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Artigo de periodico
Extended recommendation-by-explanation (2022)
Rana, Arplt ; D'Addio, Rafael Martins ; Manzato, Marcelo Garcia ; Bridge, Derek
Source: User Modeling and User-Adapted Interaction. Unidade: ICMC
Subjects: RECONHECIMENTO DE PADRÕES, RECUPERAÇÃO DA INFORMAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RANA, Arplt et al. Extended recommendation-by-explanation. User Modeling and User-Adapted Interaction, v. 32, n. 1-2, p. 91-131, 2022Tradução . . Disponível em: https://doi.org/10.1007/s11257-021-09317-4. Acesso em: 11 jul. 2024.
APA
Rana, A., D'Addio, R. M., Manzato, M. G., & Bridge, D. (2022). Extended recommendation-by-explanation. User Modeling and User-Adapted Interaction, 32( 1-2), 91-131. doi:10.1007/s11257-021-09317-4
NLM
Rana A, D'Addio RM, Manzato MG, Bridge D. Extended recommendation-by-explanation [Internet]. User Modeling and User-Adapted Interaction. 2022 ; 32( 1-2): 91-131.[citado 2024 jul. 11 ] Available from: https://doi.org/10.1007/s11257-021-09317-4
Vancouver
Rana A, D'Addio RM, Manzato MG, Bridge D. Extended recommendation-by-explanation [Internet]. User Modeling and User-Adapted Interaction. 2022 ; 32( 1-2): 91-131.[citado 2024 jul. 11 ] Available from: https://doi.org/10.1007/s11257-021-09317-4
Artigo de periodico
Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination (2022)
Aradhya, Abhay M. S ; Ashfahani, Andri; Angelina, Fienny; Pratama, Mahardhika ; Mello, Rodrigo Fernandes de ; Sundaram, Suresh
Source: Information Sciences. Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, COMPUTAÇÃO EVOLUTIVA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ARADHYA, Abhay M. S et al. Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination. Information Sciences, v. 607, p. 638-653, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2022.05.100. Acesso em: 11 jul. 2024.
APA
Aradhya, A. M. S., Ashfahani, A., Angelina, F., Pratama, M., Mello, R. F. de, & Sundaram, S. (2022). Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination. Information Sciences, 607, 638-653. doi:10.1016/j.ins.2022.05.100
NLM
Aradhya AMS, Ashfahani A, Angelina F, Pratama M, Mello RF de, Sundaram S. Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination [Internet]. Information Sciences. 2022 ; 607 638-653.[citado 2024 jul. 11 ] Available from: https://doi.org/10.1016/j.ins.2022.05.100
Vancouver
Aradhya AMS, Ashfahani A, Angelina F, Pratama M, Mello RF de, Sundaram S. Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination [Internet]. Information Sciences. 2022 ; 607 638-653.[citado 2024 jul. 11 ] Available from: https://doi.org/10.1016/j.ins.2022.05.100
Artigo de periodico
Predicting execution time of machine learning tasks for scheduling (2013)
Priya, Rattan; Souza, Bruno Feres de; Rossi, André L. D; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: International Journal of Hybrid Intelligent Systems. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PRIYA, Rattan et al. Predicting execution time of machine learning tasks for scheduling. International Journal of Hybrid Intelligent Systems, v. 10, p. 23-32, 2013Tradução . . Disponível em: https://doi.org/10.3233/HIS-130162. Acesso em: 11 jul. 2024.
APA
Priya, R., Souza, B. F. de, Rossi, A. L. D., & Carvalho, A. C. P. de L. F. de. (2013). Predicting execution time of machine learning tasks for scheduling. International Journal of Hybrid Intelligent Systems, 10, 23-32. doi:10.3233/HIS-130162
NLM
Priya R, Souza BF de, Rossi ALD, Carvalho ACP de LF de. Predicting execution time of machine learning tasks for scheduling [Internet]. International Journal of Hybrid Intelligent Systems. 2013 ; 10 23-32.[citado 2024 jul. 11 ] Available from: https://doi.org/10.3233/HIS-130162
Vancouver
Priya R, Souza BF de, Rossi ALD, Carvalho ACP de LF de. Predicting execution time of machine learning tasks for scheduling [Internet]. International Journal of Hybrid Intelligent Systems. 2013 ; 10 23-32.[citado 2024 jul. 11 ] Available from: https://doi.org/10.3233/HIS-130162

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