拥挤前
发表于 2025-3-25 04:36:19
Explainable Artificial Intelligence978-3-031-44064-9Series ISSN 1865-0929 Series E-ISSN 1865-0937
做事过头
发表于 2025-3-25 11:34:29
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acrobat
发表于 2025-3-25 14:47:17
The Xi Method: Unlocking the Mysteries of Regression with Statisticsonally, the Xi method is model and data agnostic, meaning that it can be applied to a wide range of machine learning models and data types. To demonstrate the potential of the Xi method, we provide applications that encompass three different data types: tabular, image, and text.
CHOIR
发表于 2025-3-25 18:36:14
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Analogy
发表于 2025-3-25 22:53:42
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Bucket
发表于 2025-3-26 01:39:04
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浪费物质
发表于 2025-3-26 04:20:00
Do Intermediate Feature Coalitions Aid Explainability of Black-Box Models?ronomies, i.e., part-whole relationships, via a domain expert that can be utilised to generate explanations at an abstract level. We illustrate the usability of this approach in a real-world car model example and the Titanic dataset, where intermediate concepts aid in explainability at different levels of abstraction.
词汇表
发表于 2025-3-26 09:12:13
Conference proceedings 2023bon, Portugal, in July 2023. .The 94 papers presented were thoroughly reviewed and selected from the 220 qualified submissions. They are organized in the following topical sections: .Part I: Interdisciplinary perspectives, approaches and strategies for xAI; Model-agnostic explanations, methods and
常到
发表于 2025-3-26 14:07:46
Applications of PGAA with Neutron Beams,us associated expectations, as well as non-functional explainability requirements, we show how business-oriented XAI requirements can be formulated and prepared for integration into process design. This case study is a valuable resource for researchers and practitioners seeking better to understand the role of explainable AI in practice.
隐士
发表于 2025-3-26 16:49:47
Trichotillomania (Hair-Pulling Disorder ),on-manifold data for the calculation of Shapley values upfront, instead of having to estimate a large number of conditional densities or make strong parametric assumptions. Through real and simulated data experiments, we demonstrate the effectiveness of knockoff imputation against adversarial attacks.