Types of Learners

Deep Belief Networks

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

Discriminative Restricted Boltzmann Machines

H. Larochelle and Y. Bengio. Classification using discriminative restricted Boltzmann machines. Proceedings of the 25th international conference on Machine learning (2008).

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).

Hybrid Discriminative Restricted Boltzmann Machines

H. Larochelle and Y. Bengio. Classification using discriminative restricted Boltzmann machines. Proceedings of the 25th international conference on Machine learning (2008).

Restricted Boltzmann Machines

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

Sigmoid Restricted Boltzmann Machines

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