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Edesio Alcobaça
Edesio Alcobaça
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Cited by
Cited by
Year
MFE: Towards reproducible meta-feature extraction
E Alcobaça, F Siqueira, A Rivolli, LPF Garcia, JT Oliva, AC De Carvalho
Journal of Machine Learning Research 21 (111), 1-5, 2020
982020
A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers
RG Mantovani, ALD Rossi, E Alcobaça, J Vanschoren, AC de Carvalho
Information Sciences 501, 193-221, 2019
79*2019
Explainable machine learning algorithms for predicting glass transition temperatures
E Alcobaça, SM Mastelini, T Botari, BA Pimentel, DR Cassar, ...
Acta materialia 188, 92-100, 2020
722020
Predicting and interpreting oxide glass properties by machine learning using large datasets
DR Cassar, SM Mastelini, T Botari, E Alcobaça, AC de Carvalho, ...
Ceramics international 47 (17), 23958-23972, 2021
252021
Machine learning unveils composition-property relationships in chalcogenide glasses
SM Mastelini, DR Cassar, E Alcobaça, T Botari, AC de Carvalho, ...
Acta Materialia 240, 118302, 2022
182022
Boosting meta-learning with simulated data complexity measures
LPF Garcia, A Rivolli, E Alcoba, AC Lorena, AC de Carvalho
Intelligent Data Analysis 24 (5), 1011-1028, 2020
112020
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
A El Baz, I Ullah, E Alcobaça, AC Carvalho, H Chen, F Ferreira, H Gouk, ...
NeurIPS 2021 Competitions and Demonstrations Track, 80-96, 2022
102022
Rethinking default values: a low cost and efficient strategy to define hyperparameters
RG Mantovani, ALD Rossi, E Alcobaça, JC Gertrudes, SB Junior, ...
arXiv preprint arXiv:2008.00025, 2020
72020
ACP d. LF de Carvalho, and ED Zanotto
E Alcobaca, SM Mastelini, T Botari, BA Pimentel, DR Cassar
Acta Mater 188, 92, 2020
52020
Dimensionality reduction for the algorithm recommendation problem
E Alcobaça, RG Mantovani, ALD Rossi, AC De Carvalho
2018 7th Brazilian Conference on Intelligent Systems (BRACIS), 318-323, 2018
42018
Predicting thermal, mechanical, and optical properties of oxide glasses by machine learning using large datasets
DR Cassar, SM Mastelini, T Botari, E Alcobaça, A de Carvalho, ...
arXiv preprint ArXiv:2009.03194, 2020
22020
Transfer learning for algorithm recommendation
GT Pereira, M Santos, E Alcobaça, R Mantovani, A Carvalho
arXiv preprint arXiv:1910.07012, 2019
12019
End-to-end data science (Pajé)
E Alcobaça, DP Santos, MR Santos, GT Pereira, RG Mantovani, ...
Resumos, 2019
2019
Supplementary Material for Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image …
A El Baz, I Ullah, E Alcobaça, AC Carvalho, H Chen, F Ferreira, H Gouk, ...
Feedback 51 (2,040), 2016
2016
SUPPLEMENTARY MATERIAL TO" EXPLAINABLE MACHINE LEARNING ALGORITHMS TO PREDICT GLASS TRANSITION TEMPERATURE
E Alcobaça, SM Mastelini, T Botari, BA Pimentel
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Articles 1–15