| Learning how to active learn: A deep reinforcement learning approach M Fang, Y Li, T Cohn Conference on Empirical Methods in Natural Language Processing, 2017 | 289 | 2017 |
| Iterative views agreement: An iterative low-rank based structured optimization method to multi-view spectral clustering Y Wang, W Zhang, L Wu, X Lin, M Fang, S Pan International Joint Conference on Artificial Intelligence, 2016 | 244 | 2016 |
| Saliency propagation from simple to difficult C Gong, D Tao, W Liu, SJ Maybank, M Fang, K Fu, J Yang IEEE Conference on Computer Vision and Pattern Recognition, 2531-2539, 2015 | 166 | 2015 |
| Liir: Learning individual intrinsic reward in multi-agent reinforcement learning Y Du, L Han, M Fang, J Liu, T Dai, D Tao Advances in Neural Information Processing Systems, 2019 | 139 | 2019 |
| Curriculum-guided hindsight experience replay M Fang, T Zhou, Y Du, L Han, Z Zhang Advances in Neural Information Processing Systems, 2019 | 126 | 2019 |
| Dual adversarial neural transfer for low-resource named entity recognition JT Zhou, H Zhang, D Jin, H Zhu, M Fang, RSM Goh, K Kwok Annual Meeting of the Association for Computational Linguistics, 3461-3471, 2019 | 106 | 2019 |
| Transfer hashing: From shallow to deep JT Zhou, H Zhao, X Peng, M Fang, Z Qin, RSM Goh IEEE Transactions on Neural Networks and Learning Systems 29 (12), 6191-6201, 2018 | 96 | 2018 |
| Bag: Bi-directional attention entity graph convolutional network for multi-hop reasoning question answering Y Cao, M Fang, D Tao Annual Conference of the North American Chapter of the Association for …, 2019 | 84 | 2019 |
| Revisiting metric learning for few-shot image classification X Li, L Yu, CW Fu, M Fang, PA Heng Neurocomputing 406, 49-58, 2020 | 83 | 2020 |
| Active learning for crowdsourcing using knowledge transfer M Fang, J Yin, D Tao AAAI Conference on Artificial Intelligence 28 (1), 2014 | 79 | 2014 |
| DHER: Hindsight experience replay for dynamic goals M Fang, C Zhou, B Shi, B Gong, J Xu, T Zhang International Conference on Learning Representations, 2018 | 78 | 2018 |
| Model transfer for tagging low-resource languages using a bilingual dictionary M Fang, T Cohn Annual Meeting of the Association for Computational Linguistics, 2017 | 65 | 2017 |
| Self-taught active learning from crowds M Fang, X Zhu, B Li, W Ding, X Wu IEEE International Conference on Data Mining, 858-863, 2012 | 55 | 2012 |
| Transfer learning across networks for collective classification M Fang, J Yin, X Zhu IEEE International Conference on Data Mining, 161-170, 2013 | 45 | 2013 |
| Enhancing the robustness of neural collaborative filtering systems under malicious attacks Y Du, M Fang, J Yi, C Xu, J Cheng, D Tao IEEE Transactions on Multimedia 21 (3), 555-565, 2018 | 43 | 2018 |
| Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection M Fang, T Cohn Conference on Computational Natural Language Learning, 2016 | 43 | 2016 |
| DAGN: Discourse-Aware Graph Network for Logical Reasoning Y Huang, M Fang, Y Cao, L Wang, X Liang Annual Conference of the North American Chapter of the Association for …, 2021 | 37 | 2021 |
| Trgraph: Cross-network transfer learning via common signature subgraphs M Fang, J Yin, X Zhu, C Zhang IEEE Transactions on Knowledge and Data Engineering 27 (9), 2536-2549, 2015 | 36 | 2015 |
| Unsupervised domain adaptation on reading comprehension Y Cao, M Fang, B Yu, JT Zhou Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 7480-7487, 2020 | 34 | 2020 |
| Deep reinforcement learning with stacked hierarchical attention for text-based games Y Xu, M Fang, L Chen, Y Du, JT Zhou, C Zhang Advances in Neural Information Processing Systems 33, 16495-16507, 2020 | 34 | 2020 |
Dacheng TaoThe University of SydneyVerified email at sydney.edu.au
