Sintel: A Machine Learning Framework to Extract Insights from Signals

Author:

Alnegheimish Sarah1,Liu Dongyu1,Sala Carles1,Berti-Equille Laure2,Veeramachaneni Kalyan1

Affiliation:

1. Massachusetts Institute of Technology, Cambridge, MA, USA

2. Institut de Recherche pour le Développement, Montpellier, France

Publisher

ACM

Reference51 articles.

1. Unsupervised real-time anomaly detection for streaming data

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

3. Sarah Alnegheimish. 2022. Reproducibility of Sintel: A Machine Learning Framework to Extract Insights from Signals. https://github.com/sarahmish/sintel-paper Sarah Alnegheimish. 2022. Reproducibility of Sintel: A Machine Learning Framework to Extract Insights from Signals. https://github.com/sarahmish/sintel-paper

4. Sarah Alnegheimish , Najat Alrashed , Faisal Aleissa , Shahad Althobaiti , Dongyu Liu , Mansour Alsaleh , and Kalyan Veeramachaneni . 2020 . Cardea: An Open Automated Machine Learning Framework for Electronic Health Records. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 536--545 . Sarah Alnegheimish, Najat Alrashed, Faisal Aleissa, Shahad Althobaiti, Dongyu Liu, Mansour Alsaleh, and Kalyan Veeramachaneni. 2020. Cardea: An Open Automated Machine Learning Framework for Electronic Health Records. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 536--545.

5. Samaneh Aminikhanghahi and Diane J Cook . 2017. A survey of methods for time series change point detection. Knowledge and information systems , Vol. 51 , 2 ( 2017 ), 339--367. Samaneh Aminikhanghahi and Diane J Cook. 2017. A survey of methods for time series change point detection. Knowledge and information systems, Vol. 51, 2 (2017), 339--367.

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