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Marvin Ludersdorfer

Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series

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Jun 14, 2016
Maximilian Soelch, Justin Bayer, Marvin Ludersdorfer, Patrick van der Smagt
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Approximate variational inference has shown to be a powerful tool for modeling unknown complex probability distributions. Recent advances in the field allow us to learn probabilistic models of sequences that actively exploit spatial and temporal structure. We apply a Stochastic Recurrent Network (STORN) to learn robot time series data. Our evaluation demonstrates that we can robustly detect anomalies both off- and on-line.

* Accepted as workshop paper at ICLR 2016; accepted as workshop paper for anomaly detection workshop at ICML 2016 
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