David Carlson

Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization

2016 · International Conference on Artificial Intelligence and Statistics

Belief networks are commonly used generative models of data, but require expensive posterior estimation to train and test the model. Learning typically proceeds by posterior sampling, variational approximations, or recognition networks, combined with stochastic optimization. …