國立虎尾科技大學 |

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 /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Software verification and formal methods for ML-enabled autonomous systems/ edited by Omri Isac ... [et al.].
其他題名:	5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Haifa, Israel, July 31 - August 1, and August 11, 2022 : proceedings /
其他題名:	FoMLAS 2022
其他作者:	Isac, Omri.
團體作者:	Workshop on the Preservation of Stability under Discretization
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	x, 205 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Machine learning - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-031-21222-2
ISBN:	9783031212222

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 systems5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Haifa, Israel, July 31 - August 1, and August 11, 2022 : proceedings /[electronic resource] :FoMLAS 2022edited by Omri Isac ... [et al.]. - Cham :Springer International Publishing :2022. - x, 205 p. :ill., digital ;24 cm. - Lecture notes in computer science,134660302-9743 ;. - Lecture notes in computer science ;6140..

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.

ISBN: 9783031212222

Standard No.: 10.1007/978-3-031-21222-2doiSubjects--Topical Terms:

574552
Machine learning
--Congresses.

LC Class. No.: QA76.76.V47

Dewey Class. No.: 005.14

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 /
LDR:02812nam a2200361 a 4500 001 1097753
003 DE-He213
005 20221215094755.0
006 m d
007 cr nn 008maaau
008 230419s2022 sz s 0 eng d
020 $a 9783031212222 $q (electronic bk.)
020 $a 9783031212215 $q (paper)
024 7 $a 10.1007/978-3-031-21222-2 $2 doi
035 $a 978-3-031-21222-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.76.V47
072 7 $a UYA $2 bicssc
072 7 $a COM051000 $2 bisacsh
072 7 $a UYA $2 thema
082 0 4 $a 005.14 $2 23
090 $a QA76.76.V47 $b I61 2022
111 2 $a Workshop on the Preservation of Stability under Discretization $d (2001 : $c Fort Collins, Colo.) $3 527686
245 1 0 $a Software verification and formal methods for ML-enabled autonomous systems $h [electronic resource] : $b 5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Haifa, Israel, July 31 - August 1, and August 11, 2022 : proceedings / $c edited by Omri Isac ... [et al.].
246 3 $a FoMLAS 2022
246 3 $a NSV 2022
260 $a Cham : $c 2022. $b Springer International Publishing : $b Imprint: Springer,
300 $a x, 205 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 13466
505 0 $a 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.
520 $a 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.
650 0 $a Machine learning $v Congresses. $3 574552 $3 727808
650 0 $a Formal methods (Computer science) $3 564790
650 0 $a Software engineering $v Congresses. $3 528500
650 0 $a Computer software $x Verification $v Congresses. $3 675265 $3 727805
700 1 $a Isac, Omri. $e editor. $3 1393209
710 2 $a SpringerLink (Online service) $3 593884
711 2 $a Workshop on the Preservation of Stability under Discretization $d (2001 : $c Fort Collins, Colo.) $3 527686
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 6140. $3 744199
856 4 0 $u https://doi.org/10.1007/978-3-031-21222-2
950 $a Computer Science (SpringerNature-11645)