Papers with architecture search

2 papers
Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation (D19-56)

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Challenge: Neural sequence-to-sequence models are sensitive to architecture and hyperparameter settings.
Approach: They incorporate architecture search into a single training run through auto-sizing . they show that auto-size can improve BLEU scores by up to 3.9 points .
Outcome: The proposed algorithm improves BLEU scores on low-resource language pairs while removing one-third of the parameters from the model.
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking (2021.emnlp-main)

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Challenge: Existing methods require training millions of architectures to estimate the accuracy of the search results.
Approach: They propose a performance ranking method (RankNAS) that uses pairwise ranking and search space pruning to enlarge the search space.
Outcome: The proposed method significantly accelerates NAS through pairwise ranking and search space pruning.

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