Rethinking Knowledge Graph Propagation for Zero-Shot Learning
Rethinking Knowledge Graph Propagation for Zero-Shot Learning(零樣本的知識圖譜傳播再思考) 代碼： https://github.com/cyvius96/adgpm. 摘要：圖卷積神經網絡對於零樣本學習上有很大的潛力，模型能夠很強的泛化能力生成新類，當缺少樣本的時候，然而，多層架構需要在圖中傳播，傳播的過程中使用拉普拉斯光滑會相應的
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相關文章
- 1. DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
- 2. Transfer Learning for Item Recommendations and Knowledge Graph Completion
- 3. KBGAN: Adversarial Learning for Knowledge Graph Embeddings理解
- 4. Graph Few-shot learning via Knowledge Transfer
- 5. Knowledge Graph Convolutional Networks for Recommender Systems
- 6. Knowledge Graph Attention Network for Recommendation
- 7. Recurrent Knowledge Graph Embedding for Effective Recommendation
- 8. 20200427 Knowledge Graph Attention Network for Recommendation
- 9. Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph
- 10. 論文閱讀筆記《DPGN: Distribution Propagation Graph Network for Few-shot Learning》