Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Youfa Liu"'
Publikováno v:
Data Intelligence (2024)
Externí odkaz:
https://doaj.org/article/4ceafa9eec3646a4a34548b9d0ec0aab
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 4, Iss 3, Pp 580-590 (2022)
Graph neural networks (GNNs) have developed rapidly in recent years because they can work over non-Euclidean data and possess promising prediction power in many real-word applications. The graph classification problem is one of the central problems i
Externí odkaz:
https://doaj.org/article/87deda6d68b843c5bc23f127655700e7
Publikováno v:
IEEE Access, Vol 10, Pp 56482-56492 (2022)
Graph Neural Networks (GNNs) have been intensively studied in recent years because of their promising performance over graph-structural data and have provided assistance in many fields. Recalling recent works on graph neural networks, we found that i
Externí odkaz:
https://doaj.org/article/1e3f047031554750959973bed36cebc1
Autor:
Youfa Liu1, Xiaoyang Chen1, Xiaoyu Dong1, Ao Liu1, Kefeng Ouyang1, Yan Huang1,2,3 yanhuanglib@hit.edu.cn
Publikováno v:
Science Advances. 7/26/2024, Vol. 10 Issue 30, p1-6. 6p.
Publikováno v:
ACM Transactions on Multimedia Computing, Communications & Applications; Jan2024, Vol. 20 Issue 1, p1-21, 21p
Autor:
Mangwei Cui, Ninggui Ma, Hao Lei, Youfa Liu, Wei Ling, Sheng Chen, Jiaqi Wang, Hongfei Li, Zhaohui Li, Jun Fan, Yan Huang
Publikováno v:
Angewandte Chemie.
Publikováno v:
IEEE Transactions on Multimedia. 24:4054-4066
This paper addresses the multi-view subspace clustering problem and proposes the self-paced enhanced low-rank tensor kernelized multi-view subspace clustering (SETKMC) method, which is based on two motivations: (1) singular values of the representati
Publikováno v:
Energy & Environmental Science. 15:3670-3687
Thermo-electrochemical cells, a promising heat to electricity conversion technology, which originates from thermogalvanic effect or/and Soret effect.
Publikováno v:
Information Sciences. 578:750-761
Zero-shot learning (ZSL) realizes unseen object recognition by transferring knowledge from seen classes to unseen classes under common semantic space assumption, such as attribute space and semantic word vector space. Previous works used seen image f