Semi-Supervised Anomaly Detection Via Neural Process-Reference-Cited by-同舟云学术

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Semi-Supervised Anomaly Detection Via Neural Process

Published:2023-10-01 Issue:10 Volume:35 Page:10423-10435
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Zhou Fan¹^ORCID,Wang Guanyu¹^ORCID,Zhang Kunpeng²^ORCID,Liu Siyuan³^ORCID,Zhong Ting¹^ORCID

Affiliation:

1. University of Electronic Science and Technology of China, Chengdu, Sichuan, China

2. Department of Decision, Operations & Information Technologies, University of Maryland, College park, MD, USA

3. Department of Supply Chain and Information Systems, Pennsylvania State University, State College, PA, USA

Funder

National Natural Science Foundation of China

Natural Science Foundation of Sichuan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10251471/10102264.pdf?arnumber=10102264

Reference50 articles.

1. ADBench: Anomaly Detection Benchmark

2. Anomaly detection in time series

3. LOF

4. Efficient algorithms for mining outliers from large data sets

5. A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

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