论文学习1-DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
文章目录 摘要 Introduction 最近工作 基于PRA的其他方法 其他使用强化学习的方法 我们的方法 方法介绍（强化学习 摘要 研究了大规模知识图的学习推理问题。更具体地说，我们描述了一个用于学习多跳关系路径的新型强化学习框架:我们使用一个基于知识图嵌入的具有连续状态的基于策略的代理，该代理通过采样最有希望的关系来扩展其路径，从而在KG向量空间中进行推理。与之前的工作相比，我们的方法包含一
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- 1. DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
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相关文章
- 1. DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
- 2. 论文Reinforcement Knowledge Graph Reasoning for Explainable Recommendation笔记
- 3. Variational Knowledge Graph Reasoning
- 4. 论文学习2-Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforce
- 5. Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations
- 6. 【论文阅读】Deep Reasoning with Knowledge Graph for Social Relationship Understanding
- 7. Differentiable Learning of Logical Rules for Knowledge Base Reasoning
- 8. RKGE Recurrent Knowledge Graph Embedding for Effective Recommendation 论文
- 9. Learning over Knowledge-Base Embeddings for Recommendation 论文
- 10. [Reinforcement Learning] Cross-entropy Method