Contrastive Divergence (CD) learning is frequently applied to Restricted Boltzmann Machines (RBMs), the building blocks of deep believe networks. It relies on biased approximations of the log-likelihood gradient. This bias can deteriorate the learning process. It was claimed that the signs of most components of the CD update are equal to the corresponding signs of the log-likelihood gradient. This suggests using optimization techniques only depending on the signs. Resilient backprop- agation is such a method and we combine it with CD learning. However, it does not prevent divergence caused by the approximation bias.
['Asja Fischer', 'Christian Igel']
Training RBMs Based on the Signs of the CD Approximation of the Log-likelihood Derivatives