  Deep directed generative models have attracted much attention recently due to
their expressive representation power and the ability of ancestral sampling.
One major difficulty of learning directed models with many latent variables is
the intractable inference. To address this problem, most existing algorithms
make assumptions to render the latent variables independent of each other,
either by designing specific priors, or by approximating the true posterior
using a factorized distribution. We believe the correlations among latent
variables are crucial for faithful data representation. Driven by this idea, we
propose an inference method based on the conditional pseudo-likelihood that
preserves the dependencies among the latent variables. For learning, we propose
to employ the hard Expectation Maximization (EM) algorithm, which avoids the
intractability of the traditional EM by max-out instead of sum-out to compute
the data likelihood. Qualitative and quantitative evaluations of our model
against state of the art deep models on benchmark datasets demonstrate the
effectiveness of the proposed algorithm in data representation and
reconstruction.
