From the 4 of 151 papers with an AI index.
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- Tsinghua UniversityCN57 papers
- Peking UniversityCN56 papers
- University of Science and Technology of ChinaCN51 papers
- Nanjing UniversityCN50 papers
- Shanghai Jiao Tong UniversityCN50 papers
- Zhengzhou UniversityCN50 papers
- Guangxi UniversityCN49 papers
- Yunnan UniversityCN49 papers
- Shandong UniversityCN48 papers
- University of Chinese Academy of SciencesCN48 papers
- China Center of Advanced Science and TechnologyCN45 papers
- Nanjing Normal UniversityCN40 papers
5 papers · 1 filter
Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
Yingjie Zhou, Yuqin Xie, Fanxing Liu +3
Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, given only a limited number of…
Outlier detection in mixed-attribute data: a semi-supervised approach with fuzzy approximations and relative entropy
Baiyang Chen, Zhong Yuan, Zheng Liu +4
Outlier detection is a critical task in data mining, aimed at identifying objects that significantly deviate from the norm. Semi-supervised methods improve detection performance by…
Consistency-guided semi-supervised outlier detection in heterogeneous data using fuzzy rough sets
Baiyang Chen, Zhong Yuan, Dezhong Peng +2
Outlier detection aims to find samples that behave differently from the majority of the data. Semi-supervised detection methods can utilize the supervision of partial labels, thus…
Label-Informed Outlier Detection Based on Granule Density
Baiyang Chen, Zhong Yuan, Dezhong Peng +3
Outlier detection, crucial for identifying unusual patterns with significant implications across numerous applications, has drawn considerable research interest. Existing semi-supe…
Conditional Distribution Learning for Graph Classification
Jie Chen, Hua Mao, Chuanbin Liu +2
Leveraging the diversity and quantity of data provided by various graph-structured data augmentations while preserving intrinsic semantic information is challenging. Additionally,…