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Renormalization in the neural network-quantum field theory correspondence

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arxiv 2212.11811 v1 pith:NIS4CMGD submitted 2022-12-22 hep-th cond-mat.dis-nncs.LGstat.ML

classification hep-thcond-mat.dis-nncs.LGstat.ML
keywords correspondencefieldneuralrenormalizationtheorymappednetworkschanging
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Criticality analysis of nuclear binding energy neural networks

    nucl-th 2025-08 conditional novelty 5.0 of 10

    On a two-input nuclear binding energy network, the paper validates ANNFT predictions for variance, kurtosis, and an optimal depth-to-width ratio r*=0.034 under SGD, while adaptive optimizers obscure criticality.

  2. Bulk-boundary decomposition of neural networks

    cs.LG 2025-11 reject novelty 3.0 of 10

    The paper reframes SGD training of deep networks as a local Lagrangian with data confined to the boundaries, but the advertised energy continuity equation is absent from the body.

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