Student-t Process Prior Variational Autoencoder for Anomaly Detection in Time Series

Author:

Fu Xiaowei,Bao Lei

Publisher

Springer Nature Singapore

Reference21 articles.

1. An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. Spec. Lect. IE 2, 1–18 (2015)

2. Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling (2018). arXiv preprint arXiv:1803.01271

3. Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM (JACM) 58(3), 11 (2011)

4. Gu, J., et al.: A multi-scale pyramid of fully convolutional networks for automatic cell detection. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 633–636. IEEE (2018)

5. Guo, Y., Liao, W., Wang, Q., Yu, L., Ji, T., Li, P.: Multidimensional time series anomaly detection: a GRU-based gaussian mixture variational autoencoder approach. In: Asian Conference on Machine Learning, pp. 97–112 (2018)

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