Summary of Knowledge-augmented Explainable and Interpretable Learning For Anomaly Detection and Diagnosis, by Martin Atzmueller and Tim Bohne and Patricia Windler
Knowledge-Augmented Explainable and Interpretable Learning for Anomaly Detection and Diagnosis
by Martin Atzmueller, Tim Bohne, Patricia Windler
First submitted to arxiv on: 28 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 A novel approach to knowledge-augmented learning is presented, combining data-driven and knowledge-based methods for anomaly detection and diagnosis. In high-risk areas, understandability is crucial, making explainability and interpretability essential criteria. This chapter explores the application of knowledge-augmented, explainable, and interpretable learning to enhance transparency, understandability, and computational sensemaking. Techniques from simple interpretable methods to advanced neuro-symbolic approaches are demonstrated in the domains of anomaly detection and diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research combines data-driven and knowledge-based methods to detect and diagnose anomalies. The goal is to make it easy to understand how decisions are made, especially when it’s important. They show different ways to do this, from simple to complex, and apply them to detecting and diagnosing things that don’t belong. |
Keywords
* Artificial intelligence * Anomaly detection
