NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING 本文提出了一種基於梯度的神經結構搜索方法（見圖1） 我們的工作是基於這樣的觀察：神經網絡的結構和連接性通常可以由一個可變長度的字符串來指定。因此，可以使用一個遞歸網絡（控制器）來生成這樣的字符串。在真實數據上訓練由字符串指定的網絡——「子網絡」——在驗證集的準確率。使用這種準確率作爲獎勵信
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- 5. Paper Reading:NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
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相關文章
- 1. 論文筆記——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 2. 論文筆記《Neural Architecture Search With Reinforcement Learning》
- 3. Neural Architecture Search with Reinforcement Learning論文總結
- 4. 《Neural Architecture Search with Reinforcement Learning》翻譯
- 5. Paper Reading:NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 6. Neural architecture search
- 7. 《Multinomial Distribution Learning for Effective Neural Architecture Search》
- 8. （轉）The Evolved Transformer - Enhancing Transformer with Neural Architecture Search
- 9. Neural Architecture Search(NAS)
- 10. Research Guide for Neural Architecture Search