A Bayesian encourages dropout

TL;DR

This paper provides a Bayesian perspective on dropout, showing how it acts as a form of regularization and how Bayesian methods can optimize dropout rates for improved learning and prediction.

Contribution

It introduces a Bayesian interpretation of dropout, enabling the optimization of dropout rates to enhance model training and predictive performance.

Findings

Bayesian interpretation of dropout as regularization

Optimizing dropout rate improves learning

Enhanced prediction accuracy after dropout optimization

Abstract

Dropout is one of the key techniques to prevent the learning from overfitting. It is explained that dropout works as a kind of modified L2 regularization. Here, we shed light on the dropout from Bayesian standpoint. Bayesian interpretation enables us to optimize the dropout rate, which is beneficial for learning of weight parameters and prediction after learning. The experiment result also encourages the optimization of the dropout.