Learning Robust Deep State Space for Unsupervised Anomaly Detection in Contaminated Time-Series-Reference-Cited by-同舟云学术

Learning Robust Deep State Space for Unsupervised Anomaly Detection in Contaminated Time-Series

Author:

Li Longyuan¹,Yan Junchi²,Wen Qingsong³,Jin Yaohui¹,Yang Xiaokang⁴

Affiliation:

1. State Key Lab of Advanced Optical Communication System and Network, Shanghai Jiao Tong University, 12474 Shanghai, Shanghai, China

2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 12474 Shanghai, Shanghai, China, 200240

3. DAMO Academy, Alibaba Group, Bellevue, Washington, United States

4. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 12474 Shanghai, Shanghai, China

Funder

National Natural Science Foundation of China

China Major State Research Development Program

Shanghai Municipal Science and Technology Major Project

Alibaba Innovative Research Funding

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

Reference74 articles.

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2. Variational inference for Monte Carlo objectives;mnih;Proc Int Conf Mach Learn,2016

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