Summary of Gets: Ensemble Temperature Scaling For Calibration in Graph Neural Networks, by Dingyi Zhuang et al.
GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks
by Dingyi Zhuang, Chonghe Jiang, Yunhan Zheng, Shenhao Wang, Jinhua Zhao
First submitted to arxiv on: 12 Oct 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 The proposed Graph Ensemble Temperature Scaling (GETS) framework addresses the issue of poor calibration in Graph Neural Networks (GNNs), which can lead to overconfidence or underconfidence in high-stakes applications. Existing methods often overlook the potential of leveraging diverse input information and model ensembles jointly. GETS combines input and model ensemble strategies within a Graph Mixture of Experts architecture, achieving a 25% reduction in expected calibration error across 10 GNN benchmark datasets. The framework is computationally efficient, scalable, and capable of selecting effective input combinations for improved calibration performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are super powerful at classifying things, but they often get it wrong when it comes to how sure they are about what they’re saying. This is a big problem in situations where you really need to know if the answer is 100% certain or just kinda sorta probably true. Some people have tried to fix this by making GNNs more careful, but these fixes don’t take into account all the different ways that GNNs can work together and use information from different places. The new GETS framework solves this problem by combining lots of different approaches in a clever way. It makes GNNs more accurate at guessing how sure they are, and it does this without using up too much computer power or taking forever to do its calculations. |
Keywords
» Artificial intelligence » Gnn » Mixture of experts » Temperature
