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Titlebook: Computer Aided Verification; 34th International C Sharon Shoham,Yakir Vizel Conference proceedings‘‘‘‘‘‘‘‘ 2022 The Editor(s) (if applicabl

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41#
發(fā)表于 2025-3-28 18:24:54 | 只看該作者
42#
發(fā)表于 2025-3-28 22:41:55 | 只看該作者
The Lattice-Theoretic Essence of?Property Directed Reachability Analysiste instances of LT-PDR, derive their implementation from a generic Haskell implementation of LT-PDR, and experimentally evaluate them. We also present a categorical structural theory that derives these instances.
43#
發(fā)表于 2025-3-29 00:33:27 | 只看該作者
44#
發(fā)表于 2025-3-29 04:19:56 | 只看該作者
https://doi.org/10.1007/978-1-4020-6344-2ing up general principles for the empirical analysis and evaluation of a network’s robustness as a mathematical property—during the network’s training phase, its verification, and after its deployment. We then apply these principles and conduct a case study that showcases the practical benefits of our general approach.
45#
發(fā)表于 2025-3-29 10:11:13 | 只看該作者
Mixed Methods for Research on Open Systemsarator is constructed using an abstract domain representation of convex sets. The generalization mechanism of the decision tree learning from the constraints of the separator allows the inference of general invariants, accurate enough for proving the targeted property. We implemented our algorithm and showed its efficiency.
46#
發(fā)表于 2025-3-29 14:14:47 | 只看該作者
47#
發(fā)表于 2025-3-29 18:52:04 | 只看該作者
Data-driven Numerical Invariant Synthesis with?Automatic Generation of?Attributesarator is constructed using an abstract domain representation of convex sets. The generalization mechanism of the decision tree learning from the constraints of the separator allows the inference of general invariants, accurate enough for proving the targeted property. We implemented our algorithm and showed its efficiency.
48#
發(fā)表于 2025-3-29 21:57:05 | 只看該作者
49#
發(fā)表于 2025-3-30 02:31:43 | 只看該作者
50#
發(fā)表于 2025-3-30 07:20:28 | 只看該作者
Fast and Scalable Run-time Scheduling,ple inputs to reduce overall verification costs. We perform an extensive experimental evaluation to demonstrate the effectiveness of shared certificates in reducing the verification cost on a range of datasets and attack specifications on image classifiers including the popular patch and geometric perturbations. We release our implementation at ..
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