Exploiting local structure in Boltzmann machines - Schulz, Hannes; Müller, Andreas C; Behnke, Sven (Neurocomputing, 2010)
Restricted Boltzmann machines (RBM) are well-studied generative models. For image data, however, standard RBMs are suboptimal, since they do not exploit the local nature of image statistics. We modify RBMs to focus on local structure by restricting visible–hidden interactions. We model long-range dependencies using direct or indirect lateral interaction between hidden variables. While learning in our model is much faster, it retains generative and discriminative properties of RBMs of similar complexity.