921 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of SciencesCN55 papers
- University of Chinese Academy of SciencesCN34 papers
- Hong Kong University of Science and TechnologyHK6 papers
- Peking UniversityCN6 papers
- Institute of PhysicsPL5 papers
- National Astronomical ObservatoriesCN5 papers
- University of Central FloridaUS4 papers
- University of MacauMO4 papers
- University of Science and Technology of ChinaCN4 papers
- Beijing Normal UniversityCN3 papers
- Beijing University of Posts and TelecommunicationsCN3 papers
- City University of MacauMO3 papers
12 papers · 1 filter
Enhancing Job Salary Prediction with Disentangled Composition Effect Modeling: A Neural Prototyping Approach
Yang Ji, Ying Sun, Hengshu Zhu
In the era of the knowledge economy, understanding how job skills influence salary is crucial for promoting recruitment with competitive salary systems and aligned salary expectati…
A Comprehensive Survey on Self-Interpretable Neural Networks
Yang Ji, Ying Sun, Yuting Zhang +7
Neural networks have achieved remarkable success across various fields. However, the lack of interpretability limits their practical use, particularly in critical decision-making s…
Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data
Bo Pang, Sibo Cheng, Yuhan Huang +5
Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexi…
DATTA: Domain Diversity Aware Test-Time Adaptation for Dynamic Domain Shift Data Streams
Chuyang Ye, Dongyan Wei, Zhendong Liu +5
Test-Time Adaptation (TTA) addresses domain shifts between training and testing. However, existing methods assume a homogeneous target domain (e.g., single domain) at any given tim…
Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective
Yuchen Yan, Peiyan Zhang, Zheng Fang +1
The "Graph pre-training and fine-tuning" paradigm has significantly improved Graph Neural Networks(GNNs) by capturing general knowledge without manual annotations for downstream ta…
A Comprehensive Survey on Deep Graph Representation Learning
Wei Ju, Zheng Fang, Yiyang Gu +13
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been…
Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning
Lianghao Xia, Chao Huang, Yong Xu +4
Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, cri…
Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification
Meng Xiao, Ziyue Qiao, Yanjie Fu +2
To advance the development of science and technology, research proposals are submitted to open-court competitive programs developed by government agencies (e.g., NSF). Proposal cla…
Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification
Zhengzheng Tang, Ziyue Qiao, Xuehai Hong +4
Data augmentation aims to generate new and synthetic features from the original data, which can identify a better representation of data and improve the performance and generalizab…
Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification
Guoqiang Wu, Ruobing Zheng, Yingjie Tian +1
Multi-label classification studies the task where each example belongs to multiple labels simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) aims…
Stochastic Conjugate Gradient Algorithm with Variance Reduction
Xiao-Bo Jin, Xu-Yao Zhang, Kaizhu Huang +1
Conjugate gradient (CG) methods are a class of important methods for solving linear equations and nonlinear optimization problems. In this paper, we propose a new stochastic CG alg…
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval
Liang Wu, Hui Xiong, Liang Du +6
The problem of software artifact retrieval has the goal to effectively locate software artifacts, such as a piece of source code, in a large code repository. This problem has been…