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Titlebook: Web and Big Data; Third International Jie Shao,Man Lung Yiu,Bin Cui Conference proceedings 2019 Springer Nature Switzerland AG 2019 artifi

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樓主: Braggart
41#
發(fā)表于 2025-3-28 15:20:47 | 只看該作者
Supervised Hashing with Recurrent Scalingtributes. This RNN is reformed to adjust the decorrelation of data flowing between each cell step, which not only makes the learning phase benefit from the ability of recurrent neural nets to learn with recurrent memory but also enable the availability of each hash bit to preserve distinct informati
42#
發(fā)表于 2025-3-28 19:05:46 | 只看該作者
Supervised Hashing with Recurrent Scalingtributes. This RNN is reformed to adjust the decorrelation of data flowing between each cell step, which not only makes the learning phase benefit from the ability of recurrent neural nets to learn with recurrent memory but also enable the availability of each hash bit to preserve distinct informati
43#
發(fā)表于 2025-3-28 23:40:14 | 只看該作者
44#
發(fā)表于 2025-3-29 06:01:39 | 只看該作者
TRPN: Matrix Factorization Meets Recurrent Neural Network for Temporal Rating Prediction RNN at different time step. We also apply item-dependent attention mechanism to discriminate the importance of different temporal interactions. We conduct extensive experiments to evaluate the performance of our proposed temporal rating prediction method named TRPN. The results show that TRPN can a
45#
發(fā)表于 2025-3-29 10:31:14 | 只看該作者
46#
發(fā)表于 2025-3-29 11:33:46 | 只看該作者
Improved Review Sentiment Analysis with a Syntax-Aware Encodertence representations into a sequence-structured long short-term memory network (LSTM) and exploit attention mechanism to generate the review embedding for final sentiment classification. We evaluate our attention-based tree-LSTM model on three public datasets, and experimental results turn out that
47#
發(fā)表于 2025-3-29 18:59:27 | 只看該作者
48#
發(fā)表于 2025-3-29 20:46:57 | 只看該作者
49#
發(fā)表于 2025-3-30 03:20:12 | 只看該作者
ST-DCN: A Spatial-Temporal Densely Connected Networks for Crowd Flow Prediction for modeling the external factors. Then the outputs of these three modules are merged to predict the final crowd flow in each region. ST-DCN can alleviate the vanishing-gradient problem and strengthen the propagation of spatial features in very deep network. In addition, the spatial features struct
50#
發(fā)表于 2025-3-30 06:01:44 | 只看該作者
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