[C06] Romero Morais, Vuong Le, Svetha Venkatesh, & Truyen Tran (2021, June). Learning Asynchronous and Sparse Human-Object Interaction in Videos. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'21) (pp. 16041–16050). IEEE.
[C05] Romero Morais, Vuong Le, Truyen Tran, & Svetha Venkatesh (2020, September). Learning to Abstract and Predict Human Actions. In 2020 British Machine Vision Conference (BMVC'20).
[C04] Romero Morais, Vuong Le, Truyen Tran, Budhaditya Saha, Moussa Mansour, & Svetha Venkatesh (2019, June). Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'19) (pp. 11988–11996). IEEE.
[J01] Romero Morais & Germano Vasconcelos (2019, February). Boosting the Performance of Over-Sampling Algorithms through Under-Sampling the Minority Class. Neurocomputing, 343, 3–18.
[C03] Péricles Miranda, Romero Morais, & Ricardo Silva (2018, July). Using a Many-Objective Optimization Algorithm to Select Sampling Approaches for Imbalanced Datasets. In 2018 IEEE Congress on Evolutionary Computation (CEC) (pp. 1–7). IEEE.
[W01] Romero Morais & Germano Vasconcelos (2017, August). Under-Sampling the Minority Class to Improve the Performance of Over-Sampling Algorithms in Imbalanced Data Sets. In IJCAI 2017: Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17).
[C02] Romero Morais, Péricles Miranda, & Ricardo Silva (2017, April). A multi-criteria meta-learning method to select under-sampling algorithms for imbalanced datasets. In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (pp. 147–152).
[C01] Romero Morais, Péricles Miranda, & Ricardo Silva (2016, October). A Meta-Learning Method to Select Under-Sampling Algorithms for Imbalanced Data Sets. In Intelligent Systems (BRACIS), 2016 5th Brazilian Conference on (pp. 385–390). IEEE.