cavity 发表于 2025-3-26 21:10:08

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Condescending 发表于 2025-3-27 03:24:37

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消灭 发表于 2025-3-27 05:36:43

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Credence 发表于 2025-3-27 13:26:07

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正式演说 发表于 2025-3-27 16:26:52

Zhiwei Tang,Lingling Hu,Jianye Lia for machine learning. The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact in the context of search and recommender systems.

corpuscle 发表于 2025-3-27 18:48:14

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意外的成功 发表于 2025-3-28 00:19:46

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orthodox 发表于 2025-3-28 03:54:02

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Blazon 发表于 2025-3-28 10:19:27

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heirloom 发表于 2025-3-28 11:47:17

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查看完整版本: Titlebook: Bias and Social Aspects in Search and Recommendation; First International Ludovico Boratto,Stefano Faralli,Giovanni Stilo Conference proce