Building a better probabilistic model of images by factorization

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Density Modeling of Images using a Generalized Normalization Transformation (2015, neutral): “…this induced density model more thoroughly to other model forms that have been proposed in the literature (e.g., ﬁnite mixtures of Gaussians or GSMs (Guerrero-Colón et al., 2008; Lyu & Simoncelli, 2009a; Zoran & Weiss, 2012; Theis et al., 2012), and sparse factorization (Culpepper et al., 2011)).”
Hamiltonian Monte Carlo Without Detailed Balance (2014, methodology): “(1)One method for choosing β (Culpepper et al., 2011) which we have found to be effective is to set it such that it randomizes a fixed fraction α of the momentum per unit simulation time,”
"Natural Images, Gaussian Mixtures and Dead Leaves" (2012, background): “Recently, there has been a growing interest in comparing different aspects of models for natural images such as log-likelihood and multi-information reduction performance, and much progress has been achieved [1,2, 3,4,5, 6].”
Tensor Analyzers (2013, methodology): “Culpepper et al. (2011) describe an outer-product factorization of the bilinear model that is trained as a density model using EM, but expensive Hamiltonian dynamics are required for sampling from the posterior.”