A random energy approach to deep learning

We study a generic ensemble of deep belief networks (DBN) which is parametrized by the distribution of energy levels of the hidden states of each layer. We show that, within a random energy approach, statistical dependence can propagate from the visible to deep layers only if each layer is tuned close to the critical point during learning. As a consequence, efficiently trained learning machines are characterised by a broad distribution of energy levels. The analysis of DBNs and restricted Boltzmann machines on different datasets confirms these conclusions.