  Varying data augmentation policies and regularization over the course of
optimization has led to performance improvements over using fixed values. We
show that population based training is a useful tool to continuously search
those hyperparameters, within a fixed budget. This greatly simplifies the
experimental burden and computational cost of finding such optimal schedules.
We experiment in speech recognition by optimizing SpecAugment this way, as well
as dropout. It compares favorably to a baseline that does not change those
hyperparameters over the course of training, with an 8% relative WER
improvement. We obtain 5.18% word error rate on LibriSpeech's test-other.
