Search Results for author: Qianru Zhang

Found 11 papers, 5 papers with code

A Survey of Generative Techniques for Spatial-Temporal Data Mining

no code implementations15 May 2024 Qianru Zhang, Haixin Wang, Cheng Long, Liangcai Su, Xingwei He, Jianlong Chang, Tailin Wu, Hongzhi Yin, Siu-Ming Yiu, Qi Tian, Christian S. Jensen

By integrating generative techniques and providing a standardized framework, the paper contributes to advancing the field and encourages researchers to explore the vast potential of generative techniques in spatial-temporal data mining.

Improving Factual Error Correction by Learning to Inject Factual Errors

no code implementations12 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.


Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation

1 code implementation19 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.

Contrastive Learning Graph Learning +1

Automated Spatio-Temporal Graph Contrastive Learning

1 code implementation6 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.

Contrastive Learning

On Inferring User Socioeconomic Status with Mobility Records

1 code implementation15 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.


PSDNet and DPDNet: Efficient channel expansion, Depthwise-Pointwise-Depthwise Inverted Bottleneck Block

no code implementations3 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.

Recent Advances in Convolutional Neural Network Acceleration

no code implementations23 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.

Image Classification

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