Zobrazeno 1 - 10
of 50
pro vyhledávání: '"HU Mengling"'
Publikováno v:
Meitan kexue jishu, Vol 51, Iss 3, Pp 37-51 (2023)
There are significant differences in abutment pressure distribution and rock mass deformation on both sides of section coal pillar under different working conditions, the influence of different abutment pressures on the overall stability of coal pill
Externí odkaz:
https://doaj.org/article/2c9b8b83885844ada6071252dd2252a4
Short text clustering has been popularly studied for its significance in mining valuable insights from many short texts. In this paper, we focus on the federated short text clustering (FSTC) problem, i.e., clustering short texts that are distributed
Externí odkaz:
http://arxiv.org/abs/2312.07556
Graph clustering has been popularly studied in recent years. However, most existing graph clustering methods focus on node-level clustering, i.e., grouping nodes in a single graph into clusters. In contrast, graph-level clustering, i.e., grouping mul
Externí odkaz:
http://arxiv.org/abs/2311.13953
Autor:
Liao, Xinting, Chen, Chaochao, Liu, Weiming, Zhou, Pengyang, Zhu, Huabin, Shen, Shuheng, Wang, Weiqiang, Hu, Mengling, Tan, Yanchao, Zheng, Xiaolin
Federated learning (FL) is a distributed machine learning paradigm that needs collaboration between a server and a series of clients with decentralized data. To make FL effective in real-world applications, existing work devotes to improving the mode
Externí odkaz:
http://arxiv.org/abs/2308.11646
Short text clustering is challenging since it takes imbalanced and noisy data as inputs. Existing approaches cannot solve this problem well, since (1) they are prone to obtain degenerate solutions especially on heavy imbalanced datasets, and (2) they
Externí odkaz:
http://arxiv.org/abs/2305.16335
Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the cold-start problem in recommender systems. Most of the existing CDR models assume that both the source and target domains share the same o
Externí odkaz:
http://arxiv.org/abs/2205.06440
Autor:
Li, Xiaoqian, Hu, Mengling, Zhou, Xiaogang, Yu, Lu, Qin, Dalian, Wu, Jianming, Deng, Lan, Huang, Lufeng, Ren, Fang, Liao, Bin, Wu, Anguo, Fan, Dongsheng
Publikováno v:
In Free Radical Biology and Medicine 1 November 2024 224:740-756
Autor:
Hu, Mengling, Li, Zhuman, Zhang, Ling, Wang, Cong, Wu, Danni, Zhao, Xuan, Tsai, Tsung-Yuan, Wang, Shaobai
Publikováno v:
In Gait & Posture March 2025 117:78-84
Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the data sparsity and cold-start problem in recommender systems. In this paper, we focus on the Review-based Non-overlapped Recommendation (RN
Externí odkaz:
http://arxiv.org/abs/2202.04920
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