| Discrimination-aware channel pruning for deep neural networks Z Zhuang, M Tan, B Zhuang, J Liu, Y Guo, Q Wu, J Huang, J Zhu Advances in Neural Information Processing Systems, 875-886, 2018 | 219 | 2018 |
| Stratified sampling for feature subspace selection in random forests for high dimensional data Y Ye, Q Wu, JZ Huang, MK Ng, X Li Pattern Recognition 46 (3), 769-787, 2013 | 133 | 2013 |
| ForesTexter: an efficient random forest algorithm for imbalanced text categorization Q Wu, Y Ye, H Zhang, MK Ng, SS Ho Knowledge-Based Systems 67, 105-116, 2014 | 89 | 2014 |
| A unified framework for metric transfer learning Y Xu, SJ Pan, H Xiong, Q Wu, R Luo, H Min, H Song IEEE Transactions on Knowledge and Data Engineering 29 (6), 1158-1171, 2017 | 81 | 2017 |
| Visual grounding via accumulated attention C Deng, Q Wu, Q Wu, F Hu, F Lyu, M Tan Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 73 | 2018 |
| Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests TT Nguyen, JZ Huang, Q Wu, TT Nguyen, MJ Li BMC genomics 16 (S2), S5, 2015 | 60 | 2015 |
| ML-FOREST: A multi-label tree ensemble method for multi-label classification Q Wu, M Tan, H Song, J Chen, MK Ng IEEE Transactions on Knowledge and Data Engineering 28 (10), 2665-2680, 2016 | 57 | 2016 |
| SNP selection and classification of genome-wide SNP data using stratified sampling random forests Q Wu, Y Ye, Y Liu, MK Ng IEEE transactions on nanobioscience 11 (3), 216-227, 2012 | 50 | 2012 |
| MR-NTD: Manifold regularization nonnegative tucker decomposition for tensor data dimension reduction and representation X Li, MK Ng, G Cong, Y Ye, Q Wu IEEE transactions on neural networks and learning systems 28 (8), 1787-1800, 2016 | 47 | 2016 |
| Online transfer learning with multiple homogeneous or heterogeneous sources Q Wu, H Wu, X Zhou, M Tan, Y Xu, Y Yan, T Hao IEEE Transactions on Knowledge and Data Engineering 29 (7), 1494-1507, 2017 | 38 | 2017 |
| Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation C Zhang, G Lin, F Liu, J Guo, Q Wu, R Yao Proceedings of the IEEE International Conference on Computer Vision, 9587-9595, 2019 | 33 | 2019 |
| Markov-Miml: A Markov chain-based multi-instance multi-label learning algorithm Q Wu, MK Ng, Y Ye Knowledge and information systems 37 (1), 83-104, 2013 | 33 | 2013 |
| Learning Discriminative Correlation Subspace for Heterogeneous Domain Adaptation. Y Yan, W Li, MKP Ng, M Tan, H Wu, H Min, Q Wu IJCAI, 3252-3258, 2017 | 31 | 2017 |
| ML-TREE: A tree-structure-based approach to multilabel learning Q Wu, Y Ye, H Zhang, TWS Chow, SS Ho IEEE transactions on neural networks and learning systems 26 (3), 430-443, 2014 | 30 | 2014 |
| Auto-embedding generative adversarial networks for high resolution image synthesis Y Guo, Q Chen, J Chen, Q Wu, Q Shi, M Tan IEEE Transactions on Multimedia 21 (11), 2726-2737, 2019 | 29 | 2019 |
| Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation. Y Yan, W Li, H Wu, H Min, M Tan, Q Wu IJCAI, 2969-2975, 2018 | 29 | 2018 |
| Online heterogeneous transfer by hedge ensemble of offline and online decisions Y Yan, Q Wu, M Tan, MK Ng, H Min, IW Tsang IEEE transactions on neural networks and learning systems 29 (7), 3252-3263, 2017 | 29 | 2017 |
| Adversarial learning with local coordinate coding J Cao, Y Guo, Q Wu, C Shen, J Huang, M Tan arXiv preprint arXiv:1806.04895, 2018 | 28 | 2018 |
| Cotransfer learning using coupled Markov chains with restart Q Wu, MK Ng, Y Ye IEEE intelligent Systems 29 (4), 26-33, 2013 | 28 | 2013 |
| Attention guided network for retinal image segmentation S Zhang, H Fu, Y Yan, Y Zhang, Q Wu, M Yang, M Tan, Y Xu International Conference on Medical Image Computing and Computer-Assisted …, 2019 | 26 | 2019 |
Mingkui TanSouth China University of TechnologyGeverifieerd e-mailadres voor scut.edu.cn
