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Regularization is an important component of predictive model building. Thehybrid bootstrap is a regularization technique that functions similarly todropout except that features are resampled from other training points ratherthan replaced with zeros. We show that the hybrid bootstrap offers superiorperformance to dropout. We also present a sampling based technique to simplifyhyperparameter choice. Next, we provide an alternative sampling technique forconvolutional neural networks. Finally, we demonstrate the efficacy of thehybrid bootstrap on non-image tasks using tree-based models.

The Hybrid Bootstrap: A Drop-in Replacement for Dropout