  The restricted Boltzmann machine (RBM) is a representative generative model
based on the concept of statistical mechanics. In spite of the strong merit of
interpretability, unavailability of backpropagation makes it less competitive
than other generative models. Here we derive differentiable loss functions for
both binary and multinary RBMs. Then we demonstrate their learnability and
performance by generating colored face images.
