Louis Jensen, Jayme Fosa, Ben Teitelbaum, Peter Chin. How Dense Autoencoders can still Achieve the State-of-the-art in Time-Series Anomaly Detection. In M. Arif Wani, Ishwar K. Sethi, Weisong Shi, Guangzhi Qu, Daniela Stan Raicu, Ruoming Jin, editors, 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021, Pasadena, CA, USA, December 13-16, 2021. pages 1272-1277, IEEE, 2021. [doi]
@inproceedings{JensenFTC21,
title = {How Dense Autoencoders can still Achieve the State-of-the-art in Time-Series Anomaly Detection},
author = {Louis Jensen and Jayme Fosa and Ben Teitelbaum and Peter Chin},
year = {2021},
doi = {10.1109/ICMLA52953.2021.00207},
url = {https://doi.org/10.1109/ICMLA52953.2021.00207},
researchr = {https://researchr.org/publication/JensenFTC21},
cites = {0},
citedby = {0},
pages = {1272-1277},
booktitle = {20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021, Pasadena, CA, USA, December 13-16, 2021},
editor = {M. Arif Wani and Ishwar K. Sethi and Weisong Shi and Guangzhi Qu and Daniela Stan Raicu and Ruoming Jin},
publisher = {IEEE},
isbn = {978-1-6654-4337-1},
}