| 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 | 132 | 2011 |
| Inteligencia artificial avanzada R Benítez, G Escudero, S Kanaan, DM Rodó Editorial UOC, 2014 | 92 | 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 | 57 | 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 | 55 | 2016 |
| Automatic prediction of facial trait judgments: Appearance vs. structural models M Rojas, D Masip, A Todorov, J Vitria PloS one 6 (8), e23323, 2011 | 46 | 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 | 40 | 2017 |
| 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 | 39 | 2009 |
| 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 | 29 | 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 |
| Boosted discriminant projections for nearest neighbor classification D Masip, J Vitrià Pattern Recognition 39 (2), 164-170, 2006 | 27 | 2006 |
| 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 | 26 | 2018 |
| Boosted online learning for face recognition D Masip, A Lapedriza, J Vitria IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39 …, 2008 | 26 | 2008 |
| 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 | 25 | 2017 |
| An ensemble-based method for linear feature extraction for two-class problems D Masip, LI Kuncheva, J Vitria Pattern Analysis and Applications 8 (3), 227-237, 2005 | 24 | 2005 |
| 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 | 22 | 2005 |
| Emotion recognition from mid-level features D Sanchez-Mendoza, D Masip, A Lapedriza Pattern Recognition Letters 67, 66-74, 2015 | 20 | 2015 |
| Online error correcting output codes S Escalera, D Masip, E Puertas, P Radeva, O Pujol Pattern Recognition Letters 32 (3), 458-467, 2011 | 20 | 2011 |
| Student projects empowering mobile learning in higher education À Rius, D Masip, R Clarisó International Journal of Educational Technology in Higher Education 11 (1 …, 2014 | 17 | 2014 |
| Automated gesture tracking in head-fixed mice A Giovannucci, EA Pnevmatikakis, B Deverett, T Pereira, J Fondriest, ... Journal of neuroscience methods 300, 184-195, 2018 | 14 | 2018 |
| Feature extraction methods for real-time face detection and classification D Masip, M Bressan, J Vitria EURASIP Journal on Advances in Signal Processing 2005 (13), 1-11, 2005 | 14 | 2005 |
Jordi VitriàProfessor of Computer Science, Universitat de BarcelonaVerified email at ub.edu
