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@INPROCEEDINGS{Fischer:1044669,
author = {Fischer, Kirsten and Dahmen, David and Helias, Moritz},
title = {{F}ield {T}heory of {R}esidual {N}etworks: {R}esidual
scaling leads to optimal signal propagation},
reportid = {FZJ-2025-03318},
year = {2025},
month = {Jul},
date = {2025-07-14},
organization = {The 29th International Conference on
Statistical Physics, Florence (Italy),
14 Jul 2025 - 18 Jul 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) /
RenormalizedFlows - Transparent Deep Learning with
Renormalized Flows (BMBF-01IS19077A) / ACA - Advanced
Computing Architectures (SO-092) / neuroIC002 - Recurrence
and stochasticity for neuro-inspired computation
(EXS-SF-neuroIC002)},
pid = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5234 /
G:(DE-Juel1)HGF-SMHB-2014-2018 /
G:(DE-Juel-1)BMBF-01IS19077A / G:(DE-HGF)SO-092 /
G:(DE-82)EXS-SF-neuroIC002},
typ = {PUB:(DE-HGF)6},
url = {https://juser.fz-juelich.de/record/1044669},
}