| Euclidean distance estimation in incomplete datasets DPP Mesquita, JPP Gomes, AH Souza Junior, JS Nobre Neurocomputing, 2017 | 96 | 2017 |
| Rethinking pooling in graph neural networks D Mesquita, AH Souza, S Kaski Advances in Neural Information Processing Systems (NeurIPS) 2020, 2020 | 76 | 2020 |
| Classification with reject option for software defect prediction DPP Mesquita, LS Rocha, JPP Gomes, ARR Neto Applied Soft Computing 49, 1085-1093, 2016 | 45 | 2016 |
| Ensemble of efficient minimal learning machines for classification and regression DPP Mesquita, JPP Gomes, AH Souza Junior Neural Processing Letters 46, 751-766, 2017 | 32 | 2017 |
| Building selective ensembles of randomization based neural networks with the successive projections algorithm DPP Mesquita, JPP Gomes, LR Rodrigues, SAF Oliveira, RKH Galvao Applied Soft Computing 70, 1135-1145, 2018 | 28 | 2018 |
| Federated stochastic gradient Langevin dynamics KE Mekkaoui, D Mesquita, P Blomstedt, S Kaski Conference on Uncertainty in Artificial Intelligence (UAI) 2021, 2021 | 16* | 2021 |
| Gaussian kernels for incomplete data DPP Mesquita, JPP Gomes, F Corona, AHS Junior, JS Nobre Applied Soft Computing 77, 356-365, 2019 | 15 | 2019 |
| Embarrassingly parallel MCMC using deep invertible transformations D Mesquita, P Blomstedt, S Kaski Conference on Uncertainty in Artificial Intelligence (UAI) 2019, 2019 | 15 | 2019 |
| Pruning extreme learning machines using the successive projections algorithm DP Mesquita, J Gomes, LR Rodrigues, RK Galvao IEEE Latin America Transactions 13 (12), 3974-3979, 2015 | 15 | 2015 |
| Ensemble of minimal learning machines for pattern classification DPP Mesquita, JPP Gomes, AHS Junior Advances in Computational Intelligence: 13th International Work-Conference …, 2015 | 15 | 2015 |
| A minimal learning machine for datasets with missing values DPP Mesquita, JPP Gomes, AHS Jr Neural Information Processing: 22nd International Conference, ICONIP 2015 …, 2015 | 12 | 2015 |
| Artificial neural networks with random weights for incomplete datasets DPP Mesquita, JPP Gomes, LR Rodrigues Neural Processing Letters 50, 2345-2372, 2019 | 11 | 2019 |
| LS-SVR as a Bayesian RBF network DPP Mesquita, LA Freitas, JPP Gomes, CLC Mattos IEEE Transactions on Neural Networks and Learning Systems 31 (10), 4389-4393, 2019 | 8 | 2019 |
| Fast Co-MLM: An efficient semi-supervised Co-training method based on the minimal learning machine WL Caldas, JPP Gomes, DPP Mesquita New Generation Computing 36, 41-58, 2018 | 8 | 2018 |
| A Robust Minimal Learning Machine based on the M-Estimator. JPP Gomes, DPP Mesquita, A Freire, AHS Júnior, T Kärkkäinen ESANN, 2017 | 7 | 2017 |
| Provably expressive temporal graph networks AH Souza, D Mesquita, S Kaski, V Garg Advances in Neural Information Processing Systems (NeurIPS) 2022, 2022 | 6 | 2022 |
| Shrinkage k-means: a clustering algorithm based on the James-Stein estimator FFR Damasceno, MBA Veras, DPP Mesquita, JPP Gomes, CEF de Brito 2016 5th Brazilian Conference on Intelligent Systems (BRACIS), 433-437, 2016 | 6 | 2016 |
| A sparse linear regression model for incomplete datasets MBA Veras, DPP Mesquita, CLC Mattos, JPP Gomes Pattern Analysis and Applications 23, 1293-1303, 2020 | 4 | 2020 |
| Epanechnikov kernel for incomplete data DPP Mesquita, JPP Gomes, AH Souza Junior Electronics Letters 53 (21), 1408-1410, 2017 | 4 | 2017 |
| Extreme learning machines for datasets with missing values using the unscented transform DPP Mesquita, JPP Gomes, LR Rodrigues 2016 5th Brazilian Conference on Intelligent Systems (BRACIS), 85-90, 2016 | 4 | 2016 |
João Paulo Pordeus GomesFederal University of CearaVerified email at dc.ufc.br
