Neural Architecture Search with Reinforcement Learning Neural Architecture Search with Reinforcement Learning
Paper summary _Objective:_ Design a network that will itself find the best architecture for a given task. _Dataset:_ [CIFAR10]( and [PTB]( ## Inner-workings: The meta-network (a RNN) generates a string specifying the child network parameters. Such a child network is then trained for 35-50 epochs and its accuracy is used as the reward to train the meta-network with Reinforcement Learning. The RNN first generates networks with few layers (6) then this number is increased as training progresses. ## Architecture: They develop one architecture for CNN where they predict each layers characteristic plus it's possible skip-connection: [![screen shot 2017-05-24 at 8 13 01 am](]( And one specific for LTSM-style: [![screen shot 2017-05-24 at 8 13 26 am](]( ## Distributed setting: Bellow is the distributed setting that they use with parameter servers connected to replicas (GPUs) that trained child networks. [![screen shot 2017-05-24 at 8 09 05 am](]( ## Results: Overall they trained 12800 networks on 800 GPUs but they achieve state of the art results which not human intervention except the vocabulary selection (activation type, type of cells, etc). Next step, transfer learning from one task to another for the meta-network?
Neural Architecture Search with Reinforcement Learning
Barret Zoph and Quoc V. Le
arXiv e-Print archive - 2016 via Local arXiv
Keywords: cs.LG, cs.AI, cs.NE


Summary by Martin Thoma 2 years ago
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