  Restricted Boltzmann Machines (RBMs) are a class of generative neural network
that are typically trained to maximize a log-likelihood objective function. We
argue that likelihood-based training strategies may fail because the objective
does not sufficiently penalize models that place a high probability in regions
where the training data distribution has low probability. To overcome this
problem, we introduce Boltzmann Encoded Adversarial Machines (BEAMs). A BEAM is
an RBM trained against an adversary that uses the hidden layer activations of
the RBM to discriminate between the training data and the probability
distribution generated by the model. We present experiments demonstrating that
BEAMs outperform RBMs and GANs on multiple benchmarks.
