Good Initializations of Variational Bayes for Deep Models

  title={Good Initializations of Variational Bayes for Deep Models},
  author={Simone Rossi and Pietro Michiardi and Maurizio Filippone},
Stochastic variational inference is an established way to carry out approximate Bayesian inference for deep models. While there have been effective proposals for good initializations for loss minimization in deep learning, far less attention has been devoted to the issue of initialization of stochastic variational inference. We address this by proposing a novel layer-wise initialization strategy based on Bayesian linear models. The proposed method is extensively validated on regression and… CONTINUE READING
This paper has been referenced on Twitter 15 times. VIEW TWEETS