Search

Your search keyword '"Yu, Shanshan"' showing total 2,385 results
Start Over Author "Yu, Shanshan"
2,385 results on '"Yu, Shanshan"'

4. A Mettl16/m6A/mybl2b/Igf2bp1 axis ensures cell cycle progression of embryonic hematopoietic stem and progenitor cells

Author

Han, Yunqiao, Sun, Kui, Yu, Shanshan, Qin, Yayun, Zhang, Zuxiao, Luo, Jiong, Hu, Hualei, Dai, Liyan, Cui, Manman, Jiang, Chaolin, Liu, Fei, Huang, Yuwen, Gao, Pan, Chen, Xiang, Xin, Tianqing, Ren, Xiang, Wu, Xiaoyan, Song, Jieping, Wang, Qing, Tang, Zhaohui, Chen, Jianjun, Zhang, Haojian, Zhang, Xianqin, Liu, Mugen, and Luo, Daji

Published
2024
Full Text
View/download PDF

16. Improving Natural Language Inference with Residual Attention

Author

Yu, Shanshan, Su, Jindian, Ye, Xiaobin, Ma, Dandan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Huang, De-Shuang, editor, Premaratne, Prashan, editor, and Yuan, Changan, editor

Published
2024
Full Text
View/download PDF

35. Multi-Relation Aware Temporal Interaction Network Embedding

Author

Chen, Ling, Yu, Shanshan, Lyu, Dandan, and Wang, Da

Subjects
Computer Science - Machine Learning
Abstract

Temporal interaction networks are formed in many fields, e.g., e-commerce, online education, and social network service. Temporal interaction network embedding can effectively mine the information in temporal interaction networks, which is of great significance to the above fields. Usually, the occurrence of an interaction affects not only the nodes directly involved in the interaction (interacting nodes), but also the neighbor nodes of interacting nodes. However, existing temporal interaction network embedding methods only use historical interaction relations to mine neighbor nodes, ignoring other relation types. In this paper, we propose a multi-relation aware temporal interaction network embedding method (MRATE). Based on historical interactions, MRATE mines historical interaction relations, common interaction relations, and interaction sequence similarity relations to obtain the neighbor based embeddings of interacting nodes. The hierarchical multi-relation aware aggregation method in MRATE first employs graph attention networks (GATs) to aggregate the interaction impacts propagated through a same relation type and then combines the aggregated interaction impacts from multiple relation types through the self-attention mechanism. Experiments are conducted on three public temporal interaction network datasets, and the experimental results show the effectiveness of MRATE., Comment: 18 pages, 4 figures

Published
2021

36. Multi-Level Visual Similarity Based Personalized Tourist Attraction Recommendation Using Geo-Tagged Photos

Author

Chen, Ling, Lyu, Dandan, Yu, Shanshan, and Chen, Gencai

Subjects
Computer Science - Multimedia, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Geo-tagged photo based tourist attraction recommendation can discover users' travel preferences from their taken photos, so as to recommend suitable tourist attractions to them. However, existing visual content based methods cannot fully exploit the user and tourist attraction information of photos to extract visual features, and do not differentiate the significances of different photos. In this paper, we propose multi-level visual similarity based personalized tourist attraction recommendation using geo-tagged photos (MEAL). MEAL utilizes the visual contents of photos and interaction behavior data to obtain the final embeddings of users and tourist attractions, which are then used to predict the visit probabilities. Specifically, by crossing the user and tourist attraction information of photos, we define four visual similarity levels and introduce a corresponding quintuplet loss to embed the visual contents of photos. In addition, to capture the significances of different photos, we exploit the self-attention mechanism to obtain the visual representations of users and tourist attractions. We conducted experiments on a dataset crawled from Flickr, and the experimental results proved the advantage of this method., Comment: Accepted by TKDD

Published
2021

Catalog

Books, media, physical & digital resources
See catalog results