Weakly Supervised Anomaly Detection via Knowledge-Data Alignment-Reference-Cited by-同舟云学术

Weakly Supervised Anomaly Detection via Knowledge-Data Alignment

Published:2024-05-13 Issue: Volume:9 Page:4083-4094
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Zhao Haihong¹^ORCID,Zi Chenyi¹^ORCID,Liu Yang²^ORCID,Zhang Chen³^ORCID,Zhou Yan³^ORCID,Li Jia¹^ORCID

Affiliation:

1. Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

2. Hong Kong University of Science and Technology, Hong Kong SAR, China

3. CreateLink Technology, Hangzhou, China

Funder

National Natural Science Foundation of China

HKUST(GZ)-Chuanglin Graph Data Joint Lab and Guangzhou-HKUST(GZ) Joint Funding Scheme

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645429

Reference76 articles.

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3. Gary Bécigneul, Octavian-Eugen Ganea, Benson Chen, Regina Barzilay, and Tommi S Jaakkola. 2020. Optimal transport graph neural networks. In International Conference on Learning Representations.

4. WANG Botao, Jia Li, Yang Liu, Jiashun Cheng, Yu Rong, Wenjia Wang, and Fugee Tsung. 2023. Deep Insights into Noisy Pseudo Labeling on Graph Data. In Thirty-seventh Conference on Neural Information Processing Systems.

5. Yacine Bouzida and Frederic Cuppens. 2006. Neural networks vs. decision trees for intrusion detection. In IEEE/IST workshop on monitoring, attack detection and mitigation (MonAM), Vol. 28. 29.

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