Summary of Accurate and Scalable Estimation Of Epistemic Uncertainty For Graph Neural Networks, by Puja Trivedi et al.
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
by Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
First submitted to arxiv on: 7 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 training framework called G-is proposed to improve intrinsic graph neural network (GNN) uncertainty estimates. The framework adapts stochastic data centering to graph data through graph anchoring strategies, allowing for partially stochastic GNNs. This approach leads to better-calibrated GNNs for node and graph classification tasks under covariate, concept, and graph size shifts. Additionally, it improves performance on uncertainty-based tasks such as out-of-distribution detection and generalization gap estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary G-is a new way to make graph neural networks more accurate. It’s like a special kind of training that helps the network be more confident in its predictions. This can be useful when the network needs to predict things it hasn’t seen before. The researchers tested G-and found that it works well, even when the data is very different from what the network was trained on. |
Keywords
* Artificial intelligence * Classification * Generalization * Gnn * Graph neural network
