Summary of Xai-drop: Don’t Use What You Cannot Explain, by Vincenzo Marco De Luca et al.
xAI-Drop: Don’t Use What You Cannot Explain
by Vincenzo Marco De Luca, Antonio Longa, Andrea Passerini, Pietro Liò
First submitted to arxiv on: 29 Jul 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 presents a novel approach to improving the generalization capabilities of Graph Neural Networks (GNNs) by introducing explainability as a key indicator of model quality. The proposed xAI-Drop method leverages explainability to identify and exclude noisy network elements from the GNN propagation mechanism, leading to improved accuracy and explanation quality on diverse real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to help Graph Neural Networks (GNNs) learn better from graph data. They found that GNNs can get stuck if they’re trained on too much noisy or irrelevant information. To fix this, they created a method called xAI-Drop that uses special explanations to figure out which parts of the network are causing problems and removes them. This helps the GNN make more accurate predictions. |
Keywords
» Artificial intelligence » Generalization » Gnn
