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Titlebook: AI Verification; First International Guy Avni,Mirco Giacobbe,Christian Schilling Conference proceedings 2024 The Editor(s) (if applicable)

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發(fā)表于 2025-3-21 19:28:12 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱AI Verification
期刊簡稱First International
影響因子2023Guy Avni,Mirco Giacobbe,Christian Schilling
視頻videohttp://file.papertrans.cn/168/167086/167086.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: AI Verification; First International  Guy Avni,Mirco Giacobbe,Christian Schilling Conference proceedings 2024 The Editor(s) (if applicable)
影響因子.This LNCS volume constitutes the proceedings of the First International Symposium on AI Verification, SAIV 2024, in Montreal, QC, Canada, during July 2024...The scope of the topics was broadly categorized into two groups. The first group, formal methods for artificial intelligence, comprised: formal specifications for systems with AI components; formal methods for analyzing systems with AI components; formal synthesis methods of AI components; testing approaches for systems with AI components; statistical approaches for analyzing systems with AI components; and approaches for enhancing the explainability of systems with AI components. The second group, artificial intelligence for formal methods, comprised: AI methods for formal verification; AI methods for formal synthesis; AI methods for safe control; and AI methods for falsification..
Pindex Conference proceedings 2024
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,Concept-Based Analysis of?Neural Networks via?Vision-Language Models,cifications for vision tasks and the lack of efficient verification procedures. In this paper, we propose to leverage emerging multimodal, vision-language, foundation models (VLMs) as a lens through which we can reason about vision models. VLMs have been trained on a large body of images accompanied
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,Parallel Verification for?,-Equivalence of?Neural Network Quantization,emory. However, it also brings in loss of generalization and even potential errors to the models. In this work, we propose a parallelization technique for formally . the . between quantized models and their original real-valued counterparts. In order to guarantee both . and ., mixed integer linear p
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Iterative Counter-Example Guided Robustness Verification for Neural Networks,y considering different types of abstraction techniques focused toward the non-linearity in the computation in the neural network. We propose a complementary approach of abstracting the neural network by discarding the neurons. Our abstraction is based on the robustness property being verified. We t
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Grundkurs Microsoft Dynamics AXout the laborious task of manual heuristic creation and tuning, data-driven approaches demonstrate significant potential by extracting crucial patterns from a small set of data points. However, at present, symbolic methods generally surpass data-driven solvers in performance. In this work, we develo
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