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Random Search for Hyper-Parameter Optimization

Journal of Machine Learning Research 13 (2012) 281-305 Submitted 3/11; Revised 9/11; Published 2/12 Random Search for Hyper-Parameter OptimizationJames epartement d Informatique et de recherche op erationnelleUniversit e de Montr ealMontr eal, QC, H3C 3J7, CanadaEditor:Leon BottouAbstractGrid Search and manual Search are the most widely used strategies for Hyper-Parameter optimiza-tion. This paper shows empirically and theoretically that randomly chosen trials are more efficientfor Hyper-Parameter Optimization than trials on a grid. Empirical evidence comes from a compar-ison with a large previous study that used grid Search and manual Search to configure neural net-works and deep belief networks.

RANDOM SEARCH FOR HYPER-PARAMETER OPTIMIZATION search is used to identify regions in Λthat are promising and to develop the intuition necessary to choose the sets L(k).A major drawback of manual search is the difficulty in reproducing results.

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