Gaussian Mean Field Regularizes by Limiting Learned Information
Year: 2019
Type: article
Abstract: Variational inference with a factorized Gaussian posterior estimate is a widely-used approach for learning parameters and hidden variables. Empirically, a regularizing effect can be observed that is poorly understood. In this work, we show how mean field inference improves generalization by limiting mutual information between learned parameters and the data through noise. We quantify a maximum capacity when the posterior variance is either fixed or learned and connect it to generalization error, even when the KL-divergence in the objective is scaled by a constant. Our experiments suggest that bounding information between parameters and data effectively regularizes neural networks on both supervised and unsupervised tasks.
Source: Entropy
DOI: 10.3390/e21080758
Authors: Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,621
OpenAlex ID: W2965278288