On the magnitude of parameters of RBMs being universal approximators
Linyan Gu, Lihua Yang · 2016
This paper concentrates on the magnitude of parameters of restricted Boltzmann machines (RBMs) being universal approximators. It is known that when an RMB is used to compute a probability distribution with sufficient high accuracy, the magnitude of its parameters must tends to infinite unless the probability has a positive lower bound. In this paper, for any given error and probability, we provide a bound, by which there exits an RBM computing the the probability up to the error with parameters bounded. And the bound depends on the error and the input probability.