This work analyzes centered Restricted Boltzmann Machines (RBMs) and centered Deep Boltzmann Machines (DBMs), where centering is done by subtracting offset values from visible and hidden variables. We show analytically that (i) centered and normal Boltzmann Machines (BMs) and thus RBMs and DBMs are different parameterizations of the same model class, such that any normal BM/RBM/DBM can be trans...

‎a full nesterov-todd (nt) step infeasible interior-point algorithm‎ ‎is proposed for solving monotone linear complementarity problems‎ ‎over symmetric cones by using euclidean jordan algebra‎. ‎two types of‎ ‎full nt-steps are used‎, ‎feasibility steps and centering steps‎. ‎the‎ ‎algorithm starts from strictly feasible iterates of a perturbed‎ ‎problem‎, ‎and, using the central path and feasi...

Several theories specifying the causes of jealousy have been put forth in the past few decades. Firm support for any proposed theory, however, has been limited by the difficulties inherent in inducing jealousy and examining any proposed mediating mechanisms in real time. In support of a theory of jealousy centering on threats to the self-system, 2 experiments are presented that address these pa...

Recently there have been a number of works that model the zero pronoun resolution with the concept called `center.' However, the usefulness of the previous centering frameworks has not fully evaluated with naturally occurring discourses. Furthermore, the previous centering theory has handled only the phenomena in successive simple sentences and has not adequately addressed the way to handle com...

It has long been known that neural networks can learn faster when their input and hidden unit activities are centered about zero; recently we have extended this approach to also encompass the centering of error signals (Schraudolph and Sejnowski, 1996). Here we generalize this notion to all factors involved in the weight update, leading us to propose centering the slope of hidden unit activatio...

It has long been known that neural networks can learn faster when their input and hidden unit activities are centered about zero; recently we have extended this approach to also encompass the centering of error signals [2]. Here we generalize this notion to all factors involved in the network’s gradient, leading us to propose centering the slope of hidden unit activation functions as well. Slop...