% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Lindner:1052373,
      author       = {Lindner, Javed and Fischer, Kirsten and Dahmen, David and
                      Ringel, Zohar and Krämer, Michael and Helias, Moritz},
      title        = {{F}eature learning in deep neural networks close to
                      criticality},
      reportid     = {FZJ-2026-00967},
      year         = {2025},
      abstract     = {Neural networks excel due to their ability to learn
                      features, yet its theoretical understanding continues to be
                      a field of ongoing research. We develop a finite-width
                      theory for deep non-linear networks, showing that their
                      Bayesian prior is a superposition of Gaussian processes with
                      kernel variances inversely proportional to the network
                      width. In the proportional limit where both network width
                      and training samples scale as N,P→∞ with P/N fixed, we
                      derive forward-backward equations for the maximum a
                      posteriori kernels, demonstrating how layer representations
                      align with targets across network layers. A field-theoretic
                      approach links finite-width corrections of the network
                      kernels to fluctuations of the prior, bridging classical
                      edge-of-chaos theory with feature learning and revealing key
                      interactions between criticality, response, and network
                      scales.},
      month         = {Mar},
      date          = {2025-03-16},
      organization  = {DPG Spring Meeting of the Condensed
                       Matter Section, Regensburg (Germany),
                       16 Mar 2025 - 21 Mar 2025},
      subtyp        = {After Call},
      cin          = {IAS-6},
      cid          = {I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5232 - Computational Principles (POF4-523) / 5234 -
                      Emerging NC Architectures (POF4-523) / MSNN - Theory of
                      multi-scale neuronal networks (HGF-SMHB-2014-2018) / ACA -
                      Advanced Computing Architectures (SO-092) / GRK 2416 - GRK
                      2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung
                      neuronaler multisensorischer Integration (368482240)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5234 /
                      G:(DE-Juel1)HGF-SMHB-2014-2018 / G:(DE-HGF)SO-092 /
                      G:(GEPRIS)368482240},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1052373},
}