论文笔记《Neural Architecture Search With Reinforcement Learning》
摘要 神经网络是一种强大而灵活的模型，可以很好地解决图像、语音和天然语言理解中的许多困难学习任务。尽管成功，神经网络仍然很难设计。在本文中，咱们使用一个循环网络来生成神经网络的模型描述，并经过强化学习训练该RNN，以最大限度地提升生成的架构在验证集上的预期精度。在cifar-10数据集上，咱们的方法从无到能够设计出一种新的网络体系结构，在测试集精度方面能够与人类发明的最佳体系结构相媲美。咱们的ci
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相关文章
- 1. 论文笔记——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 2. 论文笔记《Neural Architecture Search With Reinforcement Learning》
- 3. Neural Architecture Search with Reinforcement Learning论文总结
- 4. NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 5. 论文笔记系列-Neural Architecture Search With Reinforcement Learning
- 6. 《Neural Architecture Search with Reinforcement Learning》翻译
- 7. Paper Reading:NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 8. 【论文笔记】NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING
- 9. 网络结构搜索（1）—— NAS（Neural architecture search with reinforcement learning）论文笔记
- 10. NAS论文笔记——Progressive Neural Architecture Search