electronic books.
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
QA76.76.V47
005.14