Training restricted Boltzmann machines: An introduction
作者：
Highlights：
•
摘要
Highlights•We review the state-of-the-art in training restricted Boltzmann machines (RBMs) from the perspective of graphical models.•Variants and extensions of RBMs are used in a wide range of pattern recognition tasks.•The required background on graphical models and Markov chain Monte Carlo methods is provided.•Theoretical and experimental results are presented.
论文关键词：Restricted Boltzmann machines,Markov random fields,Markov chains,Gibbs sampling,Neural networks,Contrastive divergence learning,Parallel tempering
论文评审过程：Author links open overlay panelAsjaFischerab1EnvelopeChristianIgelbPersonEnvelope
论文官网地址：https://doi.org/10.1016/j.patcog.2013.05.025
