Name
Abstract | ||
|---|---|---|
In this paper, we present Paranom, a parallel anomaly dataset generator. We discuss its design and provide brief experimental results demonstrating its usefulness in improving the classification correctness of LSTM-AD, a state-of-the-art anomaly detection model. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Anomaly detection,Data mining,Correctness,Artificial intelligence,Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1801.03164 | 0 |
PageRank | References | Authors |
0.34 | 0 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Justin Gottschlich | 1 | 0 | 2.37 |