《Relational inductive biases, deep learning, and graph networks》论文解读（转载）
mark一下，感谢作者分享！ （后续还会补充） 研究背景 机器学习界有三个主要学派，符号主义（Symbolicism）、连接主义（Connectionism）、行为主义（Actionism）。 符号主义的起源，注重研究知识表达和逻辑推理。经过几十年的研究，目前这一学派的主要成果，一个是贝叶斯因果网络，另一个是知识图谱。 贝叶斯因果网络的旗手是 Judea Pearl 教授，2011年的图灵奖获得者
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相关文章
- 1. 《Relational inductive biases, deep learning, and graph networks》图网络 论文解读
- 2. Relational inductive biases, deep learning, and graph networks
- 3. Relational inductive biases, deep learning, and graph networks 论文导读，DeepMind图网络
- 4. 读书笔记7：Relational inductive biases, deep learning, and graph networks
- 5. GNN新作《Relational inductive biases，deep learning，and graph networks》读书笔记
- 6. 论文笔记11：Relational inductive biases, deep learning, and graph networks翻译笔记
- 7. GNN初学笔记（一）—— Relational inductive biases, deep learning, and graph networks简要解析
- 8. Learning about Graph Network
- 9. Inductive Representation Learning on Large Graphs 论文阅读
- 10. Neural Networks and Deep Learning-引论