Summary of Instance-aware Graph Prompt Learning, by Jiazheng Li et al.
Instance-Aware Graph Prompt Learning
by Jiazheng Li, Jundong Li, Chuxu Zhang
First submitted to arxiv on: 26 Nov 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 In this paper, researchers aim to address the limitations of current graph prompt learning methods by introducing Instance-Aware Graph Prompt Learning (IA-GPL). The approach generates distinct prompts tailored to different input instances using a lightweight architecture and trainable codebook vectors. This allows for more effective adaptation to diverse instances in downstream tasks. IA-GPL outperforms state-of-the-art baselines on multiple datasets and settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs are a key area of research in machine learning, with graph neural networks being used to learn representations of graph-structured data. However, these methods rely heavily on having high-quality labels available. To address this issue, researchers have proposed pretraining and fine-tuning models. IA-GPL takes this idea further by generating prompts that are tailored to specific instances in the data. This allows for more effective adaptation to different tasks and improves performance overall. |
Keywords
» Artificial intelligence » Fine tuning » Machine learning » Pretraining » Prompt
