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024 7 _a10.1007/978-3-031-21222-2
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245 1 0 _aSoftware Verification and Formal Methods for ML-Enabled Autonomous Systems
_h[electronic resource] :
_b5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Haifa, Israel, July 31 - August 1, and August 11, 2022, Proceedings /
_cedited by Omri Isac, Radoslav Ivanov, Guy Katz, Nina Narodytska, Laura Nenzi.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aX, 205 p. 42 illus., 34 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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347 _atext file
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490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v13466
505 0 _aFoMLAS 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 _aThis 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 _aComputer science.
_99832
650 0 _aComputer networks .
_931572
650 0 _aMachine learning.
_91831
650 0 _aComputer vision.
_9177604
650 0 _aSoftware engineering.
_94138
650 1 4 _aComputer Science Logic and Foundations of Programming.
_942203
650 2 4 _aComputer Communication Networks.
_9177605
650 2 4 _aMachine Learning.
_91831
650 2 4 _aComputer Vision.
_9177606
650 2 4 _aSoftware Engineering.
_94138
700 1 _aIsac, Omri.
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700 1 _aIvanov, Radoslav.
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700 1 _aKatz, Guy.
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700 1 _aNarodytska, Nina.
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700 1 _aNenzi, Laura.
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710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9783031212239
830 0 _aLecture Notes in Computer Science,
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856 4 0 _uhttps://doi.org/10.1007/978-3-031-21222-2
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