  Unsupervised neural networks, such as restricted Boltzmann machines (RBMs)
and deep belief networks (DBNs), are powerful tools for feature selection and
pattern recognition tasks. We demonstrate that overfitting occurs in such
models just as in deep feedforward neural networks, and discuss possible
regularization methods to reduce overfitting. We also propose a "partial"
approach to improve the efficiency of Dropout/DropConnect in this scenario, and
discuss the theoretical justification of these methods from model convergence
and likelihood bounds. Finally, we compare the performance of these methods
based on their likelihood and classification error rates for various pattern
recognition data sets.
