STRATEGIES FOR PRE-TRAINING GRAPH NEURAL NETWORKS
pre-train+fine-tune的策略已經廣泛地應用在了CV, NLP領域，但如何有效地利用圖數據集的預訓練仍然是一個有待解決的問題。本文提出了一種在node-level和graph-level同時學習的預訓練模型，能夠很好地得到圖的局部和全局的表示。實驗證明預訓練模型在ROC-AUC上超過了非預訓練模型9.4%，並在分子性質預測和蛋白質功能預測方面達到了最好的表現。 本文被ICLR2020
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相關文章
- 1. Strategies For Pre-Training Graph Neural Networks
- 2. 閱讀筆記--CAN GRAPH NEURAL NETWORKS GO 「ONLINE」? AN ANALYSIS OF PRETRAINING AND INFERENCE
- 3. 閱讀筆記-STRATEGIES FOR PRE-TRAINING GRAPH NEURAL NETWORKS
- 4. [ACL2017]Neural Word Segmentation with Rich Pretraining
- 5. Graph Neural Network Review
- 6. 筆記《Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels》-NeurIPS 2019
- 7. Cs224W 8.1-Graph Neural Network
- 8. CS224W-08-Graph Neural Networks
- 9. Introduction to Graph Neural Network翻譯-第四章Vanilla Graph Neural Networks
- 10. Adaptive Graph Convolutional Neural Networks