Types of Learners

Deep Belief Networks

G. Hinton, S. Osindero, Y. Teh. A fast learning algorithm for deep belief nets. Neural computation (2006).

Dropout Restricted Boltzmann Machines

N. Srivastava, et al. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research (2014).

Energy-based Dropout Restricted Boltzmann Machines

M. Roder, G. H. de Rosa, A. L. D. Rossi, J. P. Papa. Energy-based Dropout in Restricted Boltzmann Machines: Why Do Not Go Random. Publication pending (2020).

Gaussian Restricted Boltzmann Machines

K. Cho, A. Ilin, T. Raiko. Improved learning of Gaussian-Bernoulli restricted Boltzmann machines. International conference on artificial neural networks (2011).

Restricted Boltzmann Machines

G. Hinton. A practical guide to training restricted Boltzmann machines. Neural networks: Tricks of the trade (2012).

Sigmoid Restricted Boltzmann Machines