Summary of Rege: a Method For Incorporating Uncertainty in Graph Embeddings, by Zohair Shafi et al.
REGE: A Method for Incorporating Uncertainty in Graph Embeddings
by Zohair Shafi, Germans Savcisens, Tina Eliassi-Rad
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 machine learning approach called Radius Enhanced Graph Embeddings (REGE) is proposed to tackle uncertainty in graph models used in real-world applications. REGE addresses two types of uncertainty: data noise and model output ambiguity, as well as targeted adversarial attacks that exacerbate these uncertainties. The method incorporates data uncertainty using curriculum learning and model output uncertainty using conformal learning. Experimental results demonstrate that REGE’s graph embeddings outperform state-of-the-art methods by an average of 1.5% (accuracy) under adversarial attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make machine learning models for graphs more reliable. Right now, these models can be tricked by fake data and have trouble knowing when they’re wrong. The authors created a special kind of map called Radius Enhanced Graph Embeddings that shows how certain the model is about its answers. They also used special techniques to make sure the model learns from good and bad data equally well. In tests, this approach worked better than other methods at protecting itself against fake data. |
Keywords
» Artificial intelligence » Curriculum learning » Machine learning
