Paper Reading:NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING Motivation 神經網絡架構搜索的第一篇文章，將強化學習與深度學習結合。 其主要目的是使用強化學習尋找最優網絡，包括一個圖像分類網絡的卷積部分（表示層）和RNN的一個類似於LSTM的cell。由於現在的神經網絡一般採用堆疊block的方式搭建而成，這種堆疊的超參數可以通過一個序列來表
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相關文章
- 1. NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 2. 《Neural Architecture Search with Reinforcement Learning》翻譯
- 3. 論文筆記——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 4. 論文筆記《Neural Architecture Search With Reinforcement Learning》
- 5. Neural Architecture Search with Reinforcement Learning論文總結
- 6. （轉）The Evolved Transformer - Enhancing Transformer with Neural Architecture Search
- 7. 自學網絡結構（一）：Neural Architecture Search With Reinforcement Learning
- 8. Paper Reading:Efficient Neural Architecture Search via parameter Sharing
- 9. Neural architecture search
- 10. Research Guide for Neural Architecture Search