Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Machine Learning | Anomaly detection,Time series,Inference,Exploit,Probability distribution,Artificial intelligence,Probabilistic logic,Robot,Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1602.07109 | 10 |
PageRank | References | Authors |
0.64 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maximilian Sölch | 1 | 10 | 0.64 |
| Justin Bayer | 2 | 157 | 32.38 |
| Marvin Ludersdorfer | 3 | 19 | 1.62 |
| Patrick van der Smagt | 4 | 188 | 24.23 |