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pro vyhledávání: '"Wen, Qianlong"'
Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most existing GSL works apply a joint learning framework where the estimated adj
Externí odkaz:
http://arxiv.org/abs/2211.06545
Self-supervised learning (SSL) for graph neural networks (GNNs) has attracted increasing attention from the graph machine learning community in recent years, owing to its capability to learn performant node embeddings without costly label information
Externí odkaz:
http://arxiv.org/abs/2210.02016
Autor:
Zhang, Chunhui, Huang, Chao, Tian, Yijun, Wen, Qianlong, Ouyang, Zhongyu, Li, Youhuan, Ye, Yanfang, Zhang, Chuxu
Even pruned by the state-of-the-art network compression methods, Graph Neural Networks (GNNs) training upon non-Euclidean graph data often encounters relatively higher time costs, due to its irregular and nasty density properties, compared with data
Externí odkaz:
http://arxiv.org/abs/2210.00162
Among different existing graph self-supervised learning strategies, graph contrastive learning (GCL) has been one of the most prevalent approaches to this problem. Despite the remarkable performance those GCL methods have achieved, existing GCL metho
Externí odkaz:
http://arxiv.org/abs/2209.07699
Autor:
Zhao, Jianan, Li, Chaozhuo, Wen, Qianlong, Wang, Yiqi, Liu, Yuming, Sun, Hao, Xie, Xing, Ye, Yanfang
Transformers have achieved remarkable performance in a myriad of fields including natural language processing and computer vision. However, when it comes to the graph mining area, where graph neural network (GNN) has been the dominant paradigm, trans
Externí odkaz:
http://arxiv.org/abs/2110.13094
Drug repurposing has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on preclinical data (e.g., chemical structures, drug targets), resulting i
Externí odkaz:
http://arxiv.org/abs/2007.07886
Autor:
Wen, Qianlong1 (AUTHOR), Liu, Ruoqi2 (AUTHOR), Zhang, Ping2,3 (AUTHOR) mail.pingzhang@gmail.com
Publikováno v:
BMC Medical Informatics & Decision Making. 9/24/2021, Vol. 21 Issue 1, p1-11. 11p.
Additional file 5: Table S1. Laboratory Result List. This table includes the name of 35 kinds of laboratory result involved in ourexperiments and their corresponding NHANES code. Figure S1. Disease Clinical Variable Statistics. The figure present num
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26f3e40d5caa106fe657f9309f79b138