  Restricted Boltzmann Machines (RBMs) are probabilistic generative models that
can be trained by maximum likelihood in principle, but are usually trained by
an approximate algorithm called Contrastive Divergence (CD) in practice. In
general, a CD-k algorithm estimates an average with respect to the model
distribution using a sample obtained from a k-step Markov Chain Monte Carlo
Algorithm (e.g., block Gibbs sampling) starting from some initial
configuration. Choices of k typically vary from 1 to 100. This technical report
explores if it's possible to leverage a simple approximate sampling algorithm
with a modified version of CD in order to train an RBM with k=0. As usual, the
method is illustrated on MNIST.
