Training restricted Boltzmann machines: An introduction

@article{Fischer2014TrainingRB,
  title={Training restricted Boltzmann machines: An introduction},
  author={Asja Fischer and Christian Igel},
  journal={Pattern Recognition},
  year={2014},
  volume={47},
  pages={25-39}
}
Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can be interpreted as stochastic neural networks. They have attracted much attention as building blocks for the multi-layer learning systems called deep belief networks, and variants and extensions of RBMs have found application in a wide range of pattern recognition tasks. This tutorial introduces RBMs from the viewpoint of Markov random fields, starting with the required concepts of undirected graphical models… CONTINUE READING
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