We propose a method for improving Belief Propagation (BP) that takes into account the influence of loops in the graphical model. The method is a variation on and generalization of the method recentlyâ€¦ (More)

In the current paper, the Promedas model for internal medicine, developed by our team, is introduced. The model is based on up-todate medical knowledge and consists of approximately 2000 diagnoses,â€¦ (More)

A toy model of a neural network in which both Hebbian learning and reinforcement learning occur is studied. The problem of 'path interference', which makes that the neural net quickly forgetsâ€¦ (More)

We reformulate the Cavity Approximation (CA), a class of algorithms recently introduced for improving the Bethe approximation estimates of marginals in graphical models. In our new formulation ,â€¦ (More)

A recurrent neural net is described that learns a set of patterns {Î¾ Âµ } in the presence of noise. The learning rule is of a Hebbian type, and, if noise would be absent during the learning process,â€¦ (More)

â€“ We investigate numerically the Cavity Approximation (CA), a class of algorithms recently introduced for improving the Bethe approximation estimates of marginals in graphical models. In the case ofâ€¦ (More)

In this paper we derive the equations for Loop Corrected Belief Propagation on a continuous variable Gaussian model. Using the exactness of the averages for belief propagation for Gaussian models, aâ€¦ (More)