# Regularization for Unsupervised Deep Neural Nets

arXiv: https://arxiv.org/abs/1608.04426 · HTML: https://mlsanity.com/paper/1608.04426/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2016.

## Built on by (most cited first, 1 of 1)

- [Energy-based Dropout in Restricted Boltzmann Machines: Why not go random](https://mlsanity.com/paper/2101.06741/) (2021)

## Abstract (arXiv)

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.