MFE: towards reproducible meta-feature extraction (2020)
- Authors:
- USP affiliated authors: CARVALHO, ANDRÉ CARLOS PONCE DE LEON FERREIRA DE - ICMC ; SIQUEIRA, FELIPE ALVES - ICMC ; ALCOBAÇA NETO, EDESIO PINTO DE SOUZA - ICMC
- Unidade: ICMC
- Assunto: APRENDIZADO COMPUTACIONAL
- Keywords: AutoML; Meta-Learning; Meta-Features
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Journal of Machine Learning Research
- ISSN: 1532-4435
- Volume/Número/Paginação/Ano: v. 21, p. 1-5, 2020
-
ABNT
ALCOBAÇA, Edesio et al. MFE: towards reproducible meta-feature extraction. Journal of Machine Learning Research, v. 21, p. 1-5, 2020Tradução . . Disponível em: http://www.jmlr.org/papers/volume21/19-348/19-348.pdf. Acesso em: 02 out. 2024. -
APA
Alcobaça, E., Siqueira, F. A., Rivolli, A., Garcia, L. P. F., Oliva, J. T., & Carvalho, A. C. P. de L. F. de. (2020). MFE: towards reproducible meta-feature extraction. Journal of Machine Learning Research, 21, 1-5. Recuperado de http://www.jmlr.org/papers/volume21/19-348/19-348.pdf -
NLM
Alcobaça E, Siqueira FA, Rivolli A, Garcia LPF, Oliva JT, Carvalho ACP de LF de. MFE: towards reproducible meta-feature extraction [Internet]. Journal of Machine Learning Research. 2020 ; 21 1-5.[citado 2024 out. 02 ] Available from: http://www.jmlr.org/papers/volume21/19-348/19-348.pdf -
Vancouver
Alcobaça E, Siqueira FA, Rivolli A, Garcia LPF, Oliva JT, Carvalho ACP de LF de. MFE: towards reproducible meta-feature extraction [Internet]. Journal of Machine Learning Research. 2020 ; 21 1-5.[citado 2024 out. 02 ] Available from: http://www.jmlr.org/papers/volume21/19-348/19-348.pdf - Lessons learned from the NeurIPS 2021 MetaDL challenge: backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
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