Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination (2022)
- Authors:
- Autor USP: MELLO, RODRIGO FERNANDES DE - ICMC
- Unidade: ICMC
- DOI: 10.1016/j.ins.2022.05.100
- Subjects: REDES NEURAIS; APRENDIZADO COMPUTACIONAL; COMPUTAÇÃO EVOLUTIVA
- Keywords: Evolving Intelligent Systems; Convolution Neural Networks; Deep learning; Evolutionary computing
- Language: Inglês
- Imprenta:
- Source:
- Título: Information Sciences
- ISSN: 0020-0255
- Volume/Número/Paginação/Ano: v. 607, p. 638-653, Aug. 2022
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
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: 17 fev. 2026. -
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 2026 fev. 17 ] 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 2026 fev. 17 ] Available from: https://doi.org/10.1016/j.ins.2022.05.100 - A novel approach to quantify novelty levels applied on ubiquitous music distribution
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Informações sobre o DOI: 10.1016/j.ins.2022.05.100 (Fonte: oaDOI API)
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