Rong Jin
Rong Jin
Alibaba Group
Zweryfikowany adres z cse.msu.edu - Strona główna
Cytowane przez
Cytowane przez
Understanding bag-of-words model: a statistical framework
Y Zhang, R Jin, ZH Zhou
International journal of machine learning and cybernetics 1, 43-52, 2010
Distance metric learning: A comprehensive survey
L Yang, R Jin
Michigan State Universiy 2 (2), 4, 2006
Active learning by querying informative and representative examples
SJ Huang, R Jin, ZH Zhou
Advances in neural information processing systems 23, 2010
Batch mode active learning and its application to medical image classification
SCH Hoi, R Jin, J Zhu, MR Lyu
Proceedings of the 23rd international conference on Machine learning, 417-424, 2006
Combining link and content for community detection: a discriminative approach
T Yang, R Jin, Y Chi, S Zhu
Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009
Semiboost: Boosting for semi-supervised learning
PK Mallapragada, R Jin, AK Jain, Y Liu
IEEE transactions on pattern analysis and machine intelligence 31 (11), 2000 …, 2008
Flexible mixture model for collaborative filtering
L Si, R Jin
Proceedings of the 20th International Conference on Machine Learning (ICML …, 2003
Learning with multiple labels
R Jin, Z Ghahramani
Advances in neural information processing systems 15, 2002
An automatic weighting scheme for collaborative filtering
R Jin, JY Chai, L Si
Proceedings of the 27th annual international ACM SIGIR conference on …, 2004
Discriminative semi-supervised feature selection via manifold regularization
Z Xu, I King, MRT Lyu, R Jin
IEEE Transactions on Neural networks 21 (7), 1033-1047, 2010
Semisupervised svm batch mode active learning with applications to image retrieval
SCH Hoi, R Jin, J Zhu, MR Lyu
ACM Transactions on Information Systems (TOIS) 27 (3), 1-29, 2009
Nyström method vs random fourier features: A theoretical and empirical comparison
T Yang, YF Li, M Mahdavi, R Jin, ZH Zhou
Advances in neural information processing systems 25, 2012
Detecting communities and their evolutions in dynamic social networks—a Bayesian approach
T Yang, Y Chi, S Zhu, Y Gong, R Jin
Machine learning 82, 157-189, 2011
Simple and efficient multiple kernel learning by group lasso
Z Xu, R Jin, H Yang, I King, MR Lyu
Proceedings of the 27th international conference on machine learning (ICML …, 2010
Large-scale text categorization by batch mode active learning
SCH Hoi, R Jin, MR Lyu
Proceedings of the 15th international conference on World Wide Web, 633-642, 2006
Online feature selection and its applications
J Wang, P Zhao, SCH Hoi, R Jin
IEEE Transactions on knowledge and data engineering 26 (3), 698-710, 2013
Multiple kernel learning for visual object recognition: A review
SS Bucak, R Jin, AK Jain
IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (7), 1354-1369, 2013
Extremely low bit neural network: Squeeze the last bit out with admm
C Leng, Z Dou, H Li, S Zhu, R Jin
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
Softtriple loss: Deep metric learning without triplet sampling
Q Qian, L Shang, B Sun, J Hu, H Li, R Jin
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
Multimedia search with pseudo-relevance feedback
R Yan, A Hauptmann, R Jin
Image and Video Retrieval: Second International Conference, CIVR 2003 Urbana …, 2003
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