Hyperband

Hyperband was developed to reduce inefficient resource usage in hyperparameter search. It tries many candidates briefly and then allocates more resources only to the promising ones. This allows weak candidates to be discarded before expensive full training runs are completed. It can be especially advantageous in deep learning and other settings with costly model evaluations.