HILAR
发表于 2025-3-27 00:50:56
MLTL Multi-type (MLTLM): A Logic for Reasoning About Signals of Different Typesnctions. Capturing specifications in a logic like LTL enables verification and validation of CPS requirements, yet an LTL formula specification can imply unrealistic assumptions, such as that all signals populating the variables in the formula are of type Boolean and agree on a standard time step. T
删减
发表于 2025-3-27 01:31:14
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支架
发表于 2025-3-27 07:39:18
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orthopedist
发表于 2025-3-27 12:16:24
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严峻考验
发表于 2025-3-27 15:33:49
Conference proceedings 2022 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 dedic
neutrophils
发表于 2025-3-27 20:56:13
0302-9743 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 worksho
Fester
发表于 2025-3-28 00:06:04
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MITE
发表于 2025-3-28 04:33:13
Conference proceedings 2022ated 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. .
fibula
发表于 2025-3-28 07:09:02
A Cascade of Checkers for Run-time Certification of Local Robustnessuirement for autonomous systems where resorting to fail-safe mechanisms is highly undesirable. Though exact checks are expensive, via two case studies, we demonstrate that the exact check in a cascade is rarely invoked in practice. Code and data are available at ..
本能
发表于 2025-3-28 11:07:42
CEG4N: Counter-Example Guided Neural Network Quantization Refinements that the network’s output does not change after quantization. We evaluate CEG4N on a diverse set of benchmarks, including large and small networks. Our technique successfully quantizes the networks in our evaluation while producing models with up to 72% better accuracy than state-of-the-art techniques.