Summary of Unsupervised Event Outlier Detection in Continuous Time, by Somjit Nath et al.
Unsupervised Event Outlier Detection in Continuous Time
by Somjit Nath, Yik Chau Lui, Siqi Liu
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel unsupervised approach for detecting abnormal events in event sequence data using generative adversarial networks (GANs) and reinforcement learning (RL). The framework is based on correcting outliers in the data with a generator, which also serves as data augmentation for the discriminator. This online method outperforms state-of-the-art approaches in detecting event outliers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to find unusual events in continuous time data without needing human help. They use GANs and RL to develop an unsupervised approach that can detect abnormal events. Their unique idea is that the generator, which corrects errors, also helps train the discriminator by creating different anomalies. This online method does better than current methods for detecting unusual events. |
Keywords
» Artificial intelligence » Data augmentation » Reinforcement learning » Unsupervised
