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Gaussian Mean Field Regularizes by Limiting Learned Information
[article]
2019
arXiv
pre-print
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
arXiv:1902.04340v1
fatcat:nn3phevt25gktiognm46ckzkt4