[PDF][PDF] Random search for hyper-parameter optimization.

J Bergstra, Y Bengio - Journal of machine learning research, 2012 - jmlr.org
Grid search and manual search are the most widely used strategies for hyper-parameter
optimization. This paper shows empirically and theoretically that randomly chosen trials are
more efficient for hyper-parameter optimization than trials on a grid. Empirical evidence
comes from a comparison with a large previous study that used grid search and manual
search to configure neural networks and deep belief networks. Compared with neural
networks configured by a pure grid search, we find that random search over the same …