寄生虫
发表于 2025-3-23 10:38:39
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杀人
发表于 2025-3-23 16:35:49
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巨大没有
发表于 2025-3-23 18:27:02
K,ter vision and other fields . For a third-order HDI tensor modeling a dynamic network, this book carry out some preliminary research on latent factorization of tensors methods to implement accurate representation for dynamic networks. Further, in real industrial applications, in order to tackle
Allure
发表于 2025-3-24 01:39:33
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extrovert
发表于 2025-3-24 04:14:42
https://doi.org/10.1007/978-981-19-8934-6Dynamic network representation; Latent factorization of tensors; High-dimensional and incomplete tenso
ENACT
发表于 2025-3-24 08:03:03
978-981-19-8933-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
军械库
发表于 2025-3-24 13:20:29
Hao Wu,Xuke Wu,Xin LuoExposes readers to a novel research perspective regarding dynamic network representation.Presents four dynamic network representation methods based on latent factorization of tensors.Accomplishes accu
Hay-Fever
发表于 2025-3-24 15:26:44
SpringerBriefs in Computer Sciencehttp://image.papertrans.cn/e/image/283681.jpg
圆柱
发表于 2025-3-24 20:54:50
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antedate
发表于 2025-3-25 02:36:29
Multiple Biases-Incorporated Latent Factorization of Tensors,tion on extracting useful knowledge form an HDI tensor. However, existing LFT-based models lack solid consideration for the volatility of dynamic network data, thereby leading to the descent of model representation learning ability. To tackle this problem, this chapter proposes a multiple biases-inc