Spectral renormalization-group view of learning shows deep-network layers develop heavy-tailed spectra with strongest checkpoints near a power-law exponent α ≈ 2.
Read the original at arxiv.org→arXiv:2611.00422v1 Announce Type: new Abstract: We study how the layers in deep neural networks (NNs) converge, using Wilsonian Renormalization-Group (RG) ideas to organize the spectral picture. During training,...
Original headline: "A Spectral Renormalization-Group View of Learning"
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- Nov 3, 05:00 UTC arXiv cs.LG lead source A Spectral Renormalization-Group View of Learning