論文筆記——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
論文地址：https://arxiv.org/abs/1611.01578網絡
1. 論文思想
強化學習，用一個RNN學一個網絡參數的序列，而後將其轉換成網絡，而後訓練，獲得一個反饋，這個反饋做用於RNN網絡，用於生成新的序列。架構
2. 總體架構
3. RNN網絡
4. 具體實現
由於每生成一個網絡，都會訓練一遍，Google用了800個GPU，訓練了12800個網絡，它採用的是分佈式訓練的方法。分佈式
5. 結論
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相關文章
- 1. 論文筆記《Neural Architecture Search With Reinforcement Learning》
- 2. Neural Architecture Search with Reinforcement Learning論文總結
- 3. 論文筆記系列-Neural Architecture Search With Reinforcement Learning
- 4. NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 5. NAS論文筆記——Progressive Neural Architecture Search
- 6. 論文筆記：Progressive Neural Architecture Search
- 7. 網絡結構搜索（1）—— NAS（Neural architecture search with reinforcement learning）論文筆記
- 8. 《Neural Architecture Search with Reinforcement Learning》翻譯
- 9. 【論文筆記】NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING
- 10. Paper Reading:NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING