Meng Fang
Cited by
Cited by
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
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
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
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
Active learning for crowdsourcing using knowledge transfer
M Fang, J Yin, D Tao
AAAI Conference on Artificial Intelligence 28 (1), 2014
Curriculum-guided hindsight experience replay
M Fang, T Zhou, Y Du, L Han, Z Zhang
Advances in Neural Information Processing Systems, 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
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
Model transfer for tagging low-resource languages using a bilingual dictionary
M Fang, T Cohn
Annual Meeting of the Association for Computational Linguistics, 2017
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
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
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
Revisiting metric learning for few-shot image classification
X Li, L Yu, CW Fu, M Fang, PA Heng
Neurocomputing 406, 49-58, 2020
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
Transfer learning across networks for collective classification
M Fang, J Yin, X Zhu
IEEE International Conference on Data Mining, 161-170, 2013
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
Networked bandits with disjoint linear payoffs
M Fang, D Tao
ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1106-1115, 2014
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
Teaching Semi-Supervised Classifier via Generalized Distillation.
C Gong, X Chang, M Fang, J Yang
International Joint Conference on Artificial Intelligence, 2156-2162, 2018
Active learning with uncertain labeling knowledge
M Fang, X Zhu
Pattern Recognition Letters 43, 98-108, 2014
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