Dropout Regularization in Hierarchical Mixture of Experts
Abstract
Dropout is a very effective method in preventing overfitting and has become the go-to regularizer for multi-layer neural networks in recent years. Hierarchical mixture of experts is a hierarchically gated model that defines a soft decision tree where leaves correspond to experts and decision nodes correspond to gating models that softly choose between its children, and as such, the model defines a soft hierarchical partitioning of the input space. In this work, we propose a variant of dropout for hierarchical mixture of experts that is faithful to the tree hierarchy defined by the model, as opposed to having a flat, unitwise independent application of dropout as one has with multi-layer perceptrons. We show that on a synthetic regression data and on MNIST and CIFAR-10 datasets, our proposed dropout mechanism prevents overfitting on trees with many levels improving generalization and providing smoother fits.
Cite
@article{arxiv.1812.10158,
title = {Dropout Regularization in Hierarchical Mixture of Experts},
author = {Ozan İrsoy and Ethem Alpaydın},
journal= {arXiv preprint arXiv:1812.10158},
year = {2018}
}