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2020 – today
- 2024
[j27]Feng Sun, Ming-Kun Xie, Sheng-Jun Huang
:
A Deep Model for Partial Multi-label Image Classification with Curriculum-based Disambiguation. Mach. Intell. Res. 21(4): 801-814 (2024)- [j26]Jia-Yao Chen, Shao-Yuan Li, Sheng-Jun Huang, Songcan Chen, Lei Wang, Ming-Kun Xie:
UNM: A Universal Approach for Noisy Multi-Label Learning. IEEE Trans. Knowl. Data Eng. 36(9): 4968-4980 (2024) - [c51]Wenhai Wan, Xinrui Wang, Ming-Kun Xie, Shao-Yuan Li, Sheng-Jun Huang, Songcan Chen:
Unlocking the Power of Open Set: A New Perspective for Open-Set Noisy Label Learning. AAAI 2024: 15438-15446 - [c50]Chen-Chen Zong, Ye-Wen Wang, Ming-Kun Xie, Sheng-Jun Huang:
Dirichlet-Based Prediction Calibration for Learning with Noisy Labels. AAAI 2024: 17254-17262 - [c49]Sheng-Jun Huang, Yi Li, Yiming Sun, Ying-Peng Tang:
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models. ICLR 2024 - [c48]Ye-Wen Wang, Chen-Chen Zong, Ming-Kun Xie, Sheng-Jun Huang:
Dirichlet-Based Coarse-to-Fine Example Selection For Open-Set Annotation. ICME 2024: 1-6 - [c47]Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. ICML 2024 - [c46]Ye Li, Siqi Chen, Bin Shan, Sheng-Jun Huang:
Causality-enhanced Discreted Physics-informed Neural Networks for Predicting Evolutionary Equations. IJCAI 2024: 4497-4505 - [c45]Shiji Zhao, Shao-Yuan Li, Sheng-Jun Huang:
NanoAdapt: Mitigating Negative Transfer in Test Time Adaptation with Extremely Small Batch Sizes. IJCAI 2024: 5572-5580 - [c44]Hao-Zhe Liu, Ming-Kun Xie, Chen-Chen Zong, Sheng-Jun Huang:
Asymmetric Beta Loss for Evidence-Based Safe Semi-Supervised Multi-Label Learning. KDD 2024: 1909-1920 - [i34]Chen-Chen Zong, Ye-Wen Wang, Ming-Kun Xie, Sheng-Jun Huang:
Dirichlet-Based Prediction Calibration for Learning with Noisy Labels. CoRR abs/2401.07062 (2024) - [i33]Jing-Cheng Pang, Heng-Bo Fan, Pengyuan Wang, Jiahao Xiao, Nan Tang, Si-Hang Yang, Chengxing Jia, Sheng-Jun Huang, Yang Yu:
Empowering Language Models with Active Inquiry for Deeper Understanding. CoRR abs/2402.03719 (2024) - [i32]Chen-Chen Zong, Ye-Wen Wang, Kun-Peng Ning, Haibo Ye, Sheng-Jun Huang:
Bidirectional Uncertainty-Based Active Learning for Open Set Annotation. CoRR abs/2402.15198 (2024) - [i31]Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. CoRR abs/2404.06287 (2024) - [i30]Hamed Hemati, Lorenzo Pellegrini, Xiaotian Duan, Zixuan Zhao, Fangfang Xia, Marc Masana, Benedikt Tscheschner, Eduardo Veas, Yuxiang Zheng, Shiji Zhao, Shao-Yuan Li, Sheng-Jun Huang, Vincenzo Lomonaco, Gido M. van de Ven:
Continual Learning in the Presence of Repetition. CoRR abs/2405.04101 (2024) - [i29]Yuyan Zhou, Ye Li, Lei Feng, Sheng-Jun Huang:
Improving Generalization of Deep Neural Networks by Optimum Shifting. CoRR abs/2405.14111 (2024) - [i28]Sheng-Jun Huang, Yi Li, Yiming Sun, Ying-Peng Tang:
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models. CoRR abs/2405.14121 (2024) - [i27]Dong Liang, Yue Sun, Yun Du, Songcan Chen, Sheng-Jun Huang:
Relative Difficulty Distillation for Semantic Segmentation. CoRR abs/2407.03719 (2024) - [i26]Jiahao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning. CoRR abs/2407.18624 (2024) - [i25]Weijie Lv, Xuan Xia, Sheng-Jun Huang:
CodeACT: Code Adaptive Compute-efficient Tuning Framework for Code LLMs. CoRR abs/2408.02193 (2024) - 2023
- [j25]Ye Shi, Shao-Yuan Li, Sheng-Jun Huang:
Learning from crowds with sparse and imbalanced annotations. Mach. Learn. 112(6): 1823-1845 (2023) - [j24]Ming-Kun Xie, Sheng-Jun Huang:
CCMN: A General Framework for Learning With Class-Conditional Multi-Label Noise. IEEE Trans. Pattern Anal. Mach. Intell. 45(1): 154-166 (2023) - [j23]Dong Liang, Jing-Wei Zhang, Ying-Peng Tang, Sheng-Jun Huang:
MUS-CDB: Mixed Uncertainty Sampling With Class Distribution Balancing for Active Annotation in Aerial Object Detection. IEEE Trans. Geosci. Remote. Sens. 61: 1-13 (2023) - [j22]Ying-Peng Tang, Xiu-Shen Wei, Borui Zhao, Sheng-Jun Huang:
QBox: Partial Transfer Learning With Active Querying for Object Detection. IEEE Trans. Neural Networks Learn. Syst. 34(6): 3058-3070 (2023) - [c43]Ye Li, Songcan Chen, Sheng-Jun Huang:
Implicit Stochastic Gradient Descent for Training Physics-Informed Neural Networks. AAAI 2023: 8692-8700 - [c42]Yuyan Zhou, Dong Liang, Songcan Chen, Sheng-Jun Huang, Shuo Yang, Chongyi Li:
Improving Lens Flare Removal with General-Purpose Pipeline and Multiple Light Sources Recovery. ICCV 2023: 12923-12933 - [c41]Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Multi-Label Knowledge Distillation. ICCV 2023: 17225-17234 - [c40]Ling Li, Dong Liang, Yuanhang Gao, Sheng-Jun Huang, Songcan Chen:
ALL-E: Aesthetics-guided Low-light Image Enhancement. IJCAI 2023: 1062-1070 - [c39]Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning. NeurIPS 2023 - [i24]Ye Li, Songcan Chen, Sheng-Jun Huang:
Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks. CoRR abs/2303.01767 (2023) - [i23]Ling Li, Dong Liang, Yuanhang Gao, Sheng-Jun Huang, Songcan Chen:
ALL-E: Aesthetics-guided Low-light Image Enhancement. CoRR abs/2304.14610 (2023) - [i22]Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning. CoRR abs/2305.02795 (2023) - [i21]Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Multi-Label Knowledge Distillation. CoRR abs/2308.06453 (2023) - [i20]Yuyan Zhou, Dong Liang, Songcan Chen, Sheng-Jun Huang, Shuo Yang, Chongyi Li:
Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery. CoRR abs/2308.16460 (2023) - 2022
- [j21]Shaoyuan Li, Ye Shi, Sheng-Jun Huang, Songcan Chen:
Improving deep label noise learning with dual active label correction. Mach. Learn. 111(3): 1103-1124 (2022) - [j20]Ming-Kun Xie, Sheng-Jun Huang:
Partial Multi-Label Learning With Noisy Label Identification. IEEE Trans. Pattern Anal. Mach. Intell. 44(7): 3676-3687 (2022) - [c38]Yiwen Pang, Ye Li, Sheng-Jun Huang:
A Tailored Physics-informed Neural Network Method for Solving Singularly Perturbed Differential Equations. ACAI 2022: 19:1-19:6 - [c37]Kun-Peng Ning, Xun Zhao, Yu Li, Sheng-Jun Huang:
Active Learning for Open-set Annotation. CVPR 2022: 41-49 - [c36]Ying-Peng Tang, Sheng-Jun Huang:
Active Learning for Multiple Target Models. NeurIPS 2022 - [c35]Lue Tao, Lei Feng, Hongxin Wei, Jinfeng Yi, Sheng-Jun Huang, Songcan Chen:
Can Adversarial Training Be Manipulated By Non-Robust Features? NeurIPS 2022 - [c34]Ming-Kun Xie, Jiahao Xiao, Sheng-Jun Huang:
Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels. NeurIPS 2022 - [i19]Kun-Peng Ning, Xun Zhao, Yu Li, Sheng-Jun Huang:
Active Learning for Open-set Annotation. CoRR abs/2201.06758 (2022) - [i18]Lue Tao, Lei Feng, Hongxin Wei, Jinfeng Yi, Sheng-Jun Huang, Songcan Chen:
Can Adversarial Training Be Manipulated By Non-Robust Features? CoRR abs/2201.13329 (2022) - [i17]Feng Sun, Ming-Kun Xie, Sheng-Jun Huang:
A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation. CoRR abs/2207.02410 (2022) - [i16]Bo-Shi Zou, Ming-Kun Xie, Sheng-Jun Huang:
Meta Objective Guided Disambiguation for Partial Label Learning. CoRR abs/2208.12459 (2022) - [i15]Chen-Chen Zong, Zheng-Tao Cao, Hong-Tao Guo, Yun Du, Ming-Kun Xie, Shao-Yuan Li, Sheng-Jun Huang:
Noise-Robust Bidirectional Learning with Dynamic Sample Reweighting. CoRR abs/2209.01334 (2022) - [i14]Dong Liang, Jing-Wei Zhang, Ying-Peng Tang, Sheng-Jun Huang:
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection. CoRR abs/2212.02804 (2022) - 2021
- [j19]Shaoyuan Li, Sheng-Jun Huang, Songcan Chen:
Crowdsourcing aggregation with deep Bayesian learning. Sci. China Inf. Sci. 64(3) (2021) - [j18]Min-Ling Zhang, Sheng-Jun Huang, Mingsheng Long:
Preface. J. Comput. Sci. Technol. 36(3): 588-589 (2021) - [j17]Rui Zhang, Qi Zhu, Xiangyu Xu, Daoqiang Zhang, Sheng-Jun Huang:
Visual-guided attentive attributes embedding for zero-shot learning. Neural Networks 143: 709-718 (2021) - [j16]Jia-Lue Chen, Jia-Jia Cai, Yuan Jiang, Sheng-Jun Huang:
PU Active Learning for Recommender Systems. Neural Process. Lett. 53(5): 3639-3652 (2021) - [j15]Chuanxing Geng, Sheng-Jun Huang, Songcan Chen:
Recent Advances in Open Set Recognition: A Survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(10): 3614-3631 (2021) - [j14]Xiuyi Jia, Zechao Li, Xiang Zheng, Weiwei Li, Sheng-Jun Huang:
Label Distribution Learning with Label Correlations on Local Samples. IEEE Trans. Knowl. Data Eng. 33(4): 1619-1631 (2021) - [c33]Kun-Peng Ning, Lue Tao, Songcan Chen, Sheng-Jun Huang:
Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries. AAAI 2021: 9161-9169 - [c32]Meng-Long Wei, Shao-Yuan Li, Sheng-Jun Huang:
Weakly Supervised Crowdsourcing Learning Based on Adversarial Consensus. CSCI 2021: 47-51 - [c31]Sheng-Jun Huang, Chen-Chen Zong, Kun-Peng Ning, Haibo Ye:
Asynchronous Active Learning with Distributed Label Querying. IJCAI 2021: 2570-2576 - [c30]Ying-Peng Tang, Sheng-Jun Huang:
Dual Active Learning for Both Model and Data Selection. IJCAI 2021: 3052-3058 - [c29]Ming-Kun Xie, Feng Sun, Sheng-Jun Huang:
Partial Multi-Label Learning with Meta Disambiguation. KDD 2021: 1904-1912 - [c28]Lue Tao, Lei Feng, Jinfeng Yi, Sheng-Jun Huang, Songcan Chen:
Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training. NeurIPS 2021: 16209-16225 - [c27]Ming-Kun Xie, Sheng-Jun Huang:
Multi-Label Learning with Pairwise Relevance Ordering. NeurIPS 2021: 23545-23556 - [i13]Lue Tao, Lei Feng, Jinfeng Yi, Sheng-Jun Huang, Songcan Chen:
Provable Defense Against Delusive Poisoning. CoRR abs/2102.04716 (2021) - [i12]Kun-Peng Ning, Hu Xu, Kun Zhu, Sheng-Jun Huang:
Co-Imitation Learning without Expert Demonstration. CoRR abs/2103.14823 (2021) - [i11]Kun-Peng Ning, Lue Tao, Songcan Chen, Sheng-Jun Huang:
Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries. CoRR abs/2103.14824 (2021) - [i10]Ming-Kun Xie, Sheng-Jun Huang:
CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise. CoRR abs/2105.07338 (2021) - [i9]Ye Shi, Shao-Yuan Li, Sheng-Jun Huang:
Learning from Crowds with Sparse and Imbalanced Annotations. CoRR abs/2107.05039 (2021) - 2020
- [j13]Qi Zhu, Rui Zhang, Sheng-Jun Huang, Zheng Zhang, Daoqiang Zhang:
LGSLRR: Towards fusing discriminative ordinal local and global structured low-rank representation for image recognition. Inf. Sci. 539: 522-535 (2020) - [j12]Sheng-Jun Huang, Guo-Xiang Li, Wen-Yu Huang, Shao-Yuan Li:
Incremental Multi-Label Learning with Active Queries. J. Comput. Sci. Technol. 35(2): 234-246 (2020) - [j11]Qi Zhu, Nuoya Xu, Sheng-Jun Huang, Jianjun Qian, Daoqiang Zhang:
Adaptive feature weighting for robust Lp-norm sparse representation with application to biometric image classification. Int. J. Mach. Learn. Cybern. 11(2): 463-474 (2020) - [j10]Qi Zhu, Xiangyu Xu, Ning Yuan, Zheng Zhang, Donghai Guan, Sheng-Jun Huang, Daoqiang Zhang:
Latent correlation embedded discriminative multi-modal data fusion. Signal Process. 171: 107466 (2020) - [j9]Wei Shao, Sheng-Jun Huang, Mingxia Liu, Daoqiang Zhang:
Querying Representative and Informative Super-Pixels for Filament Segmentation in Bioimages. IEEE ACM Trans. Comput. Biol. Bioinform. 17(4): 1394-1405 (2020) - [c26]Zhao-Yang Liu, Shaoyuan Li, Songcan Chen, Yao Hu, Sheng-Jun Huang:
Uncertainty Aware Graph Gaussian Process for Semi-Supervised Learning. AAAI 2020: 4957-4964 - [c25]Ming-Kun Xie, Sheng-Jun Huang:
Partial Multi-Label Learning with Noisy Label Identification. AAAI 2020: 6454-6461 - [c24]Yifan Yan, Sheng-Jun Huang, Shaoyi Chen, Meng Liao, Jin Xu:
Active Learning with Query Generation for Cost-Effective Text Classification. AAAI 2020: 6583-6590 - [c23]Guo-Xiang Li, Yao-Feng Tu, Sheng-Jun Huang:
Cross-Task and Cross-Model Active Learning with Meta Features. ICBDS 2020: 585-598 - [c22]Ming-Kun Xie, Sheng-Jun Huang:
Semi-Supervised Partial Multi-Label Learning. ICDM 2020: 691-700 - [c21]Tian-Zuo Wang, Xi-Zhu Wu, Sheng-Jun Huang, Zhi-Hua Zhou:
Cost-effectively Identifying Causal Effects When Only Response Variable is Observable. ICML 2020: 10060-10069 - [i8]Kun-Peng Ning, Sheng-Jun Huang:
Reinforcement Learning with Supervision from Noisy Demonstrations. CoRR abs/2006.07808 (2020)
2010 – 2019
- 2019
- [j8]Sheng-Jun Huang, Wei Gao, Zhi-Hua Zhou:
Fast Multi-Instance Multi-Label Learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(11): 2614-2627 (2019) - [c20]Zhao-Yang Liu, Sheng-Jun Huang:
Active Sampling for Open-Set Classification without Initial Annotation. AAAI 2019: 4416-4423 - [c19]Ying-Peng Tang, Sheng-Jun Huang:
Self-Paced Active Learning: Query the Right Thing at the Right Time. AAAI 2019: 5117-5124 - [c18]Jia-Jia Cai, Jun Tang, Qing-Guo Chen, Yao Hu, Xiaobo Wang, Sheng-Jun Huang:
Multi-View Active Learning for Video Recommendation. IJCAI 2019: 2053-2059 - [c17]Ming-Kun Xie, Sheng-Jun Huang:
Learning Class-Conditional GANs with Active Sampling. KDD 2019: 998-1006 - [c16]Tian-Zuo Wang, Sheng-Jun Huang, Zhi-Hua Zhou:
Towards Identifying Causal Relation Between Instances and Labels. SDM 2019: 289-297 - [e3]Qiang Yang, Zhi-Hua Zhou, Zhiguo Gong, Min-Ling Zhang, Sheng-Jun Huang:
Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part I. Lecture Notes in Computer Science 11439, Springer 2019, ISBN 978-3-030-16147-7 [contents] - [e2]Qiang Yang, Zhi-Hua Zhou, Zhiguo Gong, Min-Ling Zhang, Sheng-Jun Huang:
Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part II. Lecture Notes in Computer Science 11440, Springer 2019, ISBN 978-3-030-16144-6 [contents] - [e1]Qiang Yang, Zhi-Hua Zhou, Zhiguo Gong, Min-Ling Zhang, Sheng-Jun Huang:
Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part III. Lecture Notes in Computer Science 11441, Springer 2019, ISBN 978-3-030-16141-5 [contents] - [i7]Ying-Peng Tang, Guo-Xiang Li, Sheng-Jun Huang:
ALiPy: Active Learning in Python. CoRR abs/1901.03802 (2019) - 2018
- [j7]Nengneng Gao, Sheng-Jun Huang, Yifan Yan, Songcan Chen:
Cross modal similarity learning with active queries. Pattern Recognit. 75: 214-222 (2018) - [j6]Muhammad Yousefnezhad, Sheng-Jun Huang, Daoqiang Zhang:
WoCE: A framework for Clustering Ensemble by Exploiting the Wisdom of Crowds Theory. IEEE Trans. Cybern. 48(2): 486-499 (2018) - [j5]Feihu Huang, Songcan Chen, Sheng-Jun Huang:
Joint Estimation of Multiple Conditional Gaussian Graphical Models. IEEE Trans. Neural Networks Learn. Syst. 29(7): 3034-3046 (2018) - [c15]Chong Liu, Peng Zhao, Sheng-Jun Huang, Yuan Jiang, Zhi-Hua Zhou:
Dual Set Multi-Label Learning. AAAI 2018: 3635-3642 - [c14]Ming-Kun Xie, Sheng-Jun Huang:
Partial Multi-Label Learning. AAAI 2018: 4302-4309 - [c13]Yifan Yan, Sheng-Jun Huang:
Cost-Effective Active Learning for Hierarchical Multi-Label Classification. IJCAI 2018: 2962-2968 - [c12]Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen:
Active Feature Acquisition with Supervised Matrix Completion. KDD 2018: 1571-1579 - [c11]Sheng-Jun Huang, Jia-Wei Zhao, Zhao-Yang Liu:
Cost-Effective Training of Deep CNNs with Active Model Adaptation. KDD 2018: 1580-1588 - [i6]Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen:
Active Feature Acquisition with Supervised Matrix Completion. CoRR abs/1802.05380 (2018) - [i5]Sheng-Jun Huang, Jia-Wei Zhao, Zhao-Yang Liu:
Cost-Effective Training of Deep CNNs with Active Model Adaptation. CoRR abs/1802.05394 (2018) - [i4]Chuanxing Geng, Sheng-Jun Huang, Songcan Chen:
Recent Advances in Open Set Recognition: A Survey. CoRR abs/1811.08581 (2018) - 2017
- [c10]Sheng-Jun Huang, Jia-Lve Chen, Xin Mu, Zhi-Hua Zhou:
Cost-Effective Active Learning from Diverse Labelers. IJCAI 2017: 1879-1885 - [c9]Sheng-Jun Huang, Nengneng Gao, Songcan Chen:
Multi-instance multi-label active learning. IJCAI 2017: 1886-1892 - [c8]Yi Ding, Sheng-Jun Huang, Chen Zu, Daoqiang Zhang:
Margin Distribution Logistic Machine. SDM 2017: 19-27 - 2016
- [j4]Nengneng Gao, Sheng-Jun Huang, Songcan Chen:
Multi-label active learning by model guided distribution matching. Frontiers Comput. Sci. 10(5): 845-855 (2016) - [c7]Sheng-Jun Huang, Songcan Chen:
Transfer Learning with Active Queries from Source Domain. IJCAI 2016: 1592-1598 - [i3]Muhammad Yousefnezhad, Sheng-Jun Huang, Daoqiang Zhang:
WoCE: a framework for clustering ensemble by exploiting the wisdom of Crowds theory. CoRR abs/1612.06598 (2016) - 2015
- [c6]Sheng-Jun Huang, Songcan Chen, Zhi-Hua Zhou:
Multi-Label Active Learning: Query Type Matters. IJCAI 2015: 946-952 - 2014
- [j3]Sheng-Jun Huang, Rong Jin, Zhi-Hua Zhou:
Active Learning by Querying Informative and Representative Examples. IEEE Trans. Pattern Anal. Mach. Intell. 36(10): 1936-1949 (2014) - [j2]Jian-Sheng Wu, Sheng-Jun Huang, Zhi-Hua Zhou:
Genome-Wide Protein Function Prediction through Multi-Instance Multi-Label Learning. IEEE ACM Trans. Comput. Biol. Bioinform. 11(5): 891-902 (2014) - [c5]Sheng-Jun Huang, Wei Gao, Zhi-Hua Zhou:
Fast Multi-Instance Multi-Label Learning. AAAI 2014: 1868-1874 - 2013
- [c4]Sheng-Jun Huang, Zhi-Hua Zhou:
Active Query Driven by Uncertainty and Diversity for Incremental Multi-label Learning. ICDM 2013: 1079-1084 - [i2]Sheng-Jun Huang, Zhi-Hua Zhou:
Fast Multi-Instance Multi-Label Learning. CoRR abs/1310.2049 (2013) - 2012
- [j1]Zhi-Hua Zhou, Min-Ling Zhang, Sheng-Jun Huang, Yufeng Li:
Multi-instance multi-label learning. Artif. Intell. 176(1): 2291-2320 (2012) - [c3]Sheng-Jun Huang, Zhi-Hua Zhou:
Multi-Label Learning by Exploiting Label Correlations Locally. AAAI 2012: 949-955 - [c2]Sheng-Jun Huang, Yang Yu, Zhi-Hua Zhou:
Multi-label hypothesis reuse. KDD 2012: 525-533 - 2010
- [c1]Sheng-Jun Huang, Rong Jin, Zhi-Hua Zhou:
Active Learning by Querying Informative and Representative Examples. NIPS 2010: 892-900
2000 – 2009
- 2008
- [i1]Zhi-Hua Zhou, Min-Ling Zhang, Sheng-Jun Huang, Yufeng Li:
MIML: A Framework for Learning with Ambiguous Objects. CoRR abs/0808.3231 (2008)
Coauthor Index
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