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We introduce a new family of energy-based probabilistic graphical models forefficient unsupervised learning. Its definition is motivated by the control ofthe spin-glass properties of the Ising model described by the weights ofBoltzmann machines. We use it to learn the Bars and Stripes dataset of varioussizes and the MNIST dataset, and show how they quickly achieve the performanceoffered by standard methods for unsupervised learning. Our results indicatethat the standard initialization of Boltzmann machines with random weightsequivalent to spin-glass models is an unnecessary bottleneck in the process oftraining. Furthermore, this new family allows for very easy access tolow-energy configurations, which points to new, efficient training algorithms.The simplest variant of such algorithms approximates the negative phase of thelog-likelihood gradient with no Markov chain Monte Carlo sampling costs at all,and with an accuracy sufficient to achieve good learning and generalization.

Efficient training of energy-based models via spin-glass control