Monthly Archives: June 2020

Criticality in deep neural nets

In the previous post, we introduced mean field theory (MFT) as a means of approximating the partition function for interacting systems. In particular, we used this to determine the critical point at which the system undergoes a phase transition, and … Continue reading

Posted in Minds & Machines | 2 Comments

Mean field theory: from physics to deep neural nets

In a previous post, I alluded to the question of whether criticality played any role in deep neural networks. The question I originally had in mind was whether the fact that the correlation length diverges at a critical point implies … Continue reading

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