论文笔记——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
论文地址：https://arxiv.org/abs/1611.01578网络
1. 论文思想
强化学习，用一个RNN学一个网络参数的序列，而后将其转换成网络，而后训练，获得一个反馈，这个反馈做用于RNN网络，用于生成新的序列。架构
2. 总体架构
3. RNN网络
4. 具体实现
由于每生成一个网络，都会训练一遍，Google用了800个GPU，训练了12800个网络，它采用的是分布式训练的方法。分布式
5. 结论
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相关文章
- 1. 论文笔记《Neural Architecture Search With Reinforcement Learning》
- 2. Neural Architecture Search with Reinforcement Learning论文总结
- 3. 论文笔记系列-Neural Architecture Search With Reinforcement Learning
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- 5. NAS论文笔记——Progressive Neural Architecture Search
- 6. 论文笔记：Progressive Neural Architecture Search
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- 8. 《Neural Architecture Search with Reinforcement Learning》翻译
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- 10. Paper Reading:NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING