Unsupervised Anomaly Detection & Diagnosis: A Stein Variational Gradient Descent Approach-Reference-Cited by-同舟云学术

Unsupervised Anomaly Detection & Diagnosis: A Stein Variational Gradient Descent Approach

Published:2023-10-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
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Author:

Chen Zhichao¹^ORCID,Ding Leilei¹^ORCID,Huang Jianmin¹^ORCID,Chu Zhixuan¹^ORCID,Dai Qingyang¹^ORCID,Wang Hao¹^ORCID

Affiliation:

1. Ant Group, Hangzhou, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583780.3615167

Reference21 articles.

1. Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization

2. GluonTS: Probabilistic and Neural Time Series Modeling in Python;Alexandrov Alexander;Journal of Machine Learning Research,2020

3. Mislav Balunovic , Anian Ruoss , and Martin Vechev . 2021 . Fair Normalizing Flows. In International Conference on Learning Representations. Mislav Balunovic, Anian Ruoss, and Martin Vechev. 2021. Fair Normalizing Flows. In International Conference on Learning Representations.

4. Songqiao Han Xiyang Hu Hailiang Huang Mingqi Jiang and Yue Zhao. 2022. ADBench: Anomaly Detection Benchmark. In Neural Information Processing Systems (NeurIPS). Songqiao Han Xiyang Hu Hailiang Huang Mingqi Jiang and Yue Zhao. 2022. ADBench: Anomaly Detection Benchmark. In Neural Information Processing Systems (NeurIPS).

5. Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

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