| Inteligencia artificial avanzada R Benítez, G Escudero, S Kanaan, DM Rodó Editorial UOC, 2014 | 240 | 2014 |
| Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs L Calvet, J De Armas, D Masip, AA Juan Open Mathematics 15 (1), 261-280, 2017 | 174 | 2017 |
| On the use of Monte Carlo simulation, cache and splitting techniques to improve the Clarke and Wright savings heuristics AA Juan, J Faulín, J Jorba, D Riera, D Masip, B Barrios Journal of the Operational Research Society 62 (6), 1085-1097, 2011 | 155 | 2011 |
| Supervised committee of convolutional neural networks in automated facial expression analysis G Pons, D Masip IEEE Transactions on Affective Computing 9 (3), 343-350, 2017 | 128 | 2017 |
| Combining statistical learning with metaheuristics for the multi-depot vehicle routing problem with market segmentation L Calvet, A Ferrer, MI Gomes, AA Juan, D Masip Computers & Industrial Engineering 94, 93-104, 2016 | 86 | 2016 |
| Interpreting cnn models for apparent personality trait regression C Ventura, D Masip, A Lapedriza Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 85 | 2017 |
| Automatic prediction of facial trait judgments: Appearance vs. structural models M Rojas Q, D Masip, A Todorov, J Vitria PloS one 6 (8), e23323, 2011 | 69 | 2011 |
| Multi-task, multi-label and multi-domain learning with residual convolutional networks for emotion recognition G Pons, D Masip arXiv preprint arXiv:1802.06664, 2018 | 62 | 2018 |
| Geometry-based ensembles: Toward a structural characterization of the classification boundary O Pujol, D Masip IEEE transactions on pattern analysis and machine intelligence 31 (6), 1140-1146, 2009 | 50 | 2009 |
| Explainable, automated urban interventions to improve pedestrian and vehicle safety C Bustos, D Rhoads, A Solé-Ribalta, D Masip, A Arenas, A Lapedriza, ... Transportation research part C: emerging technologies 125, 103018, 2021 | 36 | 2021 |
| Multitask, multilabel, and multidomain learning with convolutional networks for emotion recognition G Pons, D Masip IEEE Transactions on Cybernetics 52 (6), 4764-4771, 2020 | 35 | 2020 |
| A Deep Convolutional Neural Network for Classification of Aedes Albopictus Mosquitoes G Adhane, MM Dehshibi, D Masip IEEE Access 9, 72681-72690, 2021 | 34 | 2021 |
| Boosted online learning for face recognition D Masip, A Lapedriza, J Vitria IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39 …, 2008 | 33 | 2008 |
| Preferred spatial frequencies for human face processing are associated with optimal class discrimination in the machine MS Keil, A Lapedriza, D Masip, J Vitria PloS one 3 (7), e2590, 2008 | 31 | 2008 |
| Shared feature extraction for nearest neighbor face recognition D Masip, J Vitria IEEE Transactions on Neural Networks 19 (4), 586-595, 2008 | 29 | 2008 |
| Are external face features useful for automatic face classification? A Lapedriza, D Masip, J Vitria 2005 IEEE Computer Society Conference on Computer Vision and Pattern …, 2005 | 28 | 2005 |
| Boosted discriminant projections for nearest neighbor classification D Masip, J Vitrià Pattern Recognition 39 (2), 164-170, 2006 | 27 | 2006 |
| Quantile encoder: Tackling high cardinality categorical features in regression problems C Mougan, D Masip, J Nin, O Pujol Modeling Decisions for Artificial Intelligence: 18th International …, 2021 | 26 | 2021 |
| Online error correcting output codes S Escalera, D Masip, E Puertas, P Radeva, O Pujol Pattern Recognition Letters 32 (3), 458-467, 2011 | 26 | 2011 |
| An ensemble-based method for linear feature extraction for two-class problems D Masip, LI Kuncheva, J Vitria Pattern Analysis and Applications 8, 227-237, 2005 | 25 | 2005 |
Jordi VitriàProfessor of Computer Science, Universitat de BarcelonaВерификована је имејл адреса на ub.edu