A random energy approach to deep learning

Rongrong Xie; Matteo Marsili

doi:10.1088/1742-5468/ac7794

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arXiv · 2112.09420

A random energy approach to deep learning

Rongrong Xie·Matteo Marsili

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Abstract

We study a generic ensemble of deep belief networks 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 Deep Belief Networks and Restricted Boltzmann Machines on different datasets confirms these conclusions.

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cond-mat.dis-nn cs.LG stat.ML

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BibTeX RIS

Rongrong Xie, Matteo Marsili. 2021-12-17. A random energy approach to deep learning. https://doi.org/10.1088/1742-5468%2Fac7794

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