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Titlebook: Computer Aided Verification; 33rd International C Alexandra Silva,K. Rustan M. Leino Conference proceedings‘‘‘‘‘‘‘‘ 2021 The Editor(s) (if

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51#
發(fā)表于 2025-3-30 09:03:36 | 只看該作者
0302-9743 and synthesis...Part II: complexity and termination; decision procedures and solvers; hardware and model checking; logical foundations; and software verification..This is an open access book..978-3-030-81684-1978-3-030-81685-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
52#
發(fā)表于 2025-3-30 14:35:04 | 只看該作者
53#
發(fā)表于 2025-3-30 19:56:47 | 只看該作者
NN,: Constraint-Based Repair of?Neural Network Classifiersttacks. Our evaluation on MNIST and CIFAR-10 models shows that . can improve the accuracy by 45.56% points on poisoned data and 10.40% points on adversarial data. . also provides small improvement in the overall accuracy of models, without requiring new data or re-training.
54#
發(fā)表于 2025-3-30 21:39:14 | 只看該作者
Automated Safety Verification of Programs Invoking Neural Networksy interaction between programs and neural networks. Our technique provides a tight two-way integration of a program and a neural-network analysis and is formalized in a general framework based on abstract interpretation. We evaluate its effectiveness on 26 variants of a widely used, restricted autonomous-driving benchmark.
55#
發(fā)表于 2025-3-31 02:57:55 | 只看該作者
56#
發(fā)表于 2025-3-31 08:29:17 | 只看該作者
57#
發(fā)表于 2025-3-31 12:10:08 | 只看該作者
58#
發(fā)表于 2025-3-31 16:42:44 | 只看該作者
https://doi.org/10.1007/978-3-322-88430-5 10 benchmarks and compare Verisig 2.0 against three state-of-the-art verification tools. We show that Verisig 2.0 is both more accurate and faster, achieving speed-ups of up?to 21x and 268x against different tools, respectively.
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