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Goal-driven optimization of single-neuron properties in artificial networks reveals regularization role of neural diversity and adaptation
View ORCID ProfileVictor Geadah, View ORCID ProfileStefan Horoi, Giancarlo Kerg, View ORCID ProfileGuy Wolf, View ORCID ProfileGuillaume Lajoie
doi: https://doi.org/10.1101/2022.04.29.489963
Victor Geadah
1Program in Applied and Computational Mathematics, Princeton University, Princeton, U.S.A.
2Mila - Quebec Artificial Intelligence Institute, Montréal, Canada
Stefan Horoi
2Mila - Quebec Artificial Intelligence Institute, Montréal, Canada
3Département de Mathématiques et Statistiques, Université de Montréal, Montréal, Canada
Giancarlo Kerg
2Mila - Quebec Artificial Intelligence Institute, Montréal, Canada
4Département d’Informatique et Recherche Opérationelle, Université de Montréal, Montréal, Canada
Guy Wolf
2Mila - Quebec Artificial Intelligence Institute, Montréal, Canada
3Département de Mathématiques et Statistiques, Université de Montréal, Montréal, Canada
5Canada CIFAR AI Chair
Guillaume Lajoie
2Mila - Quebec Artificial Intelligence Institute, Montréal, Canada
3Département de Mathématiques et Statistiques, Université de Montréal, Montréal, Canada
5Canada CIFAR AI Chair
Posted May 01, 2022.
Goal-driven optimization of single-neuron properties in artificial networks reveals regularization role of neural diversity and adaptation
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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