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Titlebook: Bridging the Gap Between AI and Reality; First International Bernhard Steffen Conference proceedings 2024 The Editor(s) (if applicable) an

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樓主
發(fā)表于 2025-3-21 18:54:59 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Bridging the Gap Between AI and Reality
期刊簡稱First International
影響因子2023Bernhard Steffen
視頻videohttp://file.papertrans.cn/191/190775/190775.mp4
學科分類Lecture Notes in Computer Science
圖書封面Titlebook: Bridging the Gap Between AI and Reality; First International  Bernhard Steffen Conference proceedings 2024 The Editor(s) (if applicable) an
影響因子This book constitutes the proceedings of the First International Conference on?Bridging the Gap between AI and Reality, AISoLA 2023, which took place in Crete, Greece, in October 2023.?The papers included in this book focus on the following topics: The nature of AI-based systems; ethical, economic and legal implications of AI-systems in practice; ways to make controlled use of AI via the various kinds of formal methods-based validation techniques; dedicated applications scenarios which may allow certain levels of assistance; and education in times of deep learning.?.
Pindex Conference proceedings 2024
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沙發(fā)
發(fā)表于 2025-3-22 00:01:39 | 只看該作者
,Best Practices aus ausgew?hlten Industrien,, we first discuss the engineering and research challenges associated with the design and verification of such systems. Then, based on the observation that existing works cannot actually achieve provable guarantees, we promote a two-step verification method for the ultimate achievement of provable statistical guarantees.
板凳
發(fā)表于 2025-3-22 02:33:10 | 只看該作者
地板
發(fā)表于 2025-3-22 08:33:52 | 只看該作者
https://doi.org/10.1007/978-3-8349-8649-8sions using statistical model checking (SMC-based learning), which uses the results from deductive verification as a shield to ensure that only safe actions are chosen. We take component failures into account and learn a schedule that is optimized for performance and ensures resilience in a given Simulink model.
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發(fā)表于 2025-3-22 09:17:26 | 只看該作者
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發(fā)表于 2025-3-22 18:26:38 | 只看該作者
Track C1: Safety Verification of?Deep Neural Networks (DNNs)k compiles and publishes benchmarks comprising machine learning models and their specifications across domains such as computer vision, finance, security, and others. These benchmarks will help assess the suitability and applicability of formal verification methods in diverse domains.
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發(fā)表于 2025-3-22 21:42:15 | 只看該作者
Zur begrenzten Organisierbarkeit von Führungential of the proposed method: we successfully synthesized mimic programs for neural networks trained on the MNIST and the Pima Indians diabetes data sets. All experiments were performed using the SMT-based .synthesis tool.
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發(fā)表于 2025-3-23 05:21:17 | 只看該作者
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發(fā)表于 2025-3-23 06:17:28 | 只看該作者
Managementnachwuchs erfolgreich machenfor neural network verification. To increase the completeness and the scalability of the analysis, we develop a two-step verification method involving abstract interpretation and simulation-based falsification. Numerical results confirm the applicability of the approach.
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