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 | 115 | 2020 |
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 | 82 | 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 | 79 | 2020 |
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 | 31 | 2021 |
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 | 21 | 2022 |
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 | 13 | 2022 |
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 | 11 | 2020 |
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 | 9 | 2020 |
ACP de, LF de Carvalho and ED Zanotto E Alcobaca, SM Mastelini, T Botari, BA Pimentel, DR Cassar Acta Mater 188, 92-100, 2020 | 4 | 2020 |
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 | 4 | 2018 |
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 | 2 | 2020 |
Transfer learning for algorithm recommendation GT Pereira, M Santos, E Alcobaça, R Mantovani, A Carvalho arXiv preprint arXiv:1910.07012, 2019 | 2 | 2019 |
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 | | |