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@INPROCEEDINGS{Fischer:1010659,
      author       = {Fischer, Kirsten and Dahmen, David and Helias, Moritz},
      title        = {{O}ptimal signal propagation in {R}es{N}ets through
                      residual scaling},
      reportid     = {FZJ-2023-03174},
      year         = {2023},
      month         = {Jul},
      date          = {2023-07-31},
      organization  = {Statistical physics $\&$ machine
                       learning back together again, Cargese
                       (France), 31 Jul 2023 - 12 Aug 2023},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5232 - Computational Principles (POF4-523) / 5234 -
                      Emerging NC Architectures (POF4-523) / RenormalizedFlows -
                      Transparent Deep Learning with Renormalized Flows
                      (BMBF-01IS19077A) / MSNN - Theory of multi-scale neuronal
                      networks (HGF-SMHB-2014-2018) / 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-Juel-1)BMBF-01IS19077A /
                      G:(DE-Juel1)HGF-SMHB-2014-2018 / G:(DE-HGF)SO-092 /
                      G:(DE-82)EXS-SF-neuroIC002},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1010659},
}