A Correspondence Between Random Neural Networks and Statistical Field Theory

Understand

A number of recent papers have provided evidence that practical design questions about neural networks may be tackled theoretically by studying the behavior of random networks.

• However, until now the tools available for analyzing random neural networks have been relatively ad-hoc.
• In this work, we show that the distribution of pre-activations in random neural networks can be exactly mapped onto lattice models in statistical physics.
• We argue that several previous investigations of stochastic networks actually studied a particular factorial approximation to the full lattice model.