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Titlebook: Data Science; 8th International Co Yang Wang,Guobin Zhu,Zeguang Lu Conference proceedings 2022 The Editor(s) (if applicable) and The Author

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發(fā)表于 2025-3-30 10:49:45 | 只看該作者
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發(fā)表于 2025-3-30 13:29:12 | 只看該作者
Multirelationship Aware Personalized Recommendation Modelap the complex and diverse user relationships, so it is difficult to obtain an accurate modeling representation of the user. To solve this, we propose a multirelationship aware personalized recommendation(MrAPR) model, which aggregates the various relationships between social users from two aspects
53#
發(fā)表于 2025-3-30 16:38:57 | 只看該作者
Preliminary Study on Adapting ProtoPNet to Few-Shot Learning Using MAML learn class-specific prototypes and does not support few-shot learning. We propose the few-shot learning version of ProtoPNet by using MAML, enabling it to converge quickly on different classification tasks. We test our model on the Omniglot and MiniImagenet datasets and evaluate their prototype in
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發(fā)表于 2025-3-30 23:33:04 | 只看該作者
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發(fā)表于 2025-3-31 03:31:01 | 只看該作者
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發(fā)表于 2025-3-31 09:18:22 | 只看該作者
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發(fā)表于 2025-3-31 14:27:57 | 只看該作者
Real-World Superresolution by Using Deep Degradation Learninggh-resolution (HR) images and low-resolution (LR) images. Conversely, their superresolution performance in real-world superresolution tests is reduced because these methods create paired LR images by simply interpolating and downsampling HR images, which is very different from natural degradation. I
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