Designing Neural Network Architectures using Reinforcement Learning Designing Neural Network Architectures using Reinforcement Learning
Paper summary ## Ideas * Find CNN topology with Q-learning and $\varepsilon$-greedy exploration and experience replay ## Evaluation The authors seem not to know DenseNets * CIFAR-10: 6.92 % accuracy ([SOTA](https://martin-thoma.com/sota/#image-classification) is 3.46 % - not mentioned in the paper) * SVHN: 2.06 % accuracy ([SOTA](https://martin-thoma.com/sota/#image-classification) is 1.59% - not mentioned in the paper) * MNIST: 0.31 % ([SOTA](https://martin-thoma.com/sota/#image-classification) is 0.21 % - not mentioned in the paper) * CIFAR-100: 27.14 % accuracy ([SOTA](https://martin-thoma.com/sota/#image-classification) is 17.18 % - not mentioned in the paper) ## Related Work * Google: [Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578) ([summary](http://www.shortscience.org/paper?bibtexKey=journals/corr/1611.01578#martinthoma))
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Designing Neural Network Architectures using Reinforcement Learning
Bowen Baker and Otkrist Gupta and Nikhil Naik and Ramesh Raskar
arXiv e-Print archive - 2016 via Local arXiv
Keywords: cs.LG

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