no code implementations • 1 Aug 2019 • João Pereira, Margarida Silveira
Unsupervised representation learning using deep generative models (e. g., variational autoencoders) has been used to learn expressive feature representations of sequences that can make downstream tasks, such as anomaly detection, easier to execute and more accurate.
no code implementations • 2019 IEEE International Conference on Big Data and Smart Computing (BigComp) 2019 • João Pereira, Margarida Silveira
Our results on the publicly available ECG5000 electrocardiogram dataset show the ability of the proposed approach to detect anomalous heartbeats in a fully unsupervised fashion, while providing structured and expressive data representations.
Ranked #1 on Unsupervised Anomaly Detection on ECG5000
no code implementations • 17 Dec 2018 • João Pereira, Margarida Silveira
In this paper, we propose a generic, unsupervised and scalable framework for anomaly detection in time series data, based on a variational recurrent autoencoder.