Rethinking Knowledge Graph Propagation for Zero-Shot Learning
Rethinking Knowledge Graph Propagation for Zero-Shot Learning(零样本的知识图谱传播再思考) 代码： https://github.com/cyvius96/adgpm. 摘要：图卷积神经网络对于零样本学习上有很大的潜力，模型能够很强的泛化能力生成新类，当缺少样本的时候，然而，多层架构需要在图中传播，传播的过程中使用拉普拉斯光滑会相应的
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相关文章
- 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. 论文阅读笔记《DPGN: Distribution Propagation Graph Network for Few-shot Learning》