Detecting Anomalies with Autoencoders on Data Streams-Reference-Cited by-同舟云学术

Detecting Anomalies with Autoencoders on Data Streams

Published:2023 Issue: Volume: Page:258-274
ISSN:0302-9743
Container-title:Machine Learning and Knowledge Discovery in Databases
language:
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Author:

Cazzonelli Lucas^ORCID,Kulbach Cedric^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-26387-3_16

Reference32 articles.

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2. Bartos, M.D., Mullapudi, A., Troutman, S.C.: rrcf: implementation of the robust random cut forest algorithm for anomaly detection on streams. J. Open Source Softw. (2019). https://doi.org/10.21105/joss.01336

3. Baydin, A.G., et al.: Online learning rate adaptation with hypergradient descent. In: International Conference on Learning Representations (ICLR) (2018)

4. Bifet, A., et al.: Machine Learning for Data Streams: With Practical Examples in MOA. MIT Press, Cambridge (2018)

5. Borges, N., Meyer, G.G.L.: Coping with training contamination in unsupervised distributional anomaly detection. In: 2009 43rd Annual Conference on Information Sciences and Systems (CISS) (2009). https://doi.org/10.1109/CISS.2009.5054728

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