Renormalization in the neural network-quantum field theory correspondence

12/22/2022
by   Harold Erbin, et al.
0

A statistical ensemble of neural networks can be described in terms of a quantum field theory (NN-QFT correspondence). The infinite-width limit is mapped to a free field theory, while finite N corrections are mapped to interactions. After reviewing the correspondence, we will describe how to implement renormalization in this context and discuss preliminary numerical results for translation-invariant kernels. A major outcome is that changing the standard deviation of the neural network weight distribution corresponds to a renormalization flow in the space of networks.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/03/2021

Nonperturbative renormalization for the neural network-QFT correspondence

In a recent work arXiv:2008.08601, Halverson, Maiti and Stoner proposed ...
research
07/06/2023

Neural Network Field Theories: Non-Gaussianity, Actions, and Locality

Both the path integral measure in field theory and ensembles of neural n...
research
09/27/2021

The edge of chaos: quantum field theory and deep neural networks

We explicitly construct the quantum field theory corresponding to a gene...
research
08/19/2020

Neural Networks and Quantum Field Theory

We propose a theoretical understanding of neural networks in terms of Wi...
research
10/18/2017

A Correspondence Between Random Neural Networks and Statistical Field Theory

A number of recent papers have provided evidence that practical design q...
research
07/28/2022

p-Adic Statistical Field Theory and Deep Belief Networks

In this work we initiate the study of the correspondence between p-adic ...
research
02/21/2019

Correspondence Analysis Using Neural Networks

Correspondence analysis (CA) is a multivariate statistical tool used to ...

Please sign up or login with your details

Forgot password? Click here to reset