Papers by Lukas Ruff
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text (P19-1)
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| Challenge: | Existing methods for unsupervised anomaly detection use pre-trained word embeddings . proper text representation is critical for designing well-performing machine learning algorithms . |
| Approach: | They propose a new anomaly detection method that builds upon word embedding models to learn multiple sentence representations that capture multiple semantic contexts via the self-attention mechanism. |
| Outcome: | The proposed method performs on Reuters, 20 Newsgroups, and IMDB Movie Reviews datasets. |