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Oral
Understanding and Simplifying One-Shot Architecture Search
Gabriel Bender · Pieter-Jan Kindermans · Barret Zoph · Vijay Vasudevan · Quoc Le

Fri Jul 13 02:20 AM -- 02:40 AM (PDT) @ Victoria
in Deep Learning (Neural Network Architectures) 11 »

There is growing interest in automating neural network architecture design. Existing architecture search methods can be computationally expensive, requiring thousands of different architectures to be trained from scratch. Recent work has explored \emph{weight sharing} across models to amortize the cost of training. Although previous methods reduced the cost of architecture search by orders of magnitude, they remain complex, requiring hypernetworks or reinforcement learning controllers. We aim to understand weight sharing for one-shot architecture search. With careful experimental analysis, we show that it is possible to efficiently identify promising architectures from a complex search space without either hypernetworks or RL.

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Author Information

Gabriel Bender (Google)
Pieter-Jan Kindermans (Google)
Barret Zoph (Google)
Vijay Vasudevan (Google)
Quoc Le (Google Brain)

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