  We present Neural Autoregressive Distribution Estimation (NADE) models, which
are neural network architectures applied to the problem of unsupervised
distribution and density estimation. They leverage the probability product rule
and a weight sharing scheme inspired from restricted Boltzmann machines, to
yield an estimator that is both tractable and has good generalization
performance. We discuss how they achieve competitive performance in modeling
both binary and real-valued observations. We also present how deep NADE models
can be trained to be agnostic to the ordering of input dimensions used by the
autoregressive product rule decomposition. Finally, we also show how to exploit
the topological structure of pixels in images using a deep convolutional
architecture for NADE.
