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Algorithms for Hyper-Parameter Optimization - NeurIPS

Algorithms for Hyper-Parameter Optimization James Bergstra Re mi Bardenet The Rowland Institute Laboratoire de Recherche en Informatique Harvard University Universite Paris-Sud Yoshua Bengio Bala zs Ke gl De pt. d'Informatique et Recherche Ope rationelle Linear Accelerator Laboratory Universite de Montre al Universite Paris-Sud, CNRS. Abstract Several recent advances to the state of the art in image classification benchmarks have come from better configurations of existing techniques rather than novel ap- proaches to feature learning. Traditionally, Hyper-Parameter Optimization has been the job of humans because they can be very efficient in regimes where only a few trials are possible. Presently, computer clusters and GPU processors make it pos- sible to run more trials and we show that algorithmic approaches can find better results. We present Hyper-Parameter Optimization results on tasks of training neu- ral networks and deep belief networks (DBNs).

the efficiency of sequential optimization on the two hardest datasets according to random search. The paper concludes with discussion of results and concluding remarks in Section 7 and Section 8. 2 Sequential Model-based Global Optimization Sequential Model-Based Global Optimization (SMBO) algorithms have been used in many applica-

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