责问
发表于 2025-3-28 16:24:41
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痛打
发表于 2025-3-28 22:20:00
Multivariate Time Series Clustering via Multi-relational Community Detection in Networksthe ability of networks to characterize both local and global relationship amongst nodes (representing data samples), while the use of MNMF can give full play to complex relations amongst individual series and preserve the multi-way nature of multivariate information. Preliminary experiment indicates promising results of our proposed approach.
Instantaneous
发表于 2025-3-29 00:05:27
Multivariate Time Series Clustering via Multi-relational Community Detection in Networksthe ability of networks to characterize both local and global relationship amongst nodes (representing data samples), while the use of MNMF can give full play to complex relations amongst individual series and preserve the multi-way nature of multivariate information. Preliminary experiment indicates promising results of our proposed approach.
弯曲道理
发表于 2025-3-29 03:42:50
Attentive and Collaborative Deep Learning for Recommendationmodel, learning of latent factors of users and items can be facilitated by deep processing of items’ tag information. Furthermore, user preferences learned are interpretable. Experiments conducted on a real world dataset demonstrate that our model can significantly outperform the state-of-the-art deep collaborative filtering models.
NOVA
发表于 2025-3-29 10:08:01
Attentive and Collaborative Deep Learning for Recommendationmodel, learning of latent factors of users and items can be facilitated by deep processing of items’ tag information. Furthermore, user preferences learned are interpretable. Experiments conducted on a real world dataset demonstrate that our model can significantly outperform the state-of-the-art deep collaborative filtering models.
impaction
发表于 2025-3-29 14:35:18
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记忆法
发表于 2025-3-29 15:55:10
Sentiment Classification via Supplementary Information Modeling methods. Results show that our model can not only successfully capture the effect of negation and intensity words, but also achieve significant improvements over state-of-the-art deep neural network baselines without supplementary features.
Lament
发表于 2025-3-29 20:13:40
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减至最低
发表于 2025-3-30 02:03:03
Sentiment Classification via Supplementary Information Modeling methods. Results show that our model can not only successfully capture the effect of negation and intensity words, but also achieve significant improvements over state-of-the-art deep neural network baselines without supplementary features.
把手
发表于 2025-3-30 06:47:51
An Estimation Framework of Node Contribution Based on Diffusion Informationmportance of nodes in the spreading processes. Then, we propose an estimation framework and give the method to estimate node contribution based on diffusion samples. Accordingly, the Contribution Estimation algorithm is proposed upon the framework. Finally, we implement our algorithm and test the efficiency on two weighted social networks.