Zobrazeno 1 - 10
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pro vyhledávání: '"Huang, Zhenhua"'
Autor:
Su, Jinzhao, Huang, Zhenhua
In applications such as e-commerce, online education, and streaming services, sequential recommendation systems play a critical role. Despite the excellent performance of self-attention-based sequential recommendation models in capturing dependencies
Externí odkaz:
http://arxiv.org/abs/2407.13135
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
Graph neural networks (GNNs) are widely applied in graph data modeling. However, existing GNNs are often trained in a task-driven manner that fails to fully capture the intrinsic nature of the graph structure, resulting in sub-optimal node and graph
Externí odkaz:
http://arxiv.org/abs/2407.11361
Despite the Graph Neural Networks' (GNNs) proficiency in analyzing graph data, achieving high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters typically provide post-hoc explanations disjointed from GNNs' predicti
Externí odkaz:
http://arxiv.org/abs/2407.11358
Autor:
Ji, Lichuan, Lin, Yingqi, Huang, Zhenhua, Han, Yan, Xu, Xiaogang, Wu, Jiafei, Wang, Chong, Liu, Zhe
The development of AI-Generated Content (AIGC) has empowered the creation of remarkably realistic AI-generated videos, such as those involving Sora. However, the widespread adoption of these models raises concerns regarding potential misuse, includin
Externí odkaz:
http://arxiv.org/abs/2405.15343
Publikováno v:
Teshugang, Vol 45, Iss 2, Pp 56-60 (2024)
In order to study the influence of different refining processes on sulfides in ring gear steel 42CrMoS4 steel， starting from the morphology and distribution of sulfides in the steel， two different alkalinity slag and calcium content processes wer
Externí odkaz:
https://doaj.org/article/997a911965d046df9121e81b43a4f8fc
Autor:
Chen, Ziheng, Silvestri, Fabrizio, Wang, Jia, Zhang, Yongfeng, Huang, Zhenhua, Ahn, Hongshik, Tolomei, Gabriele
Recently, graph neural networks (GNNs) have been widely used to develop successful recommender systems. Although powerful, it is very difficult for a GNN-based recommender system to attach tangible explanations of why a specific item ends up in the l
Externí odkaz:
http://arxiv.org/abs/2208.04222
Publikováno v:
In Chemical Engineering and Processing - Process Intensification November 2024 205
Autor:
Wang, Xinqing, Chen, Shuo, Chen, Xiaolei, Wu, Juan, Huang, Zhenhua, Wang, Jing, Chen, Fangping, Liu, Changsheng
Publikováno v:
In Bioactive Materials November 2024 41:577-596
Autor:
Halvorson, Brady, Huang, Zhenhua
Publikováno v:
In Ocean Engineering 1 November 2024 311 Part 2
Autor:
Huang, Zhenhua, Zhang, Dong, Tong, Laiqiang, Gao, Fan, Zhang, Shaozan, Wang, Xinqing, Xie, Yina, Chen, Fangping, Liu, Changsheng
Publikováno v:
In Bioactive Materials November 2024 41:174-192