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Artigo de periodico
One period to rule them all: identifying critical learning periods in deep networks (2025)
Fukase, Vinicius Yuiti; Gama, Heitor; Bueno, Barbara; Libanio, Lucas; Reali Costa, Anna Helena ; Correia, Artur Jordão Lima
Source: Procedia Computer Science. Unidade: EP
Subjects: REDES NEURAIS, APRENDIZAGEM PROFUNDA, CONSUMO DE ENERGIA ELÉTRICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FUKASE, Vinicius Yuiti et al. One period to rule them all: identifying critical learning periods in deep networks. Procedia Computer Science, v. 264, p. 270-279, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.procs.2025.07.138. Acesso em: 11 nov. 2025.
APA
Fukase, V. Y., Gama, H., Bueno, B., Libanio, L., Reali Costa, A. H., & Correia, A. J. L. (2025). One period to rule them all: identifying critical learning periods in deep networks. Procedia Computer Science, 264, 270-279. doi:10.1016/j.procs.2025.07.138
NLM
Fukase VY, Gama H, Bueno B, Libanio L, Reali Costa AH, Correia AJL. One period to rule them all: identifying critical learning periods in deep networks [Internet]. Procedia Computer Science. 2025 ; 264 270-279.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1016/j.procs.2025.07.138
Vancouver
Fukase VY, Gama H, Bueno B, Libanio L, Reali Costa AH, Correia AJL. One period to rule them all: identifying critical learning periods in deep networks [Internet]. Procedia Computer Science. 2025 ; 264 270-279.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1016/j.procs.2025.07.138
Artigo de periodico
Predicting the intention to use the investment aggregate functionality in the context of open banking using the artificial neural network approach (2023)
Pinochet, Luis Hernan Contreras ; Bastos, Debora Cristina Marcoli ; Pardim, Vanessa Itacaramby ; Sun, Violeta ; Santos, Marcos dos
Source: Procedia Computer Science. Unidades: EACH, FEA
Assunto: REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PINOCHET, Luis Hernan Contreras et al. Predicting the intention to use the investment aggregate functionality in the context of open banking using the artificial neural network approach. Procedia Computer Science, v. 221, p. 733-740, 2023Tradução . . Disponível em: http://dx.doi.org/10.1016/j.procs.2023.08.045. Acesso em: 11 nov. 2025.
APA
Pinochet, L. H. C., Bastos, D. C. M., Pardim, V. I., Sun, V., & Santos, M. dos. (2023). Predicting the intention to use the investment aggregate functionality in the context of open banking using the artificial neural network approach. Procedia Computer Science, 221, 733-740. doi:10.1016/j.procs.2023.08.045
NLM
Pinochet LHC, Bastos DCM, Pardim VI, Sun V, Santos M dos. Predicting the intention to use the investment aggregate functionality in the context of open banking using the artificial neural network approach [Internet]. Procedia Computer Science. 2023 ; 221 733-740.[citado 2025 nov. 11 ] Available from: http://dx.doi.org/10.1016/j.procs.2023.08.045
Vancouver
Pinochet LHC, Bastos DCM, Pardim VI, Sun V, Santos M dos. Predicting the intention to use the investment aggregate functionality in the context of open banking using the artificial neural network approach [Internet]. Procedia Computer Science. 2023 ; 221 733-740.[citado 2025 nov. 11 ] Available from: http://dx.doi.org/10.1016/j.procs.2023.08.045
Artigo de periodico
Adaptive traffic signal control based on bio-neural network (2017)
Castro, Guilherme Barbosa de; Hirakawa, André Riyuiti ; Martini, José Sidnei Colombo
Source: Procedia Computer Science. Unidade: EP
Subjects: SISTEMAS DINÂMICOS, REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CASTRO, Guilherme Barbosa de e HIRAKAWA, André Riyuiti e MARTINI, José Sidnei Colombo. Adaptive traffic signal control based on bio-neural network. Procedia Computer Science, v. 109, p. 1182-1187, 2017Tradução . . Disponível em: https://doi.org/10.1016/j.procs.2017.05.394. Acesso em: 11 nov. 2025.
APA
Castro, G. B. de, Hirakawa, A. R., & Martini, J. S. C. (2017). Adaptive traffic signal control based on bio-neural network. Procedia Computer Science, 109, 1182-1187. doi:10.1016/j.procs.2017.05.394
NLM
Castro GB de, Hirakawa AR, Martini JSC. Adaptive traffic signal control based on bio-neural network [Internet]. Procedia Computer Science. 2017 ; 109 1182-1187.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1016/j.procs.2017.05.394
Vancouver
Castro GB de, Hirakawa AR, Martini JSC. Adaptive traffic signal control based on bio-neural network [Internet]. Procedia Computer Science. 2017 ; 109 1182-1187.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1016/j.procs.2017.05.394

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