Summary of Cpt: Competence-progressive Training Strategy For Few-shot Node Classification, by Qilong Yan et al.
CPT: Competence-progressive Training Strategy for Few-shot Node Classification
by Qilong Yan, Yufeng Zhang, Jinghao Zhang, Jingpu Duan, Jian Yin
First submitted to arxiv on: 1 Feb 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 This paper proposes a novel approach to Graph Neural Networks (GNNs) in few-shot node classification, addressing the limitation of traditional episodic meta-learning methods. The proposed Curriculum-based Progressive Tasking (CPT) method leverages a two-stage curriculum learning strategy, starting with simpler tasks and gradually increasing difficulty as the model’s competence grows. This approach enhances overall performance on popular node classification datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn better from graphs with limited data. Currently, they need lots of labeled examples to be good at categorizing nodes. The team introduces a new way to train these machines using curriculum learning. This means starting with easy tasks and gradually getting harder as the machine gets smarter. This approach is shown to work well on common datasets. |
Keywords
* Artificial intelligence * Classification * Curriculum learning * Few shot * Meta learning
