Bao-Gang Hu

ORCID

Publication Activity (10 Years)

Years Active: 1997-2022
Publications (10 Years): 43

Top Venues

Publications

Siwei Lyu, Yanbo Fan, Yiming Ying, Bao-Gang Hu
Average Top-k Aggregate Loss for Supervised Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44 (1) (2022)
Yifan Xu, Kekai Sheng, Weiming Dong, Baoyuan Wu, Changsheng Xu, Bao-Gang Hu
Towards Corruption-Agnostic Robust Domain Adaptation. ACM Trans. Multim. Comput. Commun. Appl. 18 (4) (2022)
Kekai Sheng, Weiming Dong, Haibin Huang, Menglei Chai, Yong Zhang, Chongyang Ma, Bao-Gang Hu
Learning to assess visual aesthetics of food images. Comput. Vis. Media 7 (1) (2021)
Huai-Yu Li, Weiming Dong, Bao-Gang Hu
Incremental Concept Learning via Online Generative Memory Recall. IEEE Trans. Neural Networks Learn. Syst. 32 (7) (2021)
Bao-Gang Hu, Wei-Ming Dong
A design of human-like robust AI machines in object identification. CoRR (2021)
Yifan Xu, Kekai Sheng, Weiming Dong, Baoyuan Wu, Changsheng Xu, Bao-Gang Hu
Towards Corruption-Agnostic Robust Domain Adaptation. CoRR (2021)
Kekai Sheng, Weiming Dong, Menglei Chai, Guohui Wang, Peng Zhou, Feiyue Huang, Bao-Gang Hu, Rongrong Ji, Chongyang Ma
Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning. AAAI (2020)
Yanbo Fan, Baoyuan Wu, Ran He, Bao-Gang Hu, Yong Zhang, Siwei Lyu
Groupwise Ranking Loss for Multi-Label Learning. IEEE Access 8 (2020)
Bao-Gang Hu, Han-Bing Qu
Generalized Constraints as A New Mathematical Problem in Artificial Intelligence: A Review and Perspective. CoRR (2020)
Huai-Yu Li, Weiming Dong, Xing Mei, Chongyang Ma, Feiyue Huang, Bao-Gang Hu
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning. CoRR (2019)
Kekai Sheng, Weiming Dong, Menglei Chai, Guohui Wang, Peng Zhou, Feiyue Huang, Bao-Gang Hu, Rongrong Ji, Chongyang Ma
Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning. CoRR (2019)
Huai-Yu Li, Weiming Dong, Xing Mei, Chongyang Ma, Feiyue Huang, Bao-Gang Hu
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning. ICML (2019)
Thomas Corpetti, Xing Gong, Meng-Zhen Kang, Bao-Gang Hu, Laurence Hubert-Moy
Time-consistent estimation of LAI by assimilation in GreenLab plant growth model. Comput. Geosci. 130 (2019)
Yong Zhang, Baoyuan Wu, Weiming Dong, Zhifeng Li, Wei Liu, Bao-Gang Hu, Qiang Ji
Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation. CVPR (2019)
Huai-Yu Li, Weiming Dong, Bao-Gang Hu
Incremental Concept Learning via Online Generative Memory Recall. CoRR (2019)
Bao-Gang Hu, Weiming Dong
"Ge Shu Zhi Zhi": Towards Deep Understanding about Worlds. CoRR (2019)
Yong Zhang, Yanbo Fan, Weiming Dong, Bao-Gang Hu, Qiang Ji
Semi-Supervised Deep Neural Network for Joint Intensity Estimation of Multiple Facial Action Units. IEEE Access 7 (2019)
Huai-Yu Li, Wei-Ming Dong, Bao-Gang Hu
Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks. J. Comput. Sci. Technol. 33 (3) (2018)
Zhi-Yong Ran, Wei Wang, Bao-Gang Hu
On connections between Rényi entropy Principal Component Analysis, kernel learning and graph embedding. Pattern Recognit. Lett. 112 (2018)
Yong Zhang, Rui Zhao, Weiming Dong, Bao-Gang Hu, Qiang Ji
Bilateral Ordinal Relevance Multi-Instance Regression for Facial Action Unit Intensity Estimation. CVPR (2018)
Yong Zhang, Weiming Dong, Bao-Gang Hu, Qiang Ji
Weakly-Supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation. CVPR (2018)
Yong Zhang, Weiming Dong, Bao-Gang Hu, Qiang Ji
Classifier Learning With Prior Probabilities for Facial Action Unit Recognition. CVPR (2018)
Kekai Sheng, Weiming Dong, Chongyang Ma, Xing Mei, Feiyue Huang, Bao-Gang Hu
Attention-based Multi-Patch Aggregation for Image Aesthetic Assessment. ACM Multimedia (2018)
Kekai Sheng, Weiming Dong, Haibin Huang, Chongyang Ma, Bao-Gang Hu
Gourmet photography dataset for aesthetic assessment of food images. SIGGRAPH Asia Technical Briefs (2018)
Xing-Rong Fan, Xiujuan Wang, Meng-Zhen Kang, Jing Hua, Shuangsheng Guo, Philippe de Reffye, Bao-Gang Hu
concentrations in a closed system of plants and humans by integrating mechanistic and empirical models. Comput. Electron. Agric. 148 (2018)
Guibiao Xu, Bao-Gang Hu, José C. Príncipe
Robust C-Loss Kernel Classifiers. IEEE Trans. Neural Networks Learn. Syst. 29 (3) (2018)
Yanbo Fan, Ran He, Jian Liang, Bao-Gang Hu
Self-Paced Learning: An Implicit Regularization Perspective. AAAI (2017)
Yanbo Fan, Siwei Lyu, Yiming Ying, Bao-Gang Hu
Learning with Average Top-k Loss. CoRR (2017)
Yong Zhang, Weiming Dong, Chongyang Ma, Xing Mei, Ke Li, Feiyue Huang, Bao-Gang Hu, Oliver Deussen
Data-Driven Synthesis of Cartoon Faces Using Different Styles. IEEE Trans. Image Process. 26 (1) (2017)
Baoyuan Wu, Bao-Gang Hu, Qiang Ji
A Coupled Hidden Markov Random Field model for simultaneous face clustering and tracking in videos. Pattern Recognit. 64 (2017)
Yingying Deng, Fan Tang, Weiming Dong, Hanxing Yao, Bao-Gang Hu
Style-oriented representative paintings selection. SIGGRAPH ASIA (Posters) (2017)
Guibiao Xu, Zheng Cao, Bao-Gang Hu, José C. Príncipe
Robust support vector machines based on the rescaled hinge loss function. Pattern Recognit. 63 (2017)
Yanbo Fan, Siwei Lyu, Yiming Ying, Bao-Gang Hu
Learning with Average Top-k Loss. NIPS (2017)
Kekai Sheng, Weiming Dong, Wei Li, Joseph Razik, Feiyue Huang, Bao-Gang Hu
Centroid-aware local discriminative metric learning in speaker verification. Pattern Recognit. 72 (2017)
Zhi-Yong Ran, Bao-Gang Hu
Parameter Identifiability in Statistical Machine Learning: A Review. Neural Comput. 29 (5) (2017)
Yanbo Fan, Jian Liang, Ran He, Bao-Gang Hu, Siwei Lyu
Robust Localized Multi-view Subspace Clustering. CoRR (2017)
Chun-Guo Li, Xing Mei, Bao-Gang Hu
Unsupervised ranking of multi-attribute objects based on principal curves. ICDE (2016)
Guibiao Xu, Bao-Gang Hu, José C. Príncipe
Robust bounded logistic regression in the class imbalance problem. IJCNN (2016)
Zhi-Yong Ran, Bao-Gang Hu
Reply to "Reply to 'Determining structural identifiability of parameter learning machines'". Neurocomputing 218 (2016)
Linlin Cao, Ran He, Bao-Gang Hu
Locally imposing function for Generalized Constraint Neural Networks - A study on equality constraints. IJCNN (2016)
Linlin Cao, Ran He, Bao-Gang Hu
Locally Imposing Function for Generalized Constraint Neural Networks - A Study on Equality Constraints. CoRR (2016)
Bao-Gang Hu, Hong-Jie Xing
An Optimization Approach of Deriving Bounds between Entropy and Error from Joint Distribution: Case Study for Binary Classifications. Entropy 18 (2) (2016)
Yanbo Fan, Ran He, Jian Liang, Bao-Gang Hu
Self-Paced Learning: an Implicit Regularization Perspective. CoRR (2016)
Kekai Sheng, Weiming Dong, Yan Kong, Xing Mei, Ji-Lin Li, Chengjie Wang, Feiyue Huang, Bao-Gang Hu
Evaluating the Quality of Face Alignment without Ground Truth. Comput. Graph. Forum 34 (7) (2015)
Bao-Gang Hu
Information Theory and its Relation to Machine Learning. CoRR (2015)
Baoyuan Wu, Siwei Lyu, Bao-Gang Hu, Qiang Ji
Multi-label learning with missing labels for image annotation and facial action unit recognition. Pattern Recognit. 48 (7) (2015)
Xing Mei, Weiming Dong, Bao-Gang Hu, Siwei Lyu
UniHIST: A unified framework for image restoration with marginal histogram constraints. CVPR (2015)
Xing Mei, Honggang Qi, Bao-Gang Hu, Siwei Lyu
Improving Image Restoration with Soft-Rounding. ICCV (2015)
Xing Mei, Honggang Qi, Bao-Gang Hu, Siwei Lyu
Improving Image Restoration with Soft-Rounding. CoRR (2015)
Linlin Cao, Bao-Gang Hu
Generalized constraint neural network regression model subject to equality function constraints. IJCNN (2015)
Chun-Guo Li, Xing Mei, Bao-Gang Hu
Unsupervised Ranking of Multi-Attribute Objects Based on Principal Curves. IEEE Trans. Knowl. Data Eng. 27 (12) (2015)
Zhi-Yong Ran, Bao-Gang Hu
An identifying function approach for determining parameter structure of statistical learning machines. Neurocomputing 162 (2015)
Yanbo Fan, Ran He, Bao-Gang Hu
Global and local consistent multi-view subspace clustering. ACPR (2015)
Wei Wang, Bao-Gang Hu, Zengfu Wang
Globality and locality incorporation in distance metric learning. Neurocomputing 129 (2014)
Bao-Gang Hu
What Are the Differences Between Bayesian Classifiers and Mutual-Information Classifiers? IEEE Trans. Neural Networks Learn. Syst. 25 (2) (2014)
Bao-Gang Hu, Weiming Dong
A study on cost behaviors of binary classification measures in class-imbalanced problems. CoRR (2014)
Zhi-Yong Ran, Bao-Gang Hu
An identifying function approach for determining structural identifiability of parameter learning machines. IJCNN (2014)
Chun-Guo Li, Xing Mei, Bao-Gang Hu
Two-Phase Attribute Ordering for Unsupervised Ranking of Multi-attribute Objects. ICDM Workshops (2014)
Zhi-Yong Ran, Bao-Gang Hu
Determining parameter identifiability from the optimization theory framework: A Kullback-Leibler divergence approach. Neurocomputing 142 (2014)
Zhi-Yong Ran, Bao-Gang Hu
Determining structural identifiability of parameter learning machines. Neurocomputing 127 (2014)
Baoyuan Wu, Zhilei Liu, Shangfei Wang, Bao-Gang Hu, Qiang Ji
Multi-label Learning with Missing Labels. ICPR (2014)
Xing Mei, Bao-Gang Hu, Siwei Lyu
Non-blind image restoration with symmetric generalized Pareto priors. ICIP (2014)
Xiaowan Zhang, Bao-Gang Hu
A New Strategy of Cost-Free Learning in the Class Imbalance Problem. IEEE Trans. Knowl. Data Eng. 26 (12) (2014)
Guibiao Xu, Bao-Gang Hu, José C. Príncipe
An asymmetric stagewise least square loss function for imbalanced classification. IJCNN (2014)
Chun-Guo Li, Xing Mei, Bao-Gang Hu
Unsupervised Ranking of Multi-Attribute Objects Based on Principal Curves. CoRR (2014)
Yong Zhang, Weiming Dong, Oliver Deussen, Feiyue Huang, Ke Li, Bao-Gang Hu
Data-driven face cartoon stylization. SIGGRAPH ASIA Technical Briefs (2014)
Ran He, Wei-Shi Zheng, Bao-Gang Hu, Xiangwei Kong
Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition. IEEE Trans. Neural Networks Learn. Syst. 24 (1) (2013)
Xiaowan Zhang, Bao-Gang Hu
A New Strategy of Cost-Free Learning in the Class Imbalance Problem. CoRR (2013)
Chun-Guo Li, Bao-Gang Hu
Robust principal curves based on maximum correntropy criterion. ICMLC (2013)
Wei Wang, Bao-Gang Hu, Zengfu Wang
Efficient and Scalable Information Geometry Metric Learning. ICDM (2013)
Guibiao Xu, Bao-Gang Hu
Cost-Free Learning for Support Vector Machines with a Reject Option. ICDM Workshops (2013)
Bao-Gang Hu, Hong-Jie Xing
A New Approach of Deriving Bounds between Entropy and Error from Joint Distribution: Case Study for Binary Classifications CoRR (2013)
Baoyuan Wu, Siwei Lyu, Bao-Gang Hu, Qiang Ji
Simultaneous Clustering and Tracklet Linking for Multi-face Tracking in Videos. ICCV (2013)
Baoyuan Wu, Yifan Zhang, Bao-Gang Hu, Qiang Ji
Constrained Clustering and Its Application to Face Clustering in Videos. CVPR (2013)
Bao-Gang Hu, Hong-Jie Xing
Analytical Bounds between Entropy and Error Probability in Binary Classifications CoRR (2012)
Wei Wang, Bao-Gang Hu, Zengfu Wang
Discriminating classes collapsing for Globality and Locality Preserving Projections. IJCNN (2012)
Ning Wang, Bao-Gang Hu
Real-Time Simulation of Aeolian Sand Movement and Sand Ripple Evolution: A Method Based on the Physics of Blown Sand. J. Comput. Sci. Technol. 27 (1) (2012)
Shuang-Hong Yang, Bao-Gang Hu
Discriminative Feature Selection by Nonparametric Bayes Error Minimization. IEEE Trans. Knowl. Data Eng. 24 (8) (2012)
Lin Wu, François-Xavier Le Dimet, Philippe de Reffye, Bao-Gang Hu, Paul-Henry Cournède, Meng-Zhen Kang
An optimal control methodology for plant growth - Case study of a water supply problem of sunflower. Math. Comput. Simul. 82 (5) (2012)
Xiaowan Zhang, Bao-Gang Hu
Learning in the Class Imbalance Problem When Costs are Unknown for Errors and Rejects. ICDM Workshops (2012)
Xiao-Tong Yuan, Bao-Gang Hu, Ran He
Agglomerative Mean-Shift Clustering. IEEE Trans. Knowl. Data Eng. 24 (2) (2012)
Bao-Gang Hu
What are the Differences between Bayesian Classifiers and Mutual-Information Classifiers? CoRR (2011)
Ning Wang, Bao-Gang Hu
IdiotPencil: An Interactive System for Generating Pencil Drawings from 3D Polygonal Models. CAD/Graphics (2011)
Baoyuan Wu, Bao-Gang Hu
Density and neighbor Adaptive Information Theoretic Clustering. IJCNN (2011)
Ran He, Bao-Gang Hu, Wei-Shi Zheng, Xiangwei Kong
Robust Principal Component Analysis Based on Maximum Correntropy Criterion. IEEE Trans. Image Process. 20 (6) (2011)
Bao-Gang Hu, Ran He, Xiaotong Yuan
Information-Theoretic Measures for Objective Evaluation of Classifications CoRR (2011)
Ran He, Wei-Shi Zheng, Bao-Gang Hu, Xiangwei Kong
Nonnegative sparse coding for discriminative semi-supervised learning. CVPR (2011)
Ran He, Wei-Shi Zheng, Bao-Gang Hu, Xiangwei Kong
A Regularized Correntropy Framework for Robust Pattern Recognition. Neural Comput. 23 (8) (2011)
Ya-Jun Qu, Bao-Gang Hu
Generalized Constraint Neural Network Regression Model Subject to Linear Priors. IEEE Trans. Neural Networks 22 (12) (2011)
Ran He, Wei-Shi Zheng, Bao-Gang Hu
Maximum Correntropy Criterion for Robust Face Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 33 (8) (2011)
Ran He, Bao-Gang Hu, Xiaotong Yuan, Wei-Shi Zheng
Principal component analysis based on non-parametric maximum entropy. Neurocomputing 73 (10-12) (2010)
Yajun Qu, Bo Dai, Bao-Gang Hu
Neural-network based regression model with prior from ranking information. IJCNN (2010)
Ran He, Bao-Gang Hu, Wei-Shi Zheng, Yanqing Guo
Two-Stage Sparse Representation for Robust Recognition on Large-Scale Database. AAAI (2010)
Bo Dai, Bao-Gang Hu, Gang Niu
Bayesian Maximum Margin Clustering. ICDM (2010)
Bo Dai, Bao-Gang Hu
Minimum Conditional Entropy Clustering: A Discriminative Framework for Clustering. ACML (2010)
Shuang-Hong Yang, Hongyuan Zha, Bao-Gang Hu
Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora. NIPS (2009)
Shuang-Hong Yang, Hongyuan Zha, Shaohua Kevin Zhou, Bao-Gang Hu
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction. ECML/PKDD (2) (2009)
Han-Bing Qu, Bao-Gang Hu
Variational learning for Generalized Associative Functional Networks in modeling dynamic process of plant growth. Ecol. Informatics 4 (3) (2009)
Hong-Jie Xing, Bao-Gang Hu
Two-Phase Construction of Multilayer Perceptrons Using Information Theory. IEEE Trans. Neural Networks 20 (4) (2009)
Xiaotong Yuan, Bao-Gang Hu
Robust feature extraction via information theoretic learning. ICML (2009)

Bao-Gang Hu.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Bao-Gang Hu