What is recorded here?
Used a recurrent controller trained with reinforcement learning to generate neural-network architectures and evaluated searched models on CIFAR-10 and Penn Treebank.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the exact two-author ICLR 2017 paper. It does not absorb later efficient neural-architecture-search methods, hardware-aware search, human engineering around the experiments or universal superiority claims.
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- Google Research record for neural architecture search
- OpenReview paper for neural architecture search
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People and systems
Barret Zoph; Quoc V. Le
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
Research locations and affiliations
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Documented countries / regions: United States
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English catalogue narrative
Zoph and Le: neural architecture search with reinforcement learning
Used a recurrent controller trained with reinforcement learning to generate neural-network architectures and evaluated searched models on CIFAR-10 and Penn Treebank.
Scientific scope
The record is the exact two-author ICLR 2017 paper. It does not absorb later efficient neural-architecture-search methods, hardware-aware search, human engineering around the experiments or universal superiority claims.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Barret Zoph; Quoc V. Le
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.