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Conference Papers Year : 2011

Algorithms for Hyper-Parameter Optimization

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). We optimize hyper-parameters using random search and two new greedy sequential methods based on the ex- pected improvement criterion. Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreli- able for training DBNs. The sequential algorithms are applied to the most difficult DBN learning problems from [1] and find significantly better results than the best previously reported. This work contributes novel techniques for making response surface models P(y|x) in which many elements of hyper-parameter assignment (x) are known to be irrelevant given particular values of other elements.
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Dates and versions

hal-00642998 , version 1 (20-11-2011)

Identifiers

  • HAL Id : hal-00642998 , version 1

Cite

James Bergstra, R. Bardenet, Yoshua Bengio, Balázs Kégl. Algorithms for Hyper-Parameter Optimization. 25th Annual Conference on Neural Information Processing Systems (NIPS 2011), Dec 2011, Granada, Spain. ⟨hal-00642998⟩
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