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快播东流影院 北京邮电大学教师个东说念主主页系统 李春光

发布日期:2024-10-08 20:32    点击次数:191


快播东流影院 北京邮电大学教师个东说念主主页系统 李春光

男,1979年11月降生,籍贯辽宁凌源。2002年7月毕业于吉林大学通讯工程学院,获工学学士学位;2007年12月毕业于北京邮电大学信息工程学院, 获工学博士学位,同庚留校执教快播东流影院,现为北京邮电大学东说念主工智能学院副素养、硕士生导师、博士生导师。2011年1月入选微软亚洲酌量院(MSRA)“铸星磋商”(StarTrack),2011年7月至2012年4月在MSRA视觉计较组(Visual Computing Group)访谒酌量;2012年12月至2013年11月底受国度留学基金委(CSC)后生主干教师出洋研修磋商资助,在好意思国约翰·霍普金斯大学(The Johns Hopkins University)生物医学工程系成像科学酌量中心视觉-动态-学习履行室访谒酌量; 2019年12月至2020年2月受北京邮电大学首批“双一流”树立指令专项资助,在好意思国约翰·霍普金斯大学(JHU)数据科学数学酌量所(Johns Hopkins Mathematical Institute for Data Science)访谒酌量。酌量所在为数据科学与机器学习,酌量趣味为高维数据建模、分析与学习相配在信号处理、花式识别、生物信息学以及精确医学中的应用,包括无监督防范力机制、寥落/低秩模子(子空间聚类与结构化矩阵填充)、多元时刻序列分析与基因抒发分析等。咫尺已主办完成国度当然科学基金神色2项,累计在智力域外洋学术会议和外洋学术期刊上发表磋商酌量论文60余篇, 完成译著1部, 谷歌学术援用3000余次, 与所领导的酌量生杨涛共同撰写的酌量论文荣获2019年IEEE 视觉通讯与图像处理大会(VCIP)最勤学生论文奖。现为CCF-CV专委会奉行委员/CSIG-MV专委会委员/CCF-AI专委会奉行委员,IEEE高档会员(Senior Member)/CCF/CSIG会员,曾担任2020年外洋花式识别大会(ICPR2020)边界主席(Area Chair)和2021年IEEE机器视觉与花式识别大会(CVPR)边界主席(Area Chair),担任40+外洋国内学术期刊和学术会议审稿东说念主。

最新动态(Latest News)

[4] 所领导的硕士生闻其帅的论文"Rethinking Decoders for Transformer-based Semantic Segmentation: Compression is All You Need"被NeurIPS2024采取![2024-09-26]

[3] 迎接数学基础塌实的有志于作念酌量的考研同学提早与我磋商![2024-9-25]

[2] 历时2年半完成的《基于低维模子的高维数据分析: 旨趣、计较和应用》一书已由机械工业出书社四色印装(精装平价)![官网]! [2024-8-31]

[1] 所领导的博士生薛超的酌量论文“A Max-Flow based Approach for Neural Architecture Search”被欧洲计较机视觉大会ECCV2022采取![2022-07-04]

造就与责任

[5] 2020年01月 至 当今 北京邮电大学 东说念主工智能学院 东说念主工智能与网罗搜索教研中心 副素养 \ 硕导 (2016.06) \ 博导 (2019.07)

[4] 2015年12月 至 2019年12月 北京邮电大学 信息与通讯工程学院 网罗搜索教研中心 副素养 \ 硕导 (2016.06) \ 博导 (2019.07)

[3] 2008年01月 至 2015年12月 北京邮电大学 信息与通讯工程学院 网罗搜索教研中心 讲师

[2] 2002年9月 至 2007年12月 北京邮电大学 信息工程学院 信号与信息处理专科 获工学博士学位

[1] 1998年9月 至 2002年07月 吉林大学 南湖校区(原长春邮电学院) 通讯工程专科 获学士学位

酌量阅历

[4] 2019年07月获北京邮电大学首批“双一流”树立指令专项资助,于2019年12月至2020年2月 在好意思国约翰·霍普金斯大学(Johns Hopkins University)数据科学数学酌量所(Mathematical Institute for Data Science)访谒酌量 (with Rene Vidal) 

[3] 2016年07月受NSFC-RS外洋趋奉调换神色资助,在英国伦敦玛丽女王大学(Queen Mary University of London)计较机视觉与多媒体(Computer Vision and Multimedia)组访谒酌量 (with Tao Xiang & Yi-Zhe Song)

[2] 2012年12月至2013年11月底受国度留学基金委(CSC)后生主干教师出洋研修磋商资助,在好意思国约翰·霍普金斯大学(Johns Hopkins University)生物医学工程系(BME)视觉-动态-学习履行室(Vision, Dynamics, Learning Lab)访谒酌量 (with Rene Vidal)  

[1] 2011年7月至2012年4月受微软亚洲酌量院(MSRA)"铸星磋商"(StarTrack)资助,在MSRA视觉计较(VC)酌量组访谒酌量 (with Zhouchen Lin & Yi Ma )

酌量所在: 数据科学与机器学习,酌量趣味为无监督/半监督/弱监督学习(额外是面向高维数据的建模与分析)相配在信号处理、花式识别、生物信息学以及精确医学中的应用,包括防范力模子、子空间聚类、多元时序数据分析和基因抒发谱分析等。(*迎接对上述酌量所在感趣味、真确宠爱科研、作念事雄厚讲求的同学随时与我磋商,可趋奉酌量或攻读学位)  频年来,主办完成国度当然科学基金神色2项,主办完成留学归国东说念主员科研初始神色1项,投入完成国度当然科学基金面上神色4项,参与完成国度当然科学基金委与英国皇家学会趋奉调换神色1项,主办完成北京邮电大学科研改进专项3项,主办完成外洋趋奉神色1项、横向寄托研发神色1项。在IEEE TPAMI/J.STSP/TSP/TIP/TCSVT/TITS/TSMC/TNNLS/JBHI, PR, Neurocom., NeurIPS, ICCV, CVPR, ECCV, BMVC, ACCV, IJPRAI, ICDAR, ACM Multimedia, ICPR, ICASSP, ICIP, ACPR, VCIP等外洋期刊和外洋会议上发表论文60余篇, 完成译著1部, 所领导的酌量生杨涛荣获2019年IEEE视觉通讯与图像处理大会(VCIP)最勤学生论文奖, 曾担任外洋花式识别大会(ICPR2020)边界主席(Area Chair)、IEEE花式识别与机器视觉大会(CVPR2021)边界主席。现为中国计较机协会(CCF)东说念主工智能与花式识别专委会(CCF-AI)奉行委员, 中国图像图形学会(CSIG)机器视觉专委会(CSIG-MV)委员, 外洋电子电气工程师协会(IEEE)高档会员(Senior Member), 中国计较机协会(CCF)和中国图像图形学会(CSIG)会员。  

 酌量神色

 [9] 学问增强的子空间聚类, 主办, 国度当然科学基金面上神色, 神色批准号: 61876022, 2019.01-2022.12. [已结题]

 [8] 北京大学机器感知与智能造就部要点履行室怒放课题系列, 课题编号:K-2019-03/K-2018-03/K-2017-04/K-2016-08, 2016.10-2019.12. [已结题]

 [7] 基于结构化低秩准则的缺值填充问题酌量, 主办, 造就部留学归国东说念主员科研初始神色, 神色批准号: 留48, 2014.09-2016.12. [已结题]

 [6] 基于激活力的复杂网罗建模相配应用, 参与(名次序3位), 国度当然科学基金面上神色, 神色批准号: 61273217, 2013.01-2016.12. [已结题]

 [5] 国度当然科学基金委与英国皇家学会趋奉调换神色, 参与, 神色批准号: 61511130081, 2015.04-2017.03. [已结题]

 [4] 高维花式分析与学习, 主办, 北京邮电大学后生科研改进磋商专项, 课题编号:2012R0108, 2012.01-2013.12. [已结题]

 [3] 基于视觉领路的图像不变特征索取, 参与(名次序2位), 国度当然科学基金面上神色, 神色批准号: 61175011, 2012.01-2015.12. [已结题]

 [2] 丛流形学习相配在物体识别中的应用, 主办, 国度当然科学基金委后生科学基金, 神色批准号:61005004, 2011.01-2011.12. [已结题]

 [1] 基于多种物体识别的标签生成本领神色(MORE), 主办, 企业趋奉神色(编号I068-2008), 2008.11 至 2009.01. [已结题]

代表性论文 (* password will prompt when pointing the download link with your mouse)

[12] Chen Zhao, Chun-Guang Li,  Wei He,  and Chong You, "Deep Self-expressive Learning", Proc. of Machine Learning Research, Vol.234, pp.228-247, Conference on Parsimony and Learning (CPAL), Jan.3-6, 2024, Hongkong, P.R. China. [pdf][code]

[11] Mingkun Li, Chun-Guang Li, and Jun Guo, “Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification ”, IEEE Trans. Image Processing, Vol.31, May 16, 2022, pp.3606-3617.[arXiv][DOI:10.1109/TIP.2022.3173163][code]

[10] Shangzhi Zhang, Chong You, Rene Vidal and Chun-Guang Li, “Learning a Self-Expressive Network for Subspace Clustering ”, In Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 19-25, 2021.[pdf][code][arXiv]

[9] Ying Chen, Chun-Guang Li, and Chong You, “Stochastic Sparse Subspace Clustering”, In Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp.4155-4164.[pdf][arXiv][ code ]

[8] Chong You, Chun-Guang Li, Daniel P. Robinson, and Rene Vidal, “Is an Affine Constraint Needed for Affine Subspace Clustering?”, In Proc. of IEEE International Conference on Computer Vision(ICCV) 2019, pp.9915-9924, Oct. 27-Nov. 2, 2019, Seoul, Korea. [pdf][longer version(with proofs)][code]

[7] Junjian Zhang, Chun-Guang Li, Chong You, Xianbiao Qi, Honggang Zhang, Jun Guo, Zhouchen Lin, “Self-Supervised Convolutional Subspace Clustering Network”, In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5473-5482, Jun. 16-20, 2019, Long Beach, California, USA. [pdf][code]

[6] Chun-Guang Li, Chong You, and René Vidal, “On Geometric Analysis of Affine Sparse Subspace Clustering”, IEEE Journal of Selected Topics in Signal Processing, Vol.12, Issue 6, pp.1520-1533, Dec. 2018. DOI: 10.1109/JSTSP.2018.2867446 [pdf]

[5] Chun-Guang Li, Chong You, and René Vidal, “Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework”, IEEE Transactions on Image Processing, Vol. 26, No. 6, pp.2988-3001, June 2017. DOI:10.1109/TIP.2017.2691557 [pdf][code][notes]

[4] Chun-Guang Li and Rene Vidal. “A Structured Sparse plus Structured Low-Rank Framework for Subspace Clustering and Completion”, IEEE Transactions on Signal Processing, Vol. 64, No. 24, pp.6557-6570, Dec.15, 2016. [pdf][code][data][notes] DOI: 10.1109/TSP.2016.2613070

[3] Chong You, Chun-Guang Li, Daniel Robinson, and Rene Vidal. “Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering”, In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 26-July 1, 2016, Lag Vegas, Nevada, US. [pdf][code](oral paper)

[2] Chun-Guang Li, Zhouchen Lin, Honggang Zhang, and Jun Guo, “Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning”, In Proc. of IEEE International Conference on Computer Vision (ICCV), Dec. 11-18, 2015, pp.2767-2775, Santiago, Chile. [pdf][spotlight][poster][code]

[1] Chun-Guang Li and René Vidal, “Structured Sparse Subspace Clustering: A Unified Optimization Framework”, In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.277-286, June 7-12, 2015, Boston, Massachusetts, US.[pdf][code][notes]

齐备论文列表 (*鼠标停留在贯串上,可教唆密码)

I. 外洋期刊: 

2024

[25] Chao Xue, Jiaxing Li, Xiaoxing Wang, Yibing Zhan, Junchi Yan and Chun-Guang Li, “On Neural Architecture Search and Hyperparameter Optimization: A Max-Flow based Approach”, Submitted to Neural Networks, Sept. 25, 2024. 

[24] Yuying Zhao, Mei Wang, Jiani Hu, Weihong Deng and Chun-Guang Li, “A Perturbed Match Filtering Approach for Face Image Quality Assessment”, Submitted to Pattern Recognition, July 6, 2024. [Major Revision] 

[23] Wei He, Shangzhi Zhang, Chun-Guang Li, Xianbiao Qi, Rong Xiao, and Jun Guo,“Neural Normalized Cut: A Differential and Generalizable Approach for Spectral Clustering”, Submitted to Pattern Recognition, Jan. 29, 2024. [Major Revision] 

2023

[22] Qian Li, Chao Xue, Mingming Li, Chun-Guang Li, Chao Ma, and Xiaokang Yang, “Neural Architecture Selection as a Nash Equilibrium with Batch Entanglement”, IEEE Trans. Neural Networks and Learning Systems, July 11, 2023. [pdf] 

2022

[21] Mingkun Li, Chun-Guang Li, and Jun Guo, “Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification ”, IEEE Trans. Image Processing, Vol.31, May 16, 2022, pp.3606-3617.[arXiv][DOI:10.1109/TIP.2022.3173163][code]

[20] Mingkun Li, He Sun, Chaoqun Lin, Chun-Guang Li, and Jun Guo, “The Devil in the Tail: Clustering Consolidation plus Cluster Adaptive Balancing Loss for Unsupervised Person Re-Identification”, Pattern Recognition, Vol.129, Sept. 2022, 108763. [arXiv][DOI:10.1016/j.patcog.2022.108763]

[19] Bingcong Li, Xin Tang, Xianbiao Qi, Yihao Chen, Chun-Guang Li, Rong Xiao, “Effective Multi-Hot Encoding and Classifier for Lightweight Scene Text Recognition with a Large Character Set”, IEEE Trans. on Circuits and Systems for Video Technology, Vol.32, No.8, pp.5374-5385, August 2022. [PDF]

[18] Yihao Chen, Xin Tang, Xianbiao Qi, Chun-Guang Li, and Rong Xiao, “Learning Graph Normalization for Graph Neural Networks”, Neurocomputing, Vol.493, Jan. 2022, 613-625. [arXiv][code][DOI]

[17] Chao Xue, Mengting Hu, Xueqi Huang, and Chun-Guang Li, “Automated Search Space and Search Strategy Selection for Neural AutoML”, Pattern Recognition, Vol.124, 108474, April 2022.[pdf]

2021

[16] Shuai Di, Qi Feng, Chun-Guang Li, Mei Zhang, Honggang Zhang, Chiu C. Tan, and Haibin Ling, “Rainy Night Scene Understanding with Near-Scene Semantic Adaptation”, IEEE Trans. on Intelligent Transportation Systems, Vol.22, No.3, pp.1594-1602, March 2021. [pdf][code] DOI: 10.1109/TITS.2020.2972912

2020

[15] Ruopei Guo, Chun-Guang Li, Yonghua Li, Jiaru Lin, and Jun Guo, “Density-Adaptive Kernel based Efficient Re-Ranking Approaches for Person Re-Identification”, Neurocomputing, Vol.411, Oct. 2020, pp.91-111.[pdf][DOI][code]

[14] Bo Xiao, Xiang-Yu Li, Chun-Guang Li, Qian-Fang Xu, “A Novel Pooling Block for Improving Lightweight Deep Neural Networks”, Pattern Recognition Letters, Vol.135, July 2020, pp.307-312.

[13] Ruopei Guo, Chaoqun Lin, Chun-Guang Li, and Jiaru Lin, “Deep Group-Shuffling Dual Random Walks with Label Smoothing for Person Re-Identification”, IEEE Access, Vol.8, Feb. 27, 2020, pp.40018-40028. [pdf][code] DOI: 10.1109/ACCESS.2020.2976849

2019

[12] Junjian Zhang, Chun-Guang Li, Tianming Du, Honggang Zhang, and Jun Guo, “Convolutional Subspace Clustering Network with Block Diagonal Prior”, IEEE Access, Vol. 8, Dec. 2019, pp. 5723-5732. [pdf][code] DOI: 10.1109/ACCESS.2019.2963279

2018

[11] Chun-Guang Li, Chong You, and René Vidal, “On Geometric Analysis of Affine Sparse Subspace Clustering”, IEEE Journal of Selected Topics in Signal Processing, Vol.12, Issue 6, pp.1520-1533, Dec. 2018. DOI: 10.1109/JSTSP.2018.2867446 [pdf]

[10] Jianlou Si, Honggang Zhang, Chun-Guang Li, and Jun Guo, “Spatial Pyramid-Based Statistical Features for Person Re-Identification: A Comprehensive Evaluation”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol.48, No.7, pp.1140-1154, July 2018. DOI: 10.1109/TSMC.2016.2645660 [pdf][code]

[9] Shuai Di, Honggang Zhang, Chun-Guang Li, Xue Mei, Danil Prokhorov, and Haibing Ling, “Cross-domain Traffic Scene Understanding: A Dense Correspondence based Transfer Learning Approach”, IEEE Transactions on Intelligent Transportation System, Vol.19, No. 3, pp.745-757, March, 2018. DOI: 10.1109/TITS.2017.2702012 [pdf]

2017

[8] Chun-Guang Li, Chong You, and René Vidal, “Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework”, IEEE Transactions on Image Processing, Vol. 26, No. 6, pp.2988-3001, June 2017. DOI:10.1109/TIP.2017.2691557 [pdf][code][notes]

[7] Xianbiao Qi, Guoying Zhao, Chun-Guang Li, Jun Guo, Matti Pietikainen, “HEp-2 Cell Classification via Combining Multi-resolution Co-occurrence Texture and Large Regional Shape Information”, IEEE Journal of Biomedical and Health Informatics (J-BHI), Vol.21, No.2, pp.429-440, 2017. DOI: 10.1109/JBHI.2015.2508938 [pdf ][code]

 

2016

[6] Chong You, Chun-Guang Li, Daniel Robinson, and Rene Vidal. “Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering”, Available on arXiv: 1605.02633, 2016. [pdf][code]

[5] Chun-Guang Li and Rene Vidal. “A Structured Sparse plus Structured Low-Rank Framework for Subspace Clustering and Completion”, IEEE Transactions on Signal Processing, Vol. 64, No. 24, pp.6557-6570, Dec. 15, 2016. [pdf][code][data][notes] DOI: 10.1109/TSP.2016.2613070

[4] Xianbiao Qi, Chun-Guang Li, Guoying Zhao, Xiaopeng Hong, Matti Pietikainen, “Dynamic texture and scene classification by transferring deep image features”, Neurocomputing, Vol.171, 2016, pp:1230-1241.[pdf]

 

2014

[3] Xianbiao Qi, Rong Xiao, Chun-Guang Li, Yu Qiao, Jun Guo, and Xiaoou Tang, “Pairwise Rotation Invariant Co-occurrence Local Binary Pattern”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, No. 11, Nov. 2014, pp.2199-2213. DOI: 10.1109/TPAMI.2014.2316826 [pdf][code][ESI高被引论文(Oct.2017)] DOI: 10.1109/TPAMI.2014.2316826

 

2013

[2] Chun-Guang Li, Zhouchen Lin, and Jun Guo, “Bases Sorting: Generalizing the Concept of Frequency for Over-complete Dictionaries”, Neurocomputing, Vol.115, Sept. 4, 2013, pp.192–200. [pdf][code][data]

 

2009

[1] Chun-Guang Li, Jun Guo, and Bo Xiao, “Intrinsic Dimensionality Estimation within Neighborhood Convex Hull”, International Journal of Pattern Recognition and Artificial Intelligence, Vol.23, No.1, Feb 2009, pp.31-44. [pdf]

 

II. 外洋会议:

2024

[49] who, who, Chun-Guang Li, and who, "Bla bla bla", submitted to ICLR 2025. 

[48] who, who, Chun-Guang Li, and who, "Bla bla bla", submitted to ICLR 2025. 

[47] who, who, who, who, and Chun-Guang Li, "Bla bla bla", submitted to ICLR 2025. 

[46] who, who, and Chun-Guang Li, "Bla bla bla", submitted to ICASSP 2025. 

[45] who, who, and Chun-Guang Li, "Bla bla bla", submitted to AAAI 2025. 

[44] who, who, who, and Chun-Guang Li, "Bla bla bla", submitted to AAAI 2025. 

[43] Qishuai Wen and Chun-Guang Li, "Rethinking Decoders for Transformer-based Semantic Segmentation: Compression is All You Need", Accepted by NeurIPS 2024. 

[42] Wei He, Zhiyuan Huang, Xianghan Meng, Xianbiao Qi, Rong Xiao, and Chun-Guang Li, "Graph Cut-guided Maximal Coding Rate Reduction for Learning Image Embedding and Clustering", Accepted by ACCV 2024. 

[41] Chen Zhao, Chun-Guang Li,  Wei He,  and Chong You, "Deep Self-expressive Learning", Proc. of Machine Learning Research, Vol.234, pp.228-247, Conference on Parsimony and Learning (CPAL), Jan.3-6, 2024, Hongkong, P.R. China. [pdf]

2023

[40] Mingkun Li, Shupeng Cheng, Peng Xu, Xiantian Zhu, Chun-Guang Li and Jun Guo, "Unsupervised Long-Term Person Re-Identification with Clothes Change", In Proc. of IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), Nov.3-5, 2023, Beijing, P.R. China. 

2022

[39] Chao Xue, Xiaoxing Wang, Junchi Yan, Chun-Guang Li, “A Max-Flow based Approach for Neural Architecture Search”, European Conference on Computer Vision (ECCV), Oct. 23-27, 2022, Tel Aviv, Israel. [pdf]

2021

[38] He Sun, Mingkun Li, and Chun-Guang Li, “Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification ”, The 6th Asian Conference on Pattern Recognition (ACPR), Nov. 9-12, 2021, Lecture Notes in Computer Science (LNCS), Vol.13189, pp.532-546.[arXiv][pdf]

[37] Shangzhi Zhang, Chong You, Rene Vidal and Chun-Guang Li, “Learning a Self-Expressive Network for Subspace Clustering ”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 19-25, 2021.[pdf][code][code][arXiv]

[36] Mingkun Li, Ruopei Guo, Chun-Guang Li, and Jun Guo, “Self-paced Bottom-up Clustering Network with Side Information for Person Re-Identification”, International Conference on Pattern Recognition (ICPR), Jan. 10-15, 2021, (Virtual) Milano, Italy.

2020

[35] Chaoqun Lin, Ruopei Guo, Mingkun Li, Xianbiao Qi, and Chun-Guang Li, “Learning Convolution Feature Aggregation via Edge Attention Convolution Network for Person Re-Identification”, IEEE International Conference on Visual Communication and Image Processing (VCIP), Dec.1-4, 2020, (Virtual) Macau, P.R. China.

[34] Ying Chen, Chun-Guang Li, and Chong You, “Stochastic Sparse Subspace Clustering”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp.4155-4164.[pdf][arXiv][code: Link-1(passwd:5jre), Link-2]

2019

[33] Tao Yang and Chun-Guang Li, “Local Convex Representation with Pruning for Manifold Clustering”, IEEE International Conference on Visual Communication and Image Processing (VCIP), December 1-4, 2019, Sydney, Australia.[pdf][code][The best student paper award][extended version] DOI:10.1109/VCIP47243.2019.8965757

[32] Chong You, Chun-Guang Li, Daniel P. Robinson, and Rene Vidal, “Is an Affine Constraint Needed for Affine Subspace Clustering?”, IEEE International Conference on Computer Vision (ICCV), 2019, pp.9915-9924, Oct. 27-Nov. 2, 2019, Seoul, Korea. [pdf][code][longer version(with proofs)]

[31] Xianbiao Qi, Yihao Chen, Rong Xiao, Chun-Guang Li, Qin Zou, and Shuguang Cui, “A Novel Joint Character Categorization and Localization Approach for Character-Level Scene Text Recognition”, accepted by International Conference on Document Analysis and Recognition (ICDAR)快播东流影院, Workshop on Machine Learning, Sept. 20-25, Sydney, Australia, 2019. [pdf]

[30] Junjian Zhang, Chun-Guang Li, Chong You, Xianbiao Qi, Honggang Zhang, Jun Guo, Zhouchen Lin, “Self-Supervised Convolutional Subspace Clustering Network”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5473-5482, Jun. 16-20, 2019, Long Beach, California, USA. [pdf][code]

2018

[29] Chun-Guang Li, Junjian Zhang, and Jun Guo, “Constrained Sparse Subspace Clustering with Side Information”, International Conference on Pattern Recognition (ICPR), Aug. 20-24, Beijing, 2018, pp.2093-2099.[pdf][slides][code] (oral rate: 10%)

[28] Ruopei Guo, Chun-Guang Li, Yonghua Li, and Jiaru Lin, “Density-Adaptive Kernel Ranking for Person Re-Identification”, International Conference on Pattern Recognition (ICPR), Aug. 20-24, Beijing, 2018, pp.982-987.[pdf][poster][code]

[27] Jianlou Si, Honggang Zhang, Chun-Guang Li, Jason Kuen, Xiangfei Kong, Alex Kot, and Gang Wang, “Dual Attention Matching Networks for Context-Aware Feature Sequence based Person Re-Identification”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 19-21, 2018, Salt Lake City, USA.[pdf]

2016

[26] Junjian Zhang, Chun-Guang Li, Honggang Zhang, and Jun Guo, “Low Rank and Structured Sparse Subspace Clustering”, In Proc. of IEEE International Conference on Visual Communication and Image Processing (VCIP), Nov. 27-30, 2016, Chengdu, China.[pdf]

[25] Chong You, Chun-Guang Li, Daniel Robinson, and Rene Vidal. “Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 26-July 1, 2016, Las Vegas, Nevada, US, pp.3928-3937. [pdf][code] (oral, rate: 3.9%)

 

2015

[24] Chun-Guang Li, Zhouchen Lin, Honggang Zhang, and Jun Guo, “Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning”, In Proc. of IEEE International Conference on Computer Vision (ICCV), Dec. 11-18, 2015, pp.2767-2775, Santiago, Chile. [pdf][spotlight][poster][code] (An extended version with theoretical investigation and extensive experimental evaluations will be available soon)

[23] Zhen Qin, Chun-Guang Li, Honggang Zhang, and Jun Guo, “Improving Tag Matrix Completion for Image Annotation and Retrieval”, In Proc. of IEEE International Conference on Visual Communication and Image Processing (VCIP), Dec. 13-16, 2015, Singapore.[pdf]

[22] Chun-Guang Li, Chong You, and René Vidal, “On Sufficient Conditions for Affine Sparse Subspace Clustering”, In Signal Processing with Adaptive Sparse Structured Representations (SPARS) Workshop, July 6-9, 2015, Cambridge, UK.[pdf](The extended version] has been accepted by IEEE Journal of Selected Topics in Signal Processing, July 2018)

[21] Jianlou Si, Honggang Zhang, and Chun-Guang Li, “Regularization in Metric Learning for Person Re-Identification”, In Proc. of IEEE Conference on Image Processing (ICIP), Sept. 27-30, 2015, Quebec, Canada.[pdf]

[20] Chun-Guang Li and René Vidal, “Structured Sparse Subspace Clustering: A Unified Optimization Framework”, In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.277-286, June 7-12, 2015, Boston, Massachusetts, US.[pdf][code][notes]

 

2014

[19] Jianlou Si, Honggang Zhang, and Chun-Guang Li, “Person Re-Identification via Region-of-Interest based Features”, In Proc. of IEEE Conference on Visual Communications and Image Processing (VCIP), Dec.7-10, 2014, Valletta, Malta.[pdf]

 

2013

[18] Xianbiao Qi, Yu Qiao, Chun-Guang Li, and Jun Guo, “Exploring Cross-Channel Texture Correlation for Color Texture Classification”, British Machine Vision Conference (BMVC), Sept 9-13, 2013, Bristol, UK. [pdf]

[17] Xianbiao Qi, Yu Qiao, Chun-Guang Li, and Jun Guo, “Multi-scale Joint Encoding of Local Binary Patterns for Texture and Material Classification”, British Machine Vision Conference (BMVC), Sept 9-13, 2013, Bristol, UK. [pdf]

[16] Xianbiao Qi, Yi Lu, Shifeng Chen, Chun-Guang Li, and Jun Guo, “Spatial Co-Occurrence of Local Intensity Order for Face Recognition”, ICME Workshop on Management Information Systems (MIS) in Multimedia Art, Education, Entertainment, and Culture (MIS-MEDIA), July 15-19, 2013, San Jose, USA. [pdf]

[15] Qiang Wang, Zhiyuan Guo, Gang Liu, Chun-Guang Li, Jun Guo, “Local alignment for query by humming”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada, May 2013.[pdf]

 

2012

[14] Chun-Guang Li, Xianbiao Qi, and Jun Guo, “Dimensionality Reduction by Low-Rank Embedding”, The 2012 Sino-foreign-interchange Workshop on Intelligence Science and Intelligent Data Engineering (ISciDE2012), LNCS 7751, pp.181-188, 2013. [pdf]

[13] Xianbiao Qi, Rong Xiao, Lei Zhang, Chun-Guang Li, and Jun Guo, “A Rapid Flower/Leaf Recognition System”, The 20th anniversary ACM Multimedia (ACM MM), 2012, Nara. [pdf]

[12] Chun-Guang Li, Xianbiao Qi, Jun Guo and Bo Xiao, “An Evaluation on Different Graphs for Semi-supervised Learning”, The 2011 Sino-foreign-interchange Workshop on Intelligence Science and Intelligent Data Engineering (ISciDE2011), LNCS 7202, pp. 58-65, 2012. [pdf]

 

2010

[11] Chun-Guang Li, Jun Guo and Hong-gang Zhang, “Local Sparse Representation based Classification”, The 20th International Conference on Pattern Recognition (ICPR), August 23-26, 2010, Istanbul, Turkey. [pdf][code]

 

2009

[10] Qianfang Xu, Chun-Guang Li, Bo Xiao, Jun Guo, “A Visualization Algorithm for Alarm Association Mining”, International Conference on Network Infrastructure and Digital Content, pp. 326-330, 2009.[pdf]

[9] Chun-Guang Li, Jun Guo, and Hong-gang Zhang, “Learning Bundle Manifold by Double Neighborhood Graphs”, The 9th Asian Conference on Computer Vision (ACCV), 2009, LNCS 5996, Part III, pp. 321-330. [pdf][code]

[8] Chuang Zhang, Ming Wu, Chun-Guang Li, Bo Xiao, Zhiqing Lin, “Resume Parser: Semi-structured Chinese Document Analysis”, CSIE (5), 2009, pp.12-16. [pdf]

[7] Hong-gang Zhang, Jun Guo, Guang Chen, and Chun-Guang Li, “HCL2000 — A Large-scale Handwritten Chinese Character Database for Handwritten Character Recognition”, ICDAR 2009, pp.286-290. [pdf][HCL2000下载央求表(Application Form for HCL2000) ][HCL2000 database ][HCL2000 I/O codes] *鼠标停留鄙人载贯串位置会自动教唆密码

 

2007

[6] Chun-Guang Li, Jun Guo, and Xiangfei Nie, “Intrinsic Dimensionality Estimation with Neighborhood Convex Hull”, Proceeding of the International Conference on Computational Intelligence and Security, 2007. [pdf]

[5] Chun-Guang Li, Jun Guo, and Hong-gang Zhang, “Pruning Neighborhood Graph for Geodesic Distance based Semi-Supervised Classification”, Proceeding of the International Conference on Computational Intelligence and Security 2007, pp.428-432. [pdf]

 

2006

[4] Xiangfei Nie, Jun Guo, Zhen Yang, Chun-Guang Li, Jian Wang, Weihong Deng, “EMD Based Face Gender Discrimination”, The Sixth World Congress on Intelligent Control and Automation (WCICA), Vol.1, pp. 4078-4081, 2006.[pdf]

[3] Chun-Guang Li, Jun Guo, and Xiangfei Nie, “Learning geodesic metric for out-of-sample extension of isometric embedding”, Proceeding of the International Conference on Computational Intelligence and Security 2006, Part I, 2006, pp.449-452. [pdf]

[2] Chun-Guang Li and Jun Guo, “Supervised Isomap with explicit mapping”, Proceeding of the First International Conference on Innovative Computing, Information and Control, Vol.3, 2006, pp.345-348. [pdf][code]

[1] Chun-Guang Li, Jun Guo, Guang Chen, Xiangfei Nie, and Zhen Yang, “A version of Isomap with explicit mapping”, Proceeding of the International Conference on Machine Learning and Cybernetics, Vol.6, 2006, pp.3201-3206. [pdf]

III. 国内期刊:

2008

[3] Bo Xiao, Qian-Fang Xu, Zhiqing Lin, Jun Guo and Chun-Guang Li, “Credible Association Rule and Its Mining Algorithm Based on Maximum Clique”, Journal of Software, Vol.19,No.10,2008,pp.2597-2610.[In Chinese][pdf]

 

2007

[2] Xiang-Fei Nie, Chun-Guang Li and Jun Guo, “Face recognition based on Gabor wavelet and locally linear embedding”, Computer Engineering and Applications, Vol.43, No. 18, 2007, pp.62-64.[In Chinese]

[1] Xiang-Fei Nie, Chun-Guang Li and Jun Guo, “Face Detection Based on Empirical Mode Decomposition and Matching Pursuit”, Computer Engineering, Vol.33, No.14, July, 2007, pp.30-33.[In Chinese]

IV. 其它:

2023

[3] 邱丽叡,李春光,“带自安妥数据插补和影响衰减的时序斟酌聚类算法”,中国科技论文在线,2024年5月10日.

[2] 范晓翰,黄致远,李春光,“自安妥栽植的正交匹配寻踪寥落子空间聚类”,中国科技论文在线,2023年5月19日.

[1] 周勇士,李春光,“自增强的深度子空间聚类”,中国科技论文在线, 2023年4月17日.

V. 博士学位论文:

[1] 李春光, “流形学习相配在花式识别中的应用 (Manifold Learning and its Applications in Pattern Recognition)”, 北京邮电大学博士学位论文, 2007年12月. [pdf]

VI. 课程教材等:

[1] 李春光, “机器学习与数据科学教材 (Lecture Notes in Machine Learning and Data Science)”, 正在准备中, 2008年-2017年-2021年-当今. [pdf]

VII. 译著:

[1] 李春光,袁晓军,高盛华 译,约翰·莱特,马毅[著],基于低维模子的高维数据分析:旨趣、计较和应用, 北京: 机械工业出书社,  2024年8月. [官网预售贯串] 

[英文原著: John Wright and Yi Ma, High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications, Cambridge University Press, April 2022.][下载贯串]

外语智力:

[2] 第2外语: 英语 [宇宙英语品级测验 5级*] (2011.06)     *: 最高档 (top level)

[1] 第1外语: 日语 [日语外洋水平测试 1级*] (2006.02)     *: 最高档 (top level)

讲讲课程

1. 闹翻数学(本科生) [2008 春季]

2. 数字信号处理(本科生) [2009 秋季]

3. 生物信息基础(本科生) [2012春] [2014秋-2018秋][2020-2023春][2024春季][每周二 上昼 1-2节 8:00-9:35 第1-16周@教二-201教室 ]

本课程定位于先容分子生物数据的计较机自动分析与处贤人力,侧重于先容生物信息学中的基本问题、数学模子及算法。通过本课程的学习,使学生掌捏分子生物数据分析与处理的基本智力,额外是处理序列与阵列数据的智力,培养学陌生析问题和惩办问题的智力,也有助于学生加深前续基础课程所往返的数学想法和表面的聚会和垄断,并为后续从事磋商边界学习、科学酌量和本领开导铺垫基础。讲课本体范围:生物学基础(DNA的结构和功能以及磋商想法),常用的生物分子数据库,序列比对算法相配应用,基因组结构与基因识别,隐马尔科夫模子,系统进化树分析,卵白质结构斟酌,基因抒发数据分析及应用等。 *其他保举阅读贵府

[0] 课程先容 [slides]

[1] 引子 [slides]

[2] 生物学基础回想 [slides]

[3] 数据库先容 [slides]

[4] 序列分析 [slides]

[5] 系统发育分析 [slides]

[6] 基因识别与基因组分析  [slides]

[7] 隐马尔科夫模子  [slides]

[8] 卵白质结构斟酌 [slides]

[9] 基因抒发谱分析 [slides]

[10] 课程小结与扫尾语 [slides]

* 旧课件下载贯串:[slides]

4. 东说念主工智能导论 (本科生 大一) [2020-2022年秋] [2023秋] 机器学习导论 3次课 [10月27-11月10日] 沙河校区 周五13:00-14:35 N513 

本课程定位于给学生提供关于东说念主工智能中的花式识别和机器学习的理性意识,对后续的基础课程学习和专科课程学习提供多少成心的淡薄,况兼揭示基础课程和专科课程中的学问点与东说念主工智能、花式识别和机器学习的内在关联, 进而结束"导趣味"和“导意识”的课程树立主见.  课件下载贯串如下: 

[1] 东说念主工智能导论 之 机器学习引论 I:花式识别引例 [slides]

[2] 东说念主工智能导论 之 机器学习引论 II: 机器学习想法先容 [slides]

[3] 东说念主工智能导论 之 机器学习引论 III: 机器学习算法初学 [slides]

5. 花式识别引论(酌量生) [2014 秋季]

[0]课程先容

[1]小序 ( slides )

[2]部分数学基础 ( slides )

[3]线性回首 ( slides A , slides B)

[4]线性模子for分类 ( slides )

[5]神经网罗 ( slides )

[6]核智力 ( slides )

[7]相沿向量机 ( slides )

[8]其它 (聚类\降维等) ( slides)

6. 神经计较(酌量生) [2008-2012 春季][2014-2015 春季][2016 春季][2017年改为“机器学习与数据科学”]: This course covers the most machine learning techniques. Topics can be divided into three categories: 1) the classical topics of neural networks (e.g., Perceptron, Multi-layer Perceptron, Regularized Networks, and Self-Organization Map, and Deep Networks); 2) a series of statistical learning theories (e.g., the classical error analysis via bias-variance decomposition, Vapnik’s statistical learning theory and VC-dimension, regularization theory); 3) the related learning machines/algorithms (e.g., bagging, boosting/AdaBoost, Mixture of Experts, Decision Tree, Support Vector Machine, Kernel Methods, and Kernel Machine, Regularization networks), and other active topics in recent machine learning community (e.g., Manifold Learning, Subspace Clustering, Compressed Sensing, Sparse Representation, Low-Rank model, Matrix Completion and Sensing).

7. 花式识别与机器学习【2021版信通专科新大纲把“花式识别与机器学习”变更为“机器学习”】[ 2018秋 ][ 2019秋(前1-10周 passwd:r242) ][2020年秋][ 2021年秋 (passwd: m4s2)][2022年秋][2023年秋][2024年秋 (课程编号: 3111100941): 每周五9:50-11:25, 教三237,迎接选课或旁听~~ 花式识别与机器学习(2)班 ]

讲课定位:面向对东说念主工智能、机器学习、花式识别、数据挖掘等边界感趣味的硕士酌量生和博士酌量生;强调基本想法、基本模子和数学推导,同期迎接数学基础较好的高年事本科生旁听。

讲课本体:部分数学基础(概率论、有磋商论和信息论) / 线性回首 / 线性分类 / 神经网罗 / 核智力 / 相沿向量机 / 概率图模子 / 聚类 / 羼杂模子 / 采样智力 / 降维等。讲课本体将尝试更新到最新版块的Textbook框架上。

参考教材:

[1] Christopher M. Bishop, "Pattern Recognition and Machine Learning",Springer 2006. [PDF][homepage] 

[2] Christopher M. Bishop and Hugh Bishop,  "Deep Learning: Foundations and Concepts",Springer 2024. [homepage] 

旧课程资源下载([Slides [下载]] [ Homeworks ][ Others])

[0] 课程先容 ( slides ) 

[1] 小序  ( slides ) 

[2] 部分数学基础  ( slides ) 

[3] 线性模子用于回首   ( slides  )

[4] 线性模子用于分类  (slides ) 

[5] 神经网罗   (slides  ) 

[6] 核智力  (slides  ) 

[7] 相沿向量机  (slides  )

[8] 概率图模子   (slides ) 

[9] 羼杂模子与EM算法   (slides  )  

[10] 访佛推理 / 采样智力 / 隐变量模子  (slides) 

[11] 课程扫尾语

8. 机器学习与数据科学 【2021版信通专科新大纲中把“机器学习与数据科学”变更为“数据科学”】 [2017-2021春]

[课程教学条款]:遮盖机器学习与数据科学边界的典型算法,从最基本的学习算法——如最周边、线性回首和感知器等——到相沿向量机、深度学习,同期涵盖流形学习、压缩感知等无监督学习方面的最新阐发。除了先容应用和算法以外,本课程强调与算法磋商的表面部分的先容。在表面方面,涵盖参数臆测、偏倚方差阐发、统计学习表面和正则化表面;在学习范式方面,涵盖有监督学习、无监督学习与半监督学习等;在课程本体树立上,力图联络基本想法、经典表面和酌量前沿。但愿通过本课程的学习,为今后有志于在花式识别、机器学习、数据科学等边界内从事磋商酌量的酌量生打下坚实基础。

专题 0: 课程先容/机器学习发展简史/典型问题例如 [课件下载]

专题 1: 基于实例的学习 [课件下载]

专题 2: 线性模子 [课件下载]

专题 3: 线性模子的膨胀 [课件下载 I II III]

专题 4: 学习流程的统计性质 [课件下载 I II]

专题 5: 相沿向量机与统计学习表面 [课件下载 I II]

专题 6: 正则化表面相配应用 [课件下载 I II]

专题 7: 无监督学习与半监督学习 [课件下载 I II III]

专题 8: 压缩感知与寥落暗意 [课件下载 ]

专题 9: 处理大限制数据的政策

* 往年课件[下载贯串] / 本课程教材: 《机器学习与数据科学教材》(2008-2013-Now)正在准备中…..* 其他保举贵府 ( Download 贯串2 ) [教唆: 鼠标滑过左侧下载贯串,密码自动泄露]

9. 数据科学 [2022-2023年春][2024年春 教3-235/ 每周五 上昼 3-5节 9:50-12:15 / 48 学时 / 3学分 / 课程微信群(待公布) / 腾讯会议贯串 ]

[课程教学条款]:本课程教导数据科学与机器学习边界的典型模子、算法及表面,额外是波及高维数据的建模、分析与处理的专科基础学问。本体涵盖基于实例的学习、线性模子相配膨胀、集成学习、子空间与流形学习、聚类分析、压缩感知(寥落暗意/矩阵补全及矩阵规复)等模子和算法,包括偏倚方差阐发、VC维统计学习表面、正则化表面、高维空间几何、低秩数据发现与规复、高维空间中低维结构检测等典型表面。本课程旨在为准备在数据科学、机器学习、花式识别、数据挖掘、大数据及物联网等智能信息处理等所在开展酌量的学术型酌量生(额外是博士生)提供较为系统和完善的专科表面基础。课程侧重于面向高维数据的建模、分析与处理的模子、算法和相应的表面恶果,在先容基本想法和表面的同期,力图联络前沿酌量阐发。

专题 0: 课程先容 引子 [下载]

专题 1: 基于实例的学习  [下载]

专题 2: 线性模子  [下载]

专题 3: 线性模子的膨胀   [下载]

专题 4: 学习流程的统计性质及应用  [下载]

专题 5: 统计学习表面及应用  [下载]

专题 6: 正则化表面及应用  [下载]

专题 7: 数据降维与可视化  [下载]

专题 8: 聚类分析与半监督学习  [下载]

专题 9: 压缩感知与寥落暗意  [下载]

专题 10: 处理大限制数据的政策

* 本课程教材: 《机器学习与数据科学教材》(2008-2013-2021-Now)正在准备中* 往年课件下载:  [课件下载]

领导与协助领导的学生 (迎接作念事雄厚讲求、基础塌实、对机器学习/数据科学/生物信息学/精确医学等磋商所在硬核学术酌量的趣味与宠爱诚挚的同学们提前与我磋商~~(非诚勿扰! Sept 28, 2021) 央求攻读免试硕士酌量生或博士酌量生的主要旁观因素: ①塌实的数学基础与庄重的编程智力; ②较好的英语基础和自学智力; ③千里着而孜孜以求的雄厚作念事格调; ④雅致的心态转化智力和对科学酌量的趣味与宠爱 *注: ①+②:酌量实习 / ①+②+③:攻读学硕 / ①+②+③+④:攻读博士)

1. 所领导和协助领导的硕士和博士酌量生

2024.09 – present: [硕] 罗云浩,闻其帅;[博] 黄致远

2023.10 – present: [博] 赵钰莹

2023.10 – 2024.06: [硕] 洪世勇 

2023.09 – 2024.06: [硕] 黄致远

2022.09 – present: [硕] 郭卓远,余德健; [博] 孟祥涵

2021.09 – 2024.06: [硕] 邱丽叡,赵晨

2021.09 – present: [硕] 童政钰; [博] 何为

2020.09 – present: [博] 薛超

2020.09 – 2023.06: [硕] 范晓翰,周勇士

2019.09 – 2022.06: [硕] 孙赫,张尚之

2018.09 – 2022.06: [博] 李鸣坤 (with 郭军 素养)

2018.09 – 2021.06: [硕] 谌瑛,林轶群

2017.09 – 2020.06: [硕] 杨涛

凌辱人妻温泉

2016.10 – 2017.06: [博] 狄帅 (with 张洪刚 副素养)

2015.11 – 2020.08: [博] 郭若沛 (with 林家儒 素养)

2014.10 – 2020.05: [博] 张军建 (with 郭军 素养)

2014.04 – 2020.05: [博] 秦臻 (with 郭军 素养)

2014.01 – 2018.06: [博] 四建楼 (with 张洪刚 副素养)

2009.09 – 2010.05: [硕] 顾芳 (with 郭军 素养)

2008.09 – 2014.12: [硕][博] 皆宪标 (with 郭军 素养)

2. 所领导的本科生

2024: 廖星,闻其帅,許璟昊,郑寒璐  

2023: 黄致远,汪子涵,徐成健,郑子楠,朱楠

2022: 白雨田,李宁,刘梓杉,唐江南,余德健

2021: 邱丽叡,赵晨

2020: 范晓翰,林泽宇,唐朝阳,王晨朔

2019: 邵嘉伟,前锋昊,孙赫,张尚之

2018: 谌瑛, 朱玥, 刘岑

2017: 罗毅超, 武瑞, 杨涛

2016: 周磊, 李青阳, 郝梓君, 鲁为

2015: 张立夫, 赵剑峰, 谭鑫睿

2014: 王筱斐, 王梓, 李津润, 李成, 冯嵩

2013*:  (未领导毕设: JHU访学)

2012*:  (未领导毕设: MSRA铸星磋商/JHU访学)

2011*:  (未领导毕设: MSRA铸星磋商)

2010: 成林, 杨志诚, 崔子腾, 李高亢, 葛晗, 马淑靖, 徐饶, 甘强科

2009: 陈亮, 卢厚祥

2008: 皆宪标, 李晖, 刘乐凯, 唐寿成

学术劳动与会员:

[7] 担任审稿东说念主(Reviewer): JMLR, NSR, Nature: Mach. Intell., IJCV, IEEE TPAMI / J-STSP / TSP / TIP / TNNLS / TKDE / TCYB / TSMC / TBME / TCBB / TCSVT / Access / SPL, ACM TKDD, PR*, Neural Networks, Neurocom.*, SP:Image Comm.*, Info. Sciences, PRL, DMKD, DATAK, AI Review, Soft Computing, IJBC, NCAA, BDIA, IJPRAI, ICML, NeurIPS, SPARS, ICCV, CVPR**, ICLR, AISTATS, ECCV, WACV, AAAI, IJCAI**, ACM MM, ICPR**, 《电子学报》, 《自动化学报》, 《计较机援助想象与图形学学报》, 《限度与有磋商》, 《北京理工大学学报》,《东南大学学报》,《华南理工大学学报》,《北京邮电大学学报》, PRCV, CCDM, ISciDE, IC-NIDC

* Outstanding Reviewer Status Achieved (2017) / ** Area Chair或SPC

[6] 担任边界主席(Area Chair): ICPR(2020) / CVPR(2021) / SPC for IJCAI(2021)

[5] 中国计较机学会(CCF)东说念主工智能专委会(CCFAI)专委会奉行委员(2019.08-present)

[4] 中国图像图形学会机器视觉(CSIG-MV)专委会奉行委员(2017.08-present)

[3] 中国计较机学管帐算机视觉(CCF-CV)专科委员会奉行委员(2016.09-present)

[2] IEEE高档会员(Senior Member) (2021.04-present)

[1] ACM会员 (2018.01-2022.12) /CCF会员 / CSIG会员

学术评释注解(Talks)/科普讲座/Tutorials:

[20] 学术评释注解:Deep Self-Expressive Learning, at 中国科学院数学与系统科学酌量院 系统科学酌量所,非线性代数与数据科学研讨会(Nonlinear Algebra and Data Science Seminar)系列讲座 , Sept 2024. [TBD]

[19] 学术评释注解:Self-Expressive Models for Clustering High-Dimensional Data, at 中国科学院数学与系统科学酌量院 应用数学酌量所, August 14, 2024.

[18] 学术评释注解:Self-Expressive Learning, at 东莞理工学院 计较机科学与本领学院,腾讯会议(ID: 996 979 1939),Dec. 22, 2023.

[17] 学术评释注解:Learning Complex Low Dimensional Structures in High Dimensional Data, at 西北民族大学图像智能分析与应用外洋学术研讨会, Sept 28, 2022.

[16] 学术评释注解:Self-Expressive Learning, at 开脱军总病院医学东说念主工智能中心, July 29, 2022.

[15] 学术评释注解:Learning Low Dimensional Structures in Data via Self-Expressive Models, at 北京理工大学, Jan. 5, 2022.

[14] 学术评释注解:Pursuing Low-Dimensional Structures in Data via Self-Expressiveness, at 深圳大学数学与统计学院, 腾讯会议(ID: 713 452 447), Oct. 27, 2021.

[13] 学术评释注解:Pursuing Low-Dimensional Structures from High Dimensional Data (跟踪高维数据中的低维结构), at 北京理工大学自动化学院,July 10, 2021.

[12] [微软亚洲酌量院(MSRA)创研论坛——CVPR2021论文共享会] “Learning a Self-Expressive Network for Subspace Clustering”, April 22, 2021.[视频回放]

[11] 学术评释注解:Pursuing Low-Dimensional Structures from High Dimensional Data, at 祥瑞财产险科技中心东说念主工智能部,Dec. 29, 2020.

[10] [微软亚洲酌量院(MSRA)创研论坛——CVPR2020论文共享会] “Stochastic Sparse Subspace Clustering”, May 14, 2020.[视频回放]

[9] ICCV2019中国预会议(pre ICCV2019), “Is an Affine Constraint Needed for Affine Subspace Clustering?”, at 北京大学秋林评释注解厅, Sept 19, 2019.

[8] 中国计较机学会东说念主工智能会议(CCFAI 2019) 聚类分析学术专题论坛——  “子空间聚类酌量阐发——算法、表面及应用”(Subspace Clustering: Recent Advances in Algorithms, Theories & Applications), at 徐州市博顿温德姆栈房, August 20, 2019.

[7] 九三学社海淀区”科创吧”后生学术沙龙系列行动 (首场) “子空间聚类的酌量阐发——算法、表面以及应用”(Subspace Clustering: Recent Advances in Algorithms, Theories & Applications), at 北京邮电大学 教一1层116会议室,12:00-13:30, April 24, 2019.

[6] [微软亚洲酌量院(MSRA)创研论坛——CVPR2019论文共享会] Self-Supervised Convolutional Subspace Clustering Network, at 清华大学罗姆楼3层评释注解厅,April 2, 2019.

[5] Tutorial: “Subspace Clustering: Recent Advances in Algorithms, Theories and Applications”, Joint with Chong You, Guangcan Liu, Risheng Liu at International Conference on Pattern Recognition (ICPR), August 20, 2018.[时刻: 14:00-17:00, 地点: 北京国度会议中心三层306A][download (passwd: yygd)]

[4] 学术评释注解:Structured Sparse Subspace Clustering and Some Extensions, at 天津大学, 世界智能大会—天津大学机器视觉与学习专题论坛, May 17, 2018.

[3] [CSIG-MV系列科普讲座] 智造翌日: 从中国制造2025谈东说念主工智能, at 东说念主大附中北京经济本领开导区履行学校,April 19, 2018.

[2] 学术评释注解:结构化子空间聚类相配膨胀 (带援助信息 / 半监督 / 缺失值), at 山东财经大学 计较机科学与本领学院, Dec. 16, 2017.

[1] 学术评释注解:Structured Sparse Subspace Clustering and Some Extensions, at SIST, ShanghaiTech(上海科技大学),Nov.27, 2017.

其它社会行动:

[1] 九三学社社员 (2009.12-present)/九三学社北邮委员会第三支社组织委员(2018.09-当今)

其它获奖/个东说念主贯串/业余行动:

[4] 阅读 / 乒乓球(右手横板双反快攻) / 音乐 / 照相

[3] 谷歌学术(Google Scholar) / ResearchGate / 领英LinedIn / QQ空间

[2] 校田径认识会(凌源一中/吉林大学南湖校区/北京邮电大学) 5000m/1500m冠军屡次

[1] 1998.09-2002.07: 吉林大学南湖校区(原 长春邮电学院)6次荣获一等奖学金

其它资源贯串:

[24] 颜宁的三点作念酌量总结 – [link]

[23] 沈向洋博士演讲系列: [You are how you read@GIX][三十年科研路我踩过的那些坑@X-talk]

[22] 施一公素养在2018年宇宙科学说念德和学风树立宣讲造就评释注解会上的演讲《作念憨厚的学问、作念合法的东说念主》 – [演讲稿全文][含演讲现场视频]

[21] 为纯科学而敕令 (A Plea for Pure Science) (by Henry A.Rowland) – [link][pdf][English Version][亨利·罗兰生平]

[20] 汉明的演讲:你和你的酌量 (You and your research, by Richard Hamming) – [link][pdf][Hamming’s Advice]

[19] 酌量生奈何教师我方 – [link][pdf]

[18] 奈何作念酌量 – [link]

[17] Modeling with High Dimensional Data: [subspace clustering] [Scalable Sparse Subspace Clustering] [Sparse and Low-Rank Model for Visual Analytics] [link]

[16] Manifold Learning Resource – [ISOMAP][LLE][LaplacianEigenmap][ManifoldCharting][HessianLLE][LTSA][SDE][Logmap][DiffusionMaps][spectralmethod][comparison][survey]

[15] Research Links: Machine Learning/Compressed Sensing /Action /Activity /Feature /Optimization/Subspace – [link]

[14] Machine Learning (Theory) – [link]

[13] Resource on Sensing and Analysis of High-dimensional Data – [link] [link]

[12] Compressed Sensing Resource – [link] [link]

[11] Preprint Papers – [link]

[10] Dodo’s Pattern Recognition Commune – [link]

[9] The Psychology of Luck – [link]

[8] Microsoft Academic Search – [link]

[7] List of Computer Science Conference – [link]

[6] Comments on SCI-Journals – [link]

[5] List of some journal impact factors – [link]

[4] Academic Lecture Videos – [link]

[3] Mathematics – Stack Exchange – [link]

[2] Ten Simple Rules for Mathematical Writing – [link]

[1] Writing & Revision [link][link]快播东流影院



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