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Bag of Tricks for Neural Architecture Search

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arxiv 2107.03719 v1 pith:LKHB6EIG submitted 2021-07-08 cs.LG cs.AIstat.ML

Thomas Elsken , Benedikt Staffler , Arber Zela , Jan Hendrik Metzen , Frank Hutter This is my paper · ORCID
classification cs.LGcs.AIstat.ML
keywords searcharchitecturebeenneuralperformancesomebetterconsiderations
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While neural architecture search methods have been successful in previous years and led to new state-of-the-art performance on various problems, they have also been criticized for being unstable, being highly sensitive with respect to their hyperparameters, and often not performing better than random search. To shed some light on this issue, we discuss some practical considerations that help improve the stability, efficiency and overall performance.

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