Unlike DBNs, Deep Restricted Boltzmann Machines (DRBM) are undirected networks of interconnected hidden layers with the capability to learn joint probabilities over these connections. In the current setup, centering is performed where visible and hidden variables are subtracted from offset bias vectors after every iteration. Research has shown that centering optimizes the performance of DRBMs and can reach higher log-likelihood values in comparison with traditional RBMs.