1 code implementation • 25 Mar 2024 • Qianru Zhang, Lianghao Xia, Xuheng Cai, SiuMing Yiu, Chao Huang, Christian S. Jensen
To address these challenges, we propose a principled framework called GraphAug.
no code implementations • 12 Dec 2023 • Xingwei He, Qianru Zhang, A-Long Jin, Jun Ma, Yuan Yuan, Siu Ming Yiu
Given the lack of paired data (i. e., false claims and their corresponding correct claims), existing methods typically adopt the mask-then-correct paradigm.
1 code implementation • 19 Jun 2023 • Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, SiuMing Yiu, Ruihua Han
In addition, we introduce a cross-view contrastive learning paradigm to model the inter-dependencies across view-specific region representations and preserve underlying relation heterogeneity.
1 code implementation • 6 May 2023 • Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, Zhonghang Li, SiuMing Yiu
In this paper, we tackle the above challenges by exploring the Automated Spatio-Temporal graph contrastive learning paradigm (AutoST) over the heterogeneous region graph generated from multi-view data sources.
1 code implementation • 15 Nov 2022 • Zheng Wang, Mingrui Liu, Cheng Long, Qianru Zhang, Jiangneng Li, Chunyan Miao
The DeepSEI model incorporates two networks called deep network and recurrent network, which extract the features of the mobility records from three aspects, namely spatiality, temporality and activity, one at a coarse level and the other at a detailed level.
1 code implementation • 12 Nov 2022 • Qianru Zhang, Zheng Wang, Cheng Long, Chao Huang, Siu-Ming Yiu, Yiding Liu, Gao Cong, Jieming Shi
Detecting anomalous trajectories has become an important task in many location-based applications.
no code implementations • 6 Aug 2020 • Qianru Zhang, Meng Zhang, Chinthaka Gamanayake, Chau Yuen, Zehao Geng, Hirunima Jayasekara, Xuewen Zhang, Chia-wei Woo, Jenny Low, Xiang Liu
With some advanced algorithms, the new technologies are expected to control the production quality based on the digital images.
no code implementations • 16 May 2020 • Haoyan Xu, Ziheng Duan, Jie Feng, Runjian Chen, Qianru Zhang, Zhongbin Xu, Yueyang Wang
Next, a novel graph neural network with an attention mechanism is designed to map each subgraph into an embedding vector.
no code implementations • 3 Sep 2019 • Guoqing Li, Meng Zhang, Qianru Zhang, Ziyang Chen, Wenzhao Liu, Jiaojie Li, Xuzhao Shen, Jianjun Li, Zhenyu Zhu, Chau Yuen
To design more efficient lightweight concolutional neural netwok, Depthwise-Pointwise-Depthwise inverted bottleneck block (DPD block) is proposed and DPDNet is designed by stacking DPD block.
no code implementations • 23 Jul 2018 • Qianru Zhang, Meng Zhang, Tinghuan Chen, Zhifei Sun, Yuzhe ma, Bei Yu
We propose a taxonomy in terms of three levels, i. e.~structure level, algorithm level, and implementation level, for acceleration methods.