婉言 发表于 2025-3-21 17:47:46

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nurture 发表于 2025-3-21 23:27:13

978-3-031-14922-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

万灵丹 发表于 2025-3-22 03:40:22

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Gourmet 发表于 2025-3-22 07:40:29

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/222324.jpg

一再烦扰 发表于 2025-3-22 11:41:53

A Note on the Wave Climatology of UK Waters Machine Learning or black-box models. Existing XAI libraries offer a good number of explanation methods, that are reusable for different domains and models, with different choices of parameters. However, it is not clear what would be a good explainer for a given situation, domain, AI model, and use

OMIT 发表于 2025-3-22 14:37:27

Alison Schwartz-Kripner,Cornelia P. ChanningHence, the popular methods generate synthetic counterfactuals using “blind” perturbation, by manipulating feature values to elicit a class change. However, this strategy has other problems, notably a tendency to generate invalid data points that are . or that involve feature-values that do not natur

OMIT 发表于 2025-3-22 20:00:57

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郊外 发表于 2025-3-22 22:05:37

W. Mestwerdt,O. Müller,H. Brandauper we show how this can be addressed in a case-based reasoning (CBR) context by a metric learning strategy that explicitly considers bias/fairness. Since one of the advantages CBR has over alternative machine learning approaches is interpretability, it is interesting to see how much this metric lea

放气 发表于 2025-3-23 02:04:37

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Sad570 发表于 2025-3-23 05:32:43

Critical Fluctuations Under Shear Flow,erize domains. The effectiveness of machine learning with deep neural networks has prompted much interest in neural network approaches to feature learning in case-based reasoning, with several works showing the value of feature extraction from input data using convolutional neural networks. Those ap
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查看完整版本: Titlebook: Case-Based Reasoning Research and Development; 30th International C Mark T. Keane,Nirmalie Wiratunga Conference proceedings 2022 The Editor