Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Yingqiang Ge"'
Publikováno v:
Information, Vol 14, Iss 8, p 424 (2023)
A graph is a widely used and effective data structure in many applications; it describes the relationships among nodes or entities. Currently, most semi-supervised or unsupervised graph neural network models are trained based on a very basic operatio
Externí odkaz:
https://doaj.org/article/b748c54aa5e84f25962604e5398aa469
Autor:
Yunyun Gao, Yingqiang Ge, Liping Yan, Nikita E. Vikhrev, Qike Wang, Nathan J. Butterworth, Dong Zhang
Publikováno v:
Insects, Vol 13, Iss 11, p 1015 (2022)
Lispe Latreille (Diptera: Muscidae) are a widespread group of predatory flies that inhabit semi-aquatic environments. Previous studies on this genus have mainly focused on morphological classification, so molecular data are entirely lacking, and ther
Externí odkaz:
https://doaj.org/article/15a1284b5f534aaba4a780cd4e6d08d5
Autor:
Jianchao Ji, Zelong Li, Shuyuan Xu, Max Xiong, Juntao Tan, Yingqiang Ge, Hao Wang, Yongfeng Zhang
Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under machine intelligence context. In this paper, we explore how the two reas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::552f43619658469b808e1005bab085f1
http://arxiv.org/abs/2307.00165
http://arxiv.org/abs/2307.00165
Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks. However, the use of such algorithms comes with trade-offs in terms of algorithmic fairness, which has been widely
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6c0ceb5c99b151cd376db77251cecde
http://arxiv.org/abs/2303.09527
http://arxiv.org/abs/2303.09527
Publikováno v:
Journal of the Association for Information Science and Technology. 73:1461-1473
With the emerging needs of creating fairness-aware solutions for search and recommendation systems, a daunting challenge exists of evaluating such solutions. While many of the traditional information retrieval (IR) metrics can capture the relevance,
Publikováno v:
ACM Transactions on Intelligent Systems & Technology; Oct2023, Vol. 14 Issue 5, p1-48, 48p
Publikováno v:
Sixteenth ACM Conference on Recommender Systems.
Autor:
Hongpeng Wang, Yachen Tao, Mercy Vimbai Masuku, Jiaren Cao, Jiayao Yang, Kexin Huang, Yingqiang Ge, Yangjin Yu, Zhuqian Xiao, Yi Kuang, Jun Huang, Shengxiang Yang
Publikováno v:
Journal of Molecular Liquids. 377:121379
Publikováno v:
Proceedings of the ACM Web Conference 2022.
Autor:
Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li, Yongfeng Zhang
Existing research on fairness-aware recommendation has mainly focused on the quantification of fairness and the development of fair recommendation models, neither of which studies a more substantial problem--identifying the underlying reason of model
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::273dd31e6752490d8a3f51af89266a89
http://arxiv.org/abs/2204.11159
http://arxiv.org/abs/2204.11159