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Pub_2019

Year: [2019] {2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 }

2019

[Conference Paper][Journal Article]

Conference Paper
  • T.-Z. Wang, S.-J. Huang, and Z.-H. Zhou. Towards identifying causal relation between instances and labels. In: Proceedings of the 19th SIAM International Conference on Data Mining (SDM'19), Calgary, Canada, 2019.

  • Y.-Q. Yu, L. Fan, W.-J. Li. Ensemble Additive Margin Softmax for Speaker Verification. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019.

  • L.-Z. Guo, T. Han, Y.-F. Li. Robust Semi-Supervised Representation Learning for Graph-Structured Data. In: Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'19). Macau, China. 2019.

  • K. Yi, J.-X Wu. Probabilistic End-to-end Noise Correction for Learning with Noisy Labels. In: Proc. The IEEE Int'l Conference on Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA, USA, June 2019.

  • Y.-F. Li, H. Wang, T. Wei, W.-W. Tu. Towards Automated Semi-Supervised Learning. In: Proceedings of the 33rd AAAI conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • T. Wei, Y.-F. Li. Learning compact model for large-scale multi-label learning. In: Proceedings of the 33rd AAAI conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • J.-C. Shi, Y. Yu, Q. Da, S.-Y. Chen, and A.-X. Zeng. Virtual-Taobao: Virtualizing real-world online retail environment for reinforcement learning. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI’19), Honolulu, HI, 2019.

  • Y.-Q. Hu, Y. Yu, W.-W. Tu, Q. Yang, Y.-Q. Chen, and W.-Y. Dai. Multi-fidelity automatic hyper-parameter tuning via transfer series expansion. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI’19), Honolulu, HI, 2019.

  • Z.-J. Pang, R.-Z. Liu, Z.-Y. Meng, Y. Zhang, Y. Y, and T. Lu. On reinforcement learning for full-length game of StarCraft. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI’19), Honolulu, HI, 2019.

  • X.-R. Sheng, D.-C. Zhan, S. Lu, Y. Jiang. Multi-View Anomaly Detection: Neighborhood in Locality Matters. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19). Honolulu, Hawaii, 2019.

  • Y. Yang, Y.-F. Wu, D.-C. Zhan, Z.-B. Liu, Y. Jiang. Deep Robust Unsupervised Multi-Modal Network. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19). Honolulu, Hawaii, 2019.

  • Y.-Y. Zhang and M. Li. Find me if you can: Deep software clone detection by exploiting the contest between the plagiarist and the detector. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • S.-T. Shi, M. Li, D. Lo, F. Thung, and X. Huo. Automatic code review by learning the revision of source code. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • Z.-H. Tan, T. Zhang and W. Wang. Coreset Stochastic Variance-Reduced Gradient with Application to Optimal Margin Distribution MachineIn: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • B.-B. Yang and W. Gao. Weighted Oblique Decision TreesIn: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

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Journal Article and Book
  • M. Xu, Y.-F. Li, Z.-H. Zhou. Robust Multi-Label Learning with PRO Loss. In: IEEE Transactions on Knowledge and Data Engineering (TKDE). in press.

  • X.-S. Wei, C.-L. Zhang, J.-X. Wu, C.-H. Shen, Z.-H. Zhou. Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transformation. Pattern Recognition, 88, 113-126, 2019.

  • Y.-F. Li, D.-M. Liang. Safe Semi-Supervised Learning: A Brief Introduction. Frontiers of Computer Science (FCS). in press.

  • H.-J. Ye, D.-C. Zhan, N. Li, Y. Jiang. Learning Multiple Local Metrics: Global Consideration Helps. IEEE Transactions on Pattern Analysis and Machine Intelligence(TPAMI), 2019, in press.

  • C.-L. Zhang, J.-X. Wu. Improving CNN Linear Layers with Power Mean Non-Linearity. Pattern Recognition, 89, 12-21, 2019.

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