The flip-the-state transition operator for restricted Boltzmann machines

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A topological insight into restricted Boltzmann machines (2016, background): “…fast persistent contrastive divergence (Tieleman and Hinton, 2009), parallel tempering (Desjardins et al, 2010)), or to replace the Gibbs sampling with a transition operator for a faster mixing rate and to improve the learning accuracy without affecting computational costs (Brgge et al, 2013).”
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning (2016, methodology): “3 In this paper we follow the original scheme in [18,19,43] to train an RBM, but other approaches like [47] can also be applied.”
Training Restricted Boltzmann Machines (2015, methodology): “We propose [3] to replace Gibbs sampling by another transition operator from this family.”
Training restricted Boltzmann machines: An introduction (2014, background): “Recently, it has been shown that this indeed also holds true for RBMs [8].”