Variational learning for rectified factor analysis

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Learning Sparse Neural Networks through L0 Regularization (2017, background): “…continuous r.v.s is not a new concept and has been previously done with Gaussian distributions in the context of generative modelling (Hinton & Ghahramani, 1997; Harva & Kabán, 2007; Salimans, 2016) and with logistic distributions at (Bengio et al., 2013) in the context of conditional computation.”
Rectified Factor Networks (2015, methodology): “For example, generative models with rectified priors, like rectified factor analysis, have zero posterior probability for negative values, therefore their means are positive and not sparse [10, 11].”