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Efficient Neural Architecture Search via Parameters Sharing
Hieu Pham · Melody Guan · Barret Zoph · Quoc Le · Jeff Dean

Wed Jul 11 08:20 AM -- 08:40 AM (PDT) @ Victoria
in Deep Learning (Neural Network Architectures) 5 »

We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. ENAS constructs a large computational graph, where each subgraph represents a neural network architecture, hence forcing all architectures to share their parameters. A controller is trained with policy gradient to search for a subgraph that maximizes the expected reward on a validation set. Meanwhile a model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Sharing parameters among child models allows ENAS to deliver strong empirical performances, whilst using much fewer GPU-hours than existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture Search. On Penn Treebank, ENAS discovers a novel architecture that achieves a test perplexity of 56.3, on par with the existing state-of-the-art among all methods without post-training processing. On CIFAR-10, ENAS finds a novel architecture that achieves 2.89% test error, which is on par with the 2.65% test error of NASNet (Zoph et al., 2018).

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Author Information

Hieu Pham (Carnegie Mellon University)
Melody Guan (Stanford University)
Barret Zoph (Google)
Quoc Le (Google Brain)
Jeff Dean (Google Brain)

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