Efficient training of energy-based models via spin-glass control
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We present an efficient method for unsupervised learning using Boltzmann machines. The method is rooted in the control of the spin-glass properties of the Ising model described by the Boltzmann machine's weights. This allows for very easy access to low-energy configurations. We apply RAPID, the combination of Restricting the Axons (RA) of the model and training via Pattern-InDuced correlations (PID), to learn the Bars and Stripes dataset of various sizes and the MNIST dataset. We show how, in these tasks, RAPID quickly outperforms standard techniques for unsupervised learning in generalization ability. Indeed, both the number of epochs needed for effective learning and the computation time per training step are greatly reduced. In its simplest form, PID allows to compute the negative phase of the log-likelihood gradient with no Markov chain Monte Carlo sampling costs at all.
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