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Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization

Victor Geadah, Stefan Horoi, Giancarlo Kerg, Guy Wolf, Guillaume Lajoie
doi: https://doi.org/10.1101/2022.04.29.489963

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doi 
https://doi.org/10.1101/2022.04.29.489963
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  • July 19, 2023.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

Author Information

  1. Victor Geadah1,2,3,
  2. Stefan Horoi2,3,
  3. Giancarlo Kerg2,4,
  4. Guy Wolf2,3,5 and
  5. Guillaume Lajoie2,3,5,*
  1. 1Program in Applied and Computational Mathematics, Princeton University, Princeton, U.S.A.
  2. 2Mila - Quebec Artificial Intelligence Institute, Montréal, Canada
  3. 3Département de Mathématiques et Statistiques, Université de Montréal, Montréal, Canada
  4. 4Département d’Informatique et Recherche Opérationelle, Université de Montréal, Montréal, Canada
  5. 5Canada CIFAR AI Chair
  1. ↵*Correspondence should be addressed to G.L. (g.lajoie{at}umontreal.ca).
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Posted July 19, 2023.
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Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization
Victor Geadah, Stefan Horoi, Giancarlo Kerg, Guy Wolf, Guillaume Lajoie
bioRxiv 2022.04.29.489963; doi: https://doi.org/10.1101/2022.04.29.489963
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