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个人简介

以及广州市机器人软件及复杂信息处理重点实验室主任、华南理工大学计算中心主任。谭明奎目前为华南理工大学软件学院“人工智能与机器学习”团队负责人,是广东省“珠江人才团队”的核心成员;现主持国家自然科学基金项目、广东省新一代人工智能重点研发项目等多个项目;主要从事机器学习和深度学习理论和算法研究;以第一作者或者通讯作者完成的相关成果发表于人工智能顶级国际会议如 NeurIPS、ICML、CVPR、KDD、ICCV、AAAI和人工智能权威期刊如JMLR、TNNLS、TIP等。 曾获得世界华人数学家联盟最佳论文奖(ICCM Best Paper Award)、第六届MICCAI workshop最佳论文奖、华南理工大学建校65周年校长基金“最具科研潜质”奖、2019年“TVP腾讯云最具价值专家”奖等。 学历 2002.09-2006.06 湖南大学 环境科学与工程学院 环境工程(学士) 2006.09-2009.06 湖南大学 电气与信息工程学院 控制科学与工程(硕士) 2010.01-2014.10 新加坡南洋理工大学 计算机学院 计算机科学(博士) 教学经历 主讲软件学院三门专业课程:1、本科生《机器学习(全英)》课程;2、本科生《人工智能前沿与软件工程》课程;3、研究生《深度学习(全英)》课程。具体课程信息如下: (1)机器学习(全英课程) 使用教材: Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David学时:48课时(32 教学 + 16 实验) (2)深度学习(全英课程) 使用教材:Deep Learning Tutorial, LISA LAB, University of Montreal 学时:32课时(24教学 + 4实验 + 4专题报告) (3)人工智能前沿与软件工程 学时:16课时(16教学) 工作经历 2009.07-2009.12 新加坡南洋理工大学 研究助理 2013.09-2014.05 新加坡南洋理工大学 副研究员 2014.06-2016.06 澳大利亚阿德莱德大学 高级副研究员 2016.09-至今 华南理工大学软件学院 教授 获奖情况 (1)论文“Towards Ultrahigh Dimensional Feature Selection for Big Data” 荣获ICCM (世界华人数学家联盟) 2019最佳论文奖 (2)论文“Guided M-Net for High-resolution Biomedical Image Segmentation with Weak Boundaries”荣获2019年MICCAI Workshop on Ophthalmic Medical Image Analysis最佳论文奖 (3)华南理工大学建校65周年校长基金“最具科研潜质”奖 (4)2019年“TVP腾讯云最具价值专家”奖 科研项目 (1)基于大规模张量解的超高维数据结构化表示与分析方法研究,国家自然科学基金项目,2017-2019,在研,主持 (2)高效可解释性神经网络模型及理论研究,广东省重点研发计划项目,2019-2022,在研,主持

研究领域

主要从事机器学习、机器视觉和深度学习的基础理论和应用研究,具体包括: 1、超高维数据分析:特征选择、大规模矩阵恢复、大规模优化 2、深度学习及应用:网络模型压缩、网络结构自动优化、可解释性和泛化性能分析 3、复杂结构数据分析:Low-level图像处理、医疗图像分析、视频内容理解、3D数据分析

近期论文

查看导师新发文章 (温馨提示:请注意重名现象,建议点开原文通过作者单位确认)

期刊论文 [1] Runhao Zeng, Chuang Gan, Peihao Chen, Wenbing Huang, Qingyao Wu, and Mingkui Tan*. Breaking Winner-takes-all: Iterative-winners-out Networks for Weakly Supervised Temporal Action Localization. TIP, 2019. [2] Yong Guo, Qi Chen, Jian Chen, Qingyao Wu, Qinfeng Shi, and Mingkui Tan*. Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis. TMM, 2019. [3] Fan Lyu, Qi Wu, Fuyuan Hu, Qingyao Wu, and Mingkui Tan*. Attend and Imagine: Multi-label Image Classification with Visual Attention and Recurrent Neural Networks. TMM, 2019. [4] Mingkui Tan, Zhibin Hu, Yuguang Yan, Jiezhang Cao, Dong Gong, and Qingyao Wu. Learning Sparse PCA with Stabilized ADMM Method on Stiefel Manifold. TKDE, 2019. [5] Peilin Zhao, Yifan Zhang, Min Wu, Steven CH Hoi, Mingkui Tan*, and Junzhou Huang. Adaptive Cost-sensitive Online Classification. TKDE, 2018. [6] Dong Gong, Mingkui Tan†, Qinfeng Shi, Anton van den Hengel, and Yanning Zhang. Mptv: Matching Pursuit-based Total Variation Minimization for Image Deconvolution. TIP, 2018. [7] Qingyao Wu, Mingkui Tan†, Xutao Li, Huaqing Min, and Ning Sun. Nmfe-sscc: Non-negative Matrix Factorization Ensemble for Semi-supervised Collective Classification. KBS, 2015. [8] Mingkui Tan, Ivor W Tsang, and Li Wang. Towards Ultrahigh Dimensional Feature Selection for Big Data. JMLR, 2014. [9] Mingkui Tan, Ivor W Tsang, and Li Wang. Matching Pursuit LASSO Part I: Sparse Recovery over Big Dictionary. TSP, 2014. [10] Mingkui Tan, Ivor W Tsang, and Li Wang. Matching Pursuit Lasso Part ii: Applications and Sparse Recovery over Batch Signals. TSP, 2014. [11] Mingkui Tan, Ivor W Tsang, and Li Wang. Minimax Sparse Logistic Regression for Very High-dimensional Feature Selection. TNNLS, 2013. 会议论文 [1] Deng Huang, Peihao Chen, Runhao Zeng, Qing Du, Mingkui Tan*, and Chuang Gan. Location-aware Graph Convolutional Networks for Video Question Answering. In AAAI, 2020. [2] Jiezhang Cao, Langyuan Mo, Yifan Zhang, Kui Jia, Chunhua Shen, and Mingkui Tan*. Multi-marginal wasserstein gan. In NeurIPS, 2019. [3] Yong Guo, Yin Zheng, Mingkui Tan*, Qi Chen, Jian Chen, Peilin Zhao, and Junzhou Huang. NAT: Neural Architecture Transformer for Accurate and Compact Architectures. In NeurIPS, 2019. [4] Runhao Zeng, Wenbing Huang, Mingkui Tan*, Yu Rong, Peilin Zhao, Junzhou Huang, and Chuang Gan. Graph Convolutional Networks for Temporal Action Localization. In ICCV, 2019. [5] Pengshuai Yin, Qingyao Wu, Yanwu Xu, Huaqing Min, Ming Yang, Yubing Zhang, and Mingkui Tan*. PM-Net: Pyramid Multi-label Network for Joint Optic Disc and Cup Segmentation. In MICCAI, 2019. [6] Yifan Zhang, Hanbo Chen, Ying Wei, Peilin Zhao, Jiezhang Cao, Xinjuan Fan, Xiaoying Lou, Hailing Liu, Jinlong Hou, Xiao Han, Jianhua Yao, Qingyao Wu, Mingkui Tan*, and Junzhou Huang. From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification. In MICCAI, 2019. [7] Shihao Zhang, Huazhu Fu, Yuguang Yan, Yubing Zhang, Qingyao Wu, Ming Yang, Mingkui Tan*, and Yanwu Xu. Attention Guided Network for Retinal Image Segmentation. In MCCAI, 2019. [8] Shihao Zhang, Yuguang Yan, Pengshuai Yin, Zhen Qiu, Wei Zhao, Guiping Cao, Wan Chen, Jin Yuan, Risa Higashita, Qingyao Wu, Mingkui Tan*, and Jiang Liu. Guided M-Net for High-Resolution Biomedical Image Segmentation with Weak Boundaries. In Workshop on OMIA, 2019. [9] Jingwen Wang, Yuguang Yan, Yanwu Xu, Wei Zhao, Huaqing Min, Mingkui Tan*, and Jiang Liu. Conditional Adversarial Transfer for Glaucoma Diagnosis. In EMBC, 2019. [10] Yuguang Yan, Mingkui Tan†, Yanwu Xu, Jiezhang Cao, Michael Ng, Huaqing Min, and Qingyao Wu. Oversampling for Imbalanced Data via Optimal Transport. In AAAI, 2019. [11] Yuguang Yan, Wen Li, Hanrui Wu, Huaqing Min, Mingkui Tan*, and Qingyao Wu. Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation. In IJCAI, 2018. [12] Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, and Mingkui Tan*. Adversarial Learning with Local Coordinate Coding. In ICML, 2018. [13] Chaorui Deng, Qi Wu, Qingyao Wu, Fuyuan Hu, Fan Lyu, and Mingkui Tan*. Visual Grounding via Accumulated Attention. In CVPR, 2018. [14] Zhuangwei Zhuang, Mingkui Tan†, Bohan Zhuang, Jing Liu, Yong Guo, Qingyao Wu, Junzhou Huang, and Jinhui Zhu. Discrimination-aware Channel Pruning for Deep Neural Networks. In NeurIPS, 2018. [15] Yifan Zhang, Peilin Zhao, Jiezhang Cao, Wenye Ma, Junzhou Huang, Qingyao Wu, and Mingkui Tan*. Online Adaptive Asymmetric Active Learning for Budgeted Imbalanced Data. In KDD, 2018. [16] Yong Guo, Qingyao Wu, Chaorui Deng, Jian Chen, and Mingkui Tan*. Double Forward Propagation for Memorized Batch Normalization. In AAAI, 2018. [17] Chao Han, Qingyao Wu, Michael K Ng, Jiezhang Cao, Mingkui Tan*, and Jian Chen. Tensor Based Relations Ranking for Multi-relational Collective Classification. In ICDM, 2017. [18] Jiezhang Cao, Qingyao Wu, Yuguang Yan, Li Wang, and Mingkui Tan*. On the Flatness of Loss Surface for Twolayered ReLU Networks. In ACML, 2017. [19] Dong Gong, Mingkui Tan†, Yanning Zhang, Anton van den Hengel, and Qinfeng Shi. Mpgl: An Efficient Matching Pursuit Method for Generalized Lasso. In AAAI, 2017. [20] Yuguang Yan, Qingyao Wu, Mingkui Tan*, and Huaqing Min. Online Heterogeneous Transfer Learning by Weighted Offline and Online Classifiers. In ECCV, 2016. [21] Mingkui Tan, Shijie Xiao, Junbin Gao, Dong Xu, Anton Van Den Hengel, and Qinfeng Shi. Proximal Riemannian Pursuit for Large-scale Trace-norm Minimization. In CVPR, 2016. [22] Wei Emma Zhang, Mingkui Tan*, Quan Z Sheng, Lina Yao, and Qingfeng Shi. Efficient Orthogonal Non-negative Matrix Factorization over Stiefel Manifold. In CIKM, 2016. [23] Mingkui Tan, Yan Yan, Li Wang, Anton Van Den Hengel, Ivor W Tsang, and Qinfeng Javen Shi. Learning Sparse Confidence-weighted Classifier on Very High Dimensional Data. In AAAI, 2016. [24] Yan Yan, Mingkui Tan*, Ivor Tsang, Yi Yang, Chengqi Zhang, and Qng Shi. Scalable Maximum Margin Matrix Factorization by Active Riemannian Subspace Search. In IJCAI, 2015. [25] Mingkui Tan, Qinfeng Shi, Anton van den Hengel, Chunhua Shen, Junbin Gao, Fuyuan Hu, and Zhen Zhang. Learning Graph Structure for Multi-label Image Classification via Clique Generation. In CVPR, 2015. [26] Mingkui Tan, Ivor W Tsang, Li Wang, Bart Vandereycken, and Sinno Jialin Pan. Riemannian Pursuit for Big Matrix Recovery. In ICML, 2014. [27] Mingkui Tan, Ivor W Tsang, Li Wang, and Xinming Zhang. Convex Matching Pursuit for Large-scale Sparse Coding and Subset Selection. In AAAI, 2012. [28] Mingkui Tan, Li Wang, and Ivor W Tsang. Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets. In ICML, 2010.

学术兼职

担任国际期刊审稿人: Journal of Machine Learning Research (JMLR), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Neural Networks and Learning Systems (TNNLS),IEEE Transactions on Signal Processing (TSP), IEEE Transactions on Image Processing (TIP), IEEE Transactions on Multimedia (TMM), IEEE Transactions on Vehicular Technology, IEEE Transactions on Big Data, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Signal Processing Letters, IEEE Transactions on Cybernetics, IEEE Transactions on Evolutionary Computation, Elsevier Int. J. of Electronics and Communications, Multimedia Tools and Applications, Future Generation Computer Systems, Tsinghua Science and Technology, Imaging Science Journal, Information Sciences, Sensing and Imaging. 担任国际会议审稿人: CVPR, NeurIPS, ICML, ICLR, ICCV, ECCV, AAAI, IJCAI, ICLR, ACMMM, MICCAI, ACML, AISTATS

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