Zobrazeno 1 - 10
of 63
pro vyhledávání: '"ZHAO Shenglin"'
Experimental Study on Dynamic Resilient Modulus of Circulating Fluidized Bed Combustion Ash Subgrade
Publikováno v:
Taiyuan Ligong Daxue xuebao, Vol 55, Iss 5, Pp 841-849 (2024)
Purposes To explore the dynamic performance of circulating fluidized bed(CFB) combustion ash subgrade under the driving load, the dynamic resilient modulus characteristics of GF and DTH combustion ashes under different curing ages, compaction degrees
Externí odkaz:
https://doaj.org/article/1fd7be7ec24d45b5a3b97b8cd3857a30
Graph neural networks (GNNs) hold the promise of learning efficient representations of graph-structured data, and one of its most important applications is semi-supervised node classification. However, in this application, GNN frameworks tend to fail
Externí odkaz:
http://arxiv.org/abs/2210.08251
Incorporating knowledge graphs (KGs) as side information in recommendation has recently attracted considerable attention. Despite the success in general recommendation scenarios, prior methods may fall short of performance satisfaction for the cold-s
Externí odkaz:
http://arxiv.org/abs/2209.13973
Autor:
Yu, Zhikai, Li, Bibo, Zhao, Shenglin, Du, Jia, Zhang, Yan, Liu, Xiu, Guo, Qing, Zhou, Hong, He, Mei
Publikováno v:
In International Journal of Nursing Studies May 2024 153
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems, 2019
Estimating covariance matrix from massive high-dimensional and distributed data is significant for various real-world applications. In this paper, we propose a data-aware weighted sampling based covariance matrix estimator, namely DACE, which can pro
Externí odkaz:
http://arxiv.org/abs/2010.04966
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering, 2019
Data-dependent hashing methods have demonstrated good performance in various machine learning applications to learn a low-dimensional representation from the original data. However, they still suffer from several obstacles: First, most of existing ha
Externí odkaz:
http://arxiv.org/abs/2010.04948
Publikováno v:
In Construction and Building Materials 15 September 2023 397
We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario. STAR-GCN employs a stack of GCN
Externí odkaz:
http://arxiv.org/abs/1905.13129
Point-of-interest (POI) recommendation that suggests new places for users to visit arises with the popularity of location-based social networks (LBSNs). Due to the importance of POI recommendation in LBSNs, it has attracted much academic and industri
Externí odkaz:
http://arxiv.org/abs/1607.00647