Types of Learners

Binary-based (Bernoulli-Bernoulli)

Convolutional Restricted Boltzmann Machines

H. Lee, et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proceedings of the 26th annual international conference on machine learning (2009).

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).

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).

Real-based (Gaussian-Bernoulli or Gaussian-Gaussian)

Gaussian Convolutional Restricted Boltzmann Machines

H. Lee, et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proceedings of the 26th annual international conference on machine learning (2009).

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).

Gaussian ReLU 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).

Stack-based (DBNs)

Deep Belief Networks

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

Residual Deep Belief Networks

M. Roder, et al. A Layer-Wise Information Reinforcement Approach to Improve Learning in Deep Belief Networks. International Conference on Artificial Intelligence and Soft Computing (2020).