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_a10.1007/978-3-031-21222-2 _2doi |
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_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. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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. | |
650 | 0 | _aComputer networks . | |
650 | 0 | _aMachine learning. | |
650 | 0 | _aComputer vision. | |
650 | 0 | _aSoftware engineering. | |
650 | 1 | 4 | _aComputer Science Logic and Foundations of Programming. |
650 | 2 | 4 | _aComputer Communication Networks. |
650 | 2 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aComputer Vision. |
650 | 2 | 4 | _aSoftware Engineering. |
700 | 1 |
_aIsac, Omri. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aIvanov, Radoslav. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aKatz, Guy. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aNarodytska, Nina. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aNenzi, Laura. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031212215 |
776 | 0 | 8 |
_iPrinted edition: _z9783031212239 |
830 | 0 |
_aLecture Notes in Computer Science, _x1611-3349 ; _v13466 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-21222-2 |
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