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1995����2004�꣬����ε�������Ĵ�ѧ�������ѧ�빤��ѧϵ���з���������о����Ⱥ��θ��о�Ա(Research Associate)���о�Ա(Research Fellow)��2005��9����2006��8�����ձ�����ѧ�о���(RIKEN)�Կ�ѧ�о���Amari�о�����п�ѧ�о��������о���ѧ��(Research Scientist)��2011��9����2012��2����������˹����������ҽԺ�о���ϵͳҽѧ�����﹤��ϵ���з���������о������ο�ѧ��(Scientist)��

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Jinwen Ma received the Ph.D. degree in probability theory and statistics from Nankai University, Tianjin, China, in 1992. Then, he joined the Department or Institute of Mathematics of Shantou University, Guangdong Province, China, throwing himself into the study of neural networks and learning algorithms, and became a full professor in 1999. Since September 2001, he has joined the School of Mathematical Sciences, Peking University, where he is currently a full professor and a Ph. D. tutor in applied mathematics at the Department of Information and Computational Sciences of this school.

During 1995 and 2004, he visited  and cooperatively studied several times at the Department of Computer Science & Engineering, the Chinese University of Hong Kong as a Research Associate or Fellow. From September 2005 to August 2006, he was a Research Scientist at the Amari Research Unit, RIKEN Brain Science Institute, Japan. From September 2011 to February 2012, he also visited and cooperatively studied at the Department of System Medicine and Biological Engineering, Research Center of Methodist Hospital System, Houston, USA.

His main research interests include neural computation, machine learning, independent component analysis (ICA), computer vision, and bioinformatics. He is the author or coauthor of more than 200 academic papers among which more than 60 papers were indexed by the Science Citation Index (SCI)��Expended. In fact, these papers have been cited over 2500 times. He has served as the Principal or Major Investigator for eleven  national and three provincial or ministerial and two other scientific research grants as well as over 10 cross-sectional research projects. At present, he is the vice-director member of the Signal Processing Society  in  the Chinese Institute of Electronics (CIE) and a member on the editorial  board of  ��Signal Processing (in Chinese)��. Moreover, he is the director of the Education Informationization Special Committee of China Chapter of International Information Study Society.  He has served as a program committee member of several major international conferences such as ISNN, ICIC, ICONIP, ICSP.  He was a co-chair of the program committee of 1999 Chinese Conference on Neural Networks and Signal Processing and the chair of the organization committee of the Third International Conference of Intelligence Science (ICIS 2018). He was selected in the 2017 AI  Impact Scholars released by Ascemap and scholar.chinaso.com  and the World��s Top 2% Scientists 2020 (Career Scientific Impact)  released by Stanford University.

 

��Ҫ��������(Main Published Papers)

 

1.��˹���̻��ģ�͡����߾���ʹ������ھ�(Mixtures of Gaussian Processes, Curve Clustering  and Big Data Mining)

 

[1.01]   Tao Li, Di Wu and Jinwen Ma, Mixture of robust Gaussian processes and its hard-cut EM algorithm with variational bounding approximation, Neurocomputing, vol. 452, pp:224-238, 2021. [Download(pdf)]

[1.02]   Tao  Li, Xiao Luo and Jinwen Ma, Average mean functions based EM algorithm for mixtures of Gaussian processes, Proc. of the 28^th International Conference On Neural Information Processing (ICONIP), CCIS, vol.1516, pp:549-557,2021. [Download(pdf)]

[1.03]   Xiangyang Guo, Daqing Wu, Tao Hong and Jinwen Ma, NSF-based mixture of Gaussian processes and its variational EM algorithm, Proc. of the 28^th  International Conference On Neural Information Processing (ICONIP), CCIS, vol.1516, pp:498�C505, 2021. [Download(pdf)]

[1.04]   Xiaoyan Li, Tao Li and Jinwen Ma, The un nu-hardcut EM algorithm for non-central student-t mixtures of Gaussian processes, Proc. of the 15^th  IEEE International Conference on Signal Processing (ICSP), pp:289-294, 2020. [Download(pdf)]

[1.05]   Di Wu and Jinwen Ma, An effective EM algorithm for mixtures of Gaussian processes via the MCMC sampling and approximation, Neurocomputing, vol.331, pp: 366-374, 2019. [Download(pdf)]

[1.06]   Di  Wu and Jinwen Ma, A two-layer mixture model of Gaussian process functional regressions and its MCMC EM algorithm, IEEE Trans. on Neural Networks and Learning Systems, vol.29, no.10, pp:4894-4904, 2018. [Download(pdf)]

[1.07]   Longbo Zhao and Jinwen Ma, A dynamic model selection algorithm for mixtures of Gaussian processes, Proc. of the 13^th  IEEE International Conference on Signal Processing (ICSP), pp:1095-1099,2016.    [Download(pdf)]

[1.08]   Zhe Qiang, Jiahui Luo and Jinwen Ma, Curve clustering via the split learning of mixtures of Gaussian processes, Proc. of the 13^th  IEEE International Conference on Signal Processing (ICSP), pp:1089- 1094, 2016. [Download(pdf)]

[1.09]   Shuanglong Liu and Jinwen Ma, Stock price prediction through the mixture of Gaussian processes via the precise hard-cut EM algorithm, Proc. of the 12^th  International Conference on Intelligent  Computing  (ICIC), LNAI, vol. 9773, pp:282-293, 2016. [Download(pdf)]

[1.10]   Di Wu and Jinwen Ma, A DAEM algorithm for mixtures of Gaussian process functional regressions,  Proc. of the 12^th  International Conference on  Intelligent  Computing (ICIC), LNAI, vol. 9773. pp:294�C303, 2016.  [Download(pdf)]

[1.11]   Yatong Zhou, Ziyi  Chen  and Jinwen Ma, From Gaussian processes to the mixture of Gaussian processes: a survey, Signal Processing (in Chinese),  vol.32, no.8, pp:960-972,2016.  [Download(pdf)]

[1.12]   Longbo Zhao, Ziyi Chen and Jinwen Ma, An Effective Model Selection Criterion for Mixtures of Gaussian Processes, Proc. of the 12^th  International Symposium on Neural Networks (ISNN), LNCS, vol. 9377, pp: 345-354, 2015.  [Download(pdf)]

[1.13]   Zhe Qiang and Jinwen Ma, Automatic model selection of the mixtures of Gaussian processes for regression, Proc. of the 12th  International Symposium on Neural Networks (ISNN), LNCS, vol. 9377. pp: 335�C344,2015.  [Download(pdf)]

[1.14]   Ziyi Chen, Jinwen Ma, and Yatong Zhou, A precise hard-cut EM algorithm for mixtures of Gaussian processes, Proc. of the 10^th  International Conference on  Intelligent  Computing (ICIC), LNCS,  vol. 8589. pp. 68�C75, 2014.  [Download(pdf)]

[1.15]   Yan Yang and Jinwen Ma, An efficient EM approach to parameter learning of the mixture of Gaussian processes, Proc. of the 8^th  International Symposium on Neural Networks (ISNN), LNCS, vol. 6676.  pp. 165�C174, 2011.  [Download(pdf)]

 

2.���޻��ģ�͡��Զ�ģ��ѡ��;������(Finite Mixture Modeling, Automated Model Selection and Clustering Analysis)

 

[2.01]   Yunsheng Jiang, Chenglin Liu and Jinwen Ma, BYY harmony learning of t-mixtures with the application to image segmentation based on contourlet texture features, Neurocomputing, vol.18, pp:262-274, 2016.  [Download(pdf)]

[2.02]   Wenli Zheng, Zhijie Ren, Yifan Zhou and Jinwen Ma, BYY harmony learning of log-normal mixtures with automated model selection, Neurocomputing, vol. 151, pp:1015-1026,2015.  [Download(pdf)]

[2.03]   Jinwen Ma and Hongyan Wang, Dynamically regularized maximum likelihood learning of Gaussian mixtures, Proc. of the 12^th  IEEE International Conference on Signal Processing (ICSP), pp:1432-1437, 2014.  [Download(pdf)]

[2.04]   Hongyan Wang and Jinwen Ma, Dynamically regularized harmony learning of Gaussian mixtures, Proc. of 2014 IEEE International Conference on System, Man and Cybernetics (SMC), pp:1158-1164. [Download(pdf)]

[2.05]   Chonglun Fang, Wei Jin and Jinwen Ma, k'-Means algorithms for clustering analysis with frequency sensitive discrepancy metrics, Pattern Recognition Letters, vol.34,no.3, pp:580-586, 2013.  [Download(pdf)]

[2.06]   Hongyan Wang and Jinwen Ma,  Simultaneous model selection and feature selection via BYY harmony learning, Lecture Notes in Computer Science, vol.6676, pp: 47-56, 2011. [Download(pdf)]

[2.07]   Yanqiao Zhu and Jinwen Ma, A stage by stage pruning algorithms for detecting the number of clusters in a dataset, Lecture Notes in Computer Science, vol. 6215, pp: 222-229, 2010. [Download(pdf)]

[2.08]   Jinwen Ma, Jianfeng Liu and Zhijie Ren, Parameter estimation of Poisson mixture with automated model selection through BYY harmony learning, Pattern  Recognition, vol.42,  pp:2659-2670, 2009. [Download(pdf)]

[2.09]   Lin Wang and Jinwen Ma, A kurtosis and skewness based criterion for model selection on Gaussian mixture, Proc. of the 2^nd International Conference on Biomedical Engineering and Informatics (BMEI, 2009), 17-19 October 2009, Tianjin, China. [Download(pdf)]

[2.10]   Jinwen Ma and Xuefen He, A fast fixed-point BYY harmony learning algorithm on Gaussian mixture with automated model selection, Pattern Recognition Letters, vol.29, pp: 701-711, 2008. [Download(pdf)]

[2.11]   Lei Li and Jinwen Ma, A BYY scale-incremental EM algorithm for Gaussian mixture learning, Applied Mathematics and Computation, vol.205, pp: 832-840, 2008. [Download(pdf)]

[2.12]   Hengyu Wang, Lei Li and Jinwen Ma, The competitive EM algorithm for Gaussian mixtures with BYY harmony criterion, Lecture Notes in Computer Science, vol.5226, pp: 552-560, 2008. [Download(pdf)]

[2.13]   Lei Li and Jinwen Ma, A BYY split-and-merge EM algorithm for Gaussian mixture learning, Lecture Notes in Computer Science, vol.5263, pp: 600-609, 2008. [Download(pdf)]

[2.14]   Zhijie Ren, Jinwen Ma, BYY Harmony Learning on Weibull Mixture with Automated Model Selection, Lecture Notes in Computer Science, vol.5263, pp: 589-599, 2008. [Download(pdf)]

[2.15]   Hongyan Wang and Jinwen Ma, BYY harmony enforcing regularization for Gaussian mixture learning, Proc. of the 9^th International Conference on Signal Processing (ICSP), pp: 1664-1667. [Download(pdf)]

[2.16]   Jinwen Ma and Jianfeng Liu, The BYY annealing learning algorithm for Gaussian mixture with automated model selection, Pattern  Recognition, vol.40, pp:2029-2037, 2007. [Download(pdf)]

[2.17]   Kai Huang, Le Wang, and Jinwen Ma, Efficient training of RBF networks via the BYY automated model selection learning algorithms, , Lecture Notes in Computer Science, vol.4491, pp: 1183-1192, 2007. [Download(pdf)]

[2.18]   Jinwen Ma, Automated model selection (AMS) on finite mixtures: a theoretical analysis, Proc. of 2006 International Joint Conference on Neural Networks (IJCNN��06), pp: 8255-8261, 2006. [Download(pdf)]

[2.19]   Jinwen Ma and Le Wang, BYY harmony learning on finite mixture: adaptive gradient implementation and a floating RPCL mechanism, Neural Processing Letters,  vol.24, no.1, pp: 19-40, 2006. [Download(pdf)]

[2.20]   Jinwen Ma and Taijun Wang, A cost-function approach to rival penalized Competitive learning (RPCL), IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, vol.36, no.4, pp: 722-737, 2006. [Download(pdf)]

[2.21]   Jinwen Ma and Bin Cao, The Mahalanobis distance based rival penalized competitive learning algorithm, Lecture Notes in Computer Science, vol.3971, pp: 442-447, 2006. [Download(pdf)]

[2.22]   Jinwen Ma and Qicai He, A dynamic merge-or-split learning algorithm on Gaussian mixture for automated model selection,  Lecture Notes in Computer Science, vol.3578, pp: 203-210, 2005. [Download(pdf)]

[2.23]   Jinwen Ma, Bin Gao, Yang Wang, and Qiansheng Cheng,  Conjugate and natural gradient rules for BYY harmony learning on Gaussian mixture with automated model selection, International Journal of Pattern Recognition and Artificial Intelligence, vol.19, no.5, pp: 701-713, 2005. [Download(pdf)]

[2.24]   Jinwen Ma, Taijun Wang, and Lei Xu, A gradient BYY harmony learning rule on Gaussian mixture with automated model selection,  Neurocomputing,  vol.56, pp: 481-487, 2004. [Download(pdf)]

[2.25]   Jinwen Ma and Taijun Wang, Entropy penalized automated model selection on Gaussian mixture, International Journal of Pattern Recognition and Artificial Intelligence, vol.18, no.8, pp: 1501-1512, 2004. [Download(pdf)]

[2.26]  Hua-Jun Zeng, Qi-Cai He, Zheng Chen, Wei-Ying Ma, and  Jinwen Ma, Learning to cluster web search results, Proc. of the 27th International ACM Conference on Research and Development in Information Retrieval (SIGIR��04), Sheffield, UK, July 25-29, 2004, pp: 210-217. [Download(pdf)]

 

3.���ѧϰ�����ɶԿ������һ��ѧϰ����(Deep Learning, Generative Adversarial Network (GAN)  and General Learning Theory)

 

[3.01]   Wenpeng Hu, Ran Le, Bing Liu, Feng Ji, Jinwen Ma, Dongyan Zhao and Rui Yan, Predictive adversarial learning from positive and unlabeled data,  Proc. of the 35^th AAAI  Conference  on  Artificial  Intelligence, vol.35, pp:7806-7814, 2021. [Download(pdf)]

[3.02]   Wenpeng Hu, Qi Qin, Mengyu Wang, Jinwen Ma, Bing Liu, Continual learning by using information of each class holistically, Proc. of the 35th  AAAI Conference on Artificial Intelligence, vol.35,  pp:7797-7805, 2021. [Download(pdf)]

[3.03]   Zhengyang  Shen, Tiancheng Shen, Zhouchen Lin, Jinwen Ma, PDO-eS(2) CNNs: partial differential operator based equivariant spherical CNNs, Proc. of the 35th AAAI Conference on Artificial Intelligence, vol.35,  pp:9585-9593, 2021.  [Download(pdf)]

[3.04]   Xiao Luo, Daqing Wu, Zeyu Ma, Chong Chen, Minghua Deng and Jinwen Ma, Zhongming Jin, Jianqiang Huang and Xiansheng Hua, CIMON: Towards High-quality Hash Codes, Proc. of the 30^th  International Joint Conference on Artificial Intelligence (IJCAI), pp:902-908, 2021. [Download(pdf)]

[3.05]   Imran Iqbal, Muhammad Younus, Khuram Walayat, Mohib Ullah Kakar and Jinwen Ma, Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images, Computerized Medical Imaging and Graphics, vol.88, Article no.101843, 2021.  [Download(pdf)]

[3.06]   Tao Li and Jinwen Ma, T-SVD based non-convex tensor completion and robust principal component analysis, Proc. of  the 25^th International Conference on Pattern Recognition (ICPR), pp:6980-6987, 2021. [Download(pdf)]

[3.07]   Tao Hong, Yajun Zou and Jinwen Ma, STDA-inf: style transfer for data augmentation through in-data training and fusion inference, Proc. of  the 17^th  International Conference on Intelligent Computing (ICIC), LNCS, vol.12837, pp: 76-90, 2021. [Download(pdf)]

[3.08]   Imran Iqbal, Ghazala Shahzad, Nida Rafiq, Ghulam Mustafa and Jinwen Ma, Deep learning-based automated detection of human knee joint's synovial fluid from magnetic resonance images with transfer learning,  IET Image Processing, vol.14, no.10, pp: 1990-1998, 2020. [Download(pdf)]

[3.09]   Imran Iqbal, Ghulam Mustafa  and Jinwen Ma, Deep learning-based morphological classification of human sperm heads, Diagnostics, vol.10, no.5, Article no.325, 2020. [Download(pdf)]

[3.10]   Wenpeng Hu, Mengyu Wang, Qi Qin, Jinwen Ma, Bing Liu, HRN: a holistic approach to one class learning, Advances in Neural Information Processing Systems, vol.33 (NeurIPS 2020). [Download(pdf)]

[3.11]   Ya Wang, Dongliang He, Fu Li, Xiang Long, Zhichao Zhou, Jinwen Ma and Shilei Wen, Multi-label classification with label graph superimposing, Proc. of the 34th  AAAI Conference on Artificial Intelligence, vol.34, pp: 12265-12272, 2020. [Download(pdf)]

[3.12]   Ya Wang, Jinwen Ma, Xiangchen Li and Albert Zhong, Hierarchical multi-classification for sensor-based badminton activity recognition, Proc. of the 15^th  IEEE International Conference on Signal Processing (ICSP), pp: 371-375, 2020. [Download(pdf)]

[3.13]   Zhengyang Shen, Lingshen He, Zhouchen Lin, and Jinwen Ma, PDO-eConvs: partial differential operator based equivariant convolutions, Proc. of the the 37^th  International Conference on Machine Learning (ICML), pp: 8697-8706, 2020.  [Download(pdf)]

[3.14]   Bing Yu, Jingfeng Wu, Jinwen Ma and Zhanxing Zhu, Tangent-normal adversarial regularization for semi-supervised learning, Proc. of 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR  2019), pp:10668-10676. [Download(pdf)]

[3.15]   Zhanxing Zhu, Jingfeng Wu, Lei Wu and Jinwen Ma, The anisotropic noise in stochastic gradient descent: its behavior of escaping from sharp minima and regularization effects, Proc. of  the 36^th International Conference on Machine Learning (ICML), vol.97, 2019. [Download(pdf)]

[3.16]   Tao Li and Jinwen Ma, Swarm intelligence based ensemble learning of deep neural networks, Proc. of the 26^th  International Conference on Neural Information Processing (ICONIP),  CCIS, vol.1142, pp:256�C264, 2019.  [Download(pdf)]

[3.17]   Wenpeng Hu, Zhangming Chan, Bing Liu, Dongyan Zhao, Jinwen Ma and Rui Yan, GSN: a graph-structured network for multi-party dialogues, Proc. of the 28^th International Joint Conference on Artificial Intelligence (IJCAI) , pp:5010-5016,2019. [Download(pdf)]

[3.18]   Jie An, Jingfeng Wu  and Jinwen Ma, Automatic cloud segmentation based on fused fully convolutional networks, Proc. of  the 15^th International Conference on Intelligent Computing (ICIC), LNCS, vol.11643, pp: 520-528, 2019. [Download(pdf)]

[3.19]   Taihong Xiao, Jiapeng  Hong  and Jinwen Ma, ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes,  Proc. of the 15^th  European Conference on Computer Vision  (ECCV), LNCS vol. 11214, pp:172-187, 2018. [Download(pdf)]

[3.20]   Shuanglong Liu, Chao Zhang and Jinwen Ma, CNN-LSTM Neural Network Model for Quantitative Strategy Analysis in Stock Markets, Proc. of the 24^th  International Conference on Neural Information Processing (ICONIP), LNCS, vol.10635, pp:198-206,2017. [Download(pdf)]

[3.21]   Yunsheng Jiang and Jinwen Ma, Combination features and models for human Detection, Proc. of  2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp: 240-248. [Download(pdf)]

[3.22]   Mohammad Farhad Bulbul, Yunsheng Jiang and Jinwen Ma, DMMs-based multiple features fusion for human action recognition, International Journal of Multimedia Data Engineering and Management, vol.6, no. 4, Article No.2, 2015.  [Download(pdf)]

 

4.��Ȼ���Դ����Ͱ������(Natural Language Processing (NLP) and Document Analysis)

 

[4.01]   Yajun Zou and Jinwen Ma, Deep learning based semantic page segmentation of document images in Chinese and English, Proc. of  the 17^th  International Conference on Intelligent Computing (ICIC),LNCS, vol.12837, pp: 484�C498, 2021. [Download(pdf)]

[4.02]   Wenpeng Hu, Mengyu Wang, Bing Liu,Feng Ji, Jinwen Ma and Dongyan Zhao, Transformation of Dense and Sparse Text Representations, Proc. of the 28^th  International Conference on Computational Linguistics (COLING), pp:3257�C3267,2020. [Download(pdf)]

[4.03]   Wenpeng Hu, Ran Le, Bing Liu, Jinwen Ma, Dongyan Zhao and Rui Yan, Translation vs. dialogue: a comparative analysis of sequence-to-sequence modeling, Proc. of the 28^th  International Conference on Computational Linguistics (COLING), pp:4111-4122,2020. [Download(pdf)]

[4.04]   Yajun Zou and Jinwen Ma, A deep semantic segmentation model for image-based table structure recognition, the 15^th  IEEE International Conference on Signal Processing (ICSP), pp: 274�C280, 2020. [Download(pdf)]

[4.05]   Wenpeng Hu, Zhangming Chan, Bing Liu, Dongyan Zhao, Jinwen Ma and  Rui Yan, GSN: a graph-structured network for multi-party dialogues, Proc. of the 28^th International Joint Conference onArtificial Intelligence (IJCAI), pp:5010-5016, 2019.  [Download(pdf)]

[4.06]   Yixin Li, Yajun Zou and Jinwen Ma, DeepLayout: a semantic segmentation approach to page layout analysis, Proc. of the 14^th International Conference on Intelligent Computing (ICIC), LNAI, vol. 10956, pp: 266�C277, 2018. [Download(pdf)]

[4.07]   Daqing Wu and Jinwen Ma, Related text discovery through consecutive filtering and supervised learning, Proc. of the third International Conference  on Intelligence Science (ICIS), IFIP AICT, vol.539, pp: 211�C220, 2018. [Download(pdf)]

[4.08]   Yixin Li and Jinwen Ma, A unified deep neural network for scene text detection, Proc. of the 13^th  International Conference on Intelligent Computing (ICIC), LNCS, vol.10361, pp:101�C112, 2017. [Download(pdf)]

[4.09]   Wei Zhao and Jinwen Ma, End-to-end scene text recognition with character centroid prediction, Proc. of the 24^th  International Conference on Neural Information Processing (ICONIP), LNCS, vol.10636, pp: 291�C299, 2017 [Download(pdf)]

[4.10]   Yixin Li and Jinwen Ma, The developments and challenges of text detection algorithms, Signal Processing (in Chinese),  vol.33, no.4, pp: 558-571, 2017. [Download(pdf)]

 

5.ͼ�����⡢��������������(Image Understanding, Search and Texture Classification)

 

[5.01]   Xiaoqing Li, Jiansheng Yang and Jinwen Ma, Recent developments of content-based image retrieval (CBIR), Neurocomputing, vol.452, pp: 675-689, 2021. [Download(pdf)]

[5.02]   Xiaoqing Li, Jiansheng Yang and Jinwen Ma, Large scale category-structured image retrieval for object identification through supervised learning of CNN and SURF-based matching, IEEE Access, vol.8, pp: 57796-57809, 2000.  [Download(pdf)]

[5.03]   Yongsheng Dong, Dacheng Tao, Xuelong Li, Jinwen Ma and Jiexin Pu, Texture classification and retrieval using shearlets and linear regression, IEEE Trans. on Cybernetics, vol.45, no.3, pp:358-369, 2015.  [Download(pdf)]

[5.04]   Yongsheng Dong and Jinwen Ma, Feature extraction through contourlet subband clustering for texture classification, Nerocomputing, vol.116, pp: 157-164, 2013.  [Download(pdf)]

[5.05]   Yongsheng Dong and Jinwen Ma, Bayesian texture classification based on contourlet transform and BYY harmony learning of Poisson mixtures, IEEE Trans. on Image Processing, vol. 21, no.3, pp:909-918,  2012.  [Download(pdf)]

[5.06]   Chonglun Fang and Jinwen Ma, A fixed-point EM algorithm for straight line detection, Lecture Notes in Computer Science, vol.6676,pp:136-143,2011. [Download(pdf)]

[5.07]   Yongsheng Dong and Jinwen Ma, Contourlet-based texture classification with product Bernoulli distributions, Lecture Notes in Computer Science, vol.6676,pp:9-18,2011. [Download(pdf)]

[5.08]   Yongsheng Dong and Jinwen Ma,  Wavelet-based image texture classification using local energy histograms,  IEEE Signal Processing Letters, vol.18.no.4, pp: 247-250, 2011. [Download(pdf)]

[5.09]   Jinwen Ma and Lei Li, Automatic straight line detection through fixed-point BYY harmony learning, Lecture Notes in Computer Science, vol.5226, pp: 569-576, 2008. [Download(pdf)]

[5.10]   Gang Chen, Lei Li, Jinwen Ma, A gradient BYY harmony learning algorithm for straight line detection, Lecture Notes in Computer Science, vol.5263, pp: 618-626, 2008. [Download(pdf)]

[5.11]   Zhiwu Lu, Qiansheng Cheng, Jinwen Ma, A gradient BYY harmony learning algorithm on mixture of experts for curve detection, Lecture Notes in Computer Science, vol.3578, pp: 250-257, 2005. [Download(pdf)]

 

6. ������������(Independent Component Analysis)

 

[6.01]   Md Shamim Reza and Jinwen Ma, Quantile kurtosis in ICA and integrated feature extraction for classification, Proc. of the 13th International Conference on Intelligent Computing (ICIC), LNCS, vol.10361,  pp: 681-692, 2017. [Download(pdf)]

[6.02]   Fei Ge and Jinwen Ma, An efficient pairwise kurtosis optimization algorithm for independent component analysis, Communications in Computer and Information Science, vol.93, pp:94-101, 2010.   [Download(pdf)]

[6.03]   Fei Ge  and Jinwen Ma, Spurious solution of the maximum likelihood approach to ICA, IEEE Signal Processing Letters, vol.17.no.7, pp: 655-658, 2010. [Download(pdf)]

[6.04]   Fei Ge and Jinwen Ma, Analysis of the Kurtosis-sum objective function for ICA, Lecture Notes in Computer Science, vol.5263, pp: 579-588, 2008. [Download(pdf)]

[6.05]   Zhe Chen and Jinwen Ma, Contrast functions for non-circular and circular sources separation in complex-valued ICA , Proc. of 2006 IEEE International Joint Conference on Neural Networks (IJCNN��06), pp: 1192-1199, 2006. [Download(pdf)]

[6.06]   Jinwen Ma , Zhe Chen and Shun-ichi Amari, Analysis of feasible solutions of the ICA problem under the one-bit-matching condition, Lecture Notes in Computer Science, vol.3889, pp: 838-845, 2006. [Download(pdf)]

[6.07]   Jinwen Ma, Dengpan Gao, Fei Ge  and Shun-ichi Amari, A one-bit-matching learning algorithm for independent component analysis, Lecture Notes in Computer Science, vol.3889, pp: 173-180, 2006. [Download(pdf)]

[6.08]   Jinwen Ma, Fei Ge and Dengpan Gao, Two adaptive matching learning algorithms for independent component analysis, Lecture Notes in Artificial Intelligence, vol.3801, pp: 915-920, 2005. [Download(pdf)]

[6.09]   Dengpan Gao, Jinwen Ma and Qiansheng Cheng, An alternative switching criterion for independent component analysis (ICA), Neurocomputing, vol.68, pp: 267-272, 2005. [Download(pdf)]

[6.10]   Jinwen Ma, Zhiyong Liu and Lei Xu, A further result on the ICA one-bit-matching conjecture, Neural Computation, vol.17, no.2, pp: 331-334, 2005. [Download(pdf)]

 

7.������Ϣѧ(Bioinformatics)

 

[7.01]   Xu Chen, Yanqiao Zhu, Fuhai Li, Ze-Yi Zheng, Eric C. Chang, Jinwen Ma and Stephen T. C.  Wong, Accurate segmentation of touching cells in multi-channel microscopy images with geodesic distance based clustering, Neurocomputing, vol. 149, pp:39-47,2015. [Download(pdf)]

[7.02]   Chenglin  Liu, Jing Su, Fei Yang, Kun Wei, Jinwen Ma and Xiaobo Zhou,  Compound signature detection on LINCS L1000 big data, Molecular Biosystems, vol.11, no.3, pp:714-722, 2015. [Download(pdf)]

[7.03]   Lei Huang Fuhai Li, Jianting Sheng, Xiaofeng Xia, Jinwen Ma, Ming Zhan and Stephen T. C. Wong, DrugComboRanker: drug combination discovery based on target network analysis, Bioinformatics, vo.30, no.12, pp:228-236, 2014. [Download(pdf)]

[7.04]   Chenglin Liu, Jinwen Ma, Chungche(Jeff) Chang, Xiaobo Zhou, FusionQ: a novel approach for gene fusion detection and quantification from paired-end RNA-Seq, BMC Bioinformatics, vol.14, Article no.193, 2013. [Download(pdf)]

[7.05]   Fuhai  Li, Hua Tan, Jaykrishna Singh, Jian Yang, Xiaofeng Xia, Jiguang Bao, Jinwen Ma,  Ming Zhan, Stephen T. C. Wong, A 3D multiscale model of cancer stem cell in tumor development, BMC Systems Biology, vol.7, Special Issue 2, Article no.S12, 2013. [Download(pdf)]

[7.06]   Wei Wang and Jinwen Ma, Density based merging search of functional modules in protein-protein interaction (PPI) networks, Lecture Notes in Computer Science, vol. 6215, pp: 634-649, 2010.  [Download(pdf)]

[7.07]   Fuhai Li, Xiaobo Zhou, Jinwen Ma, and Stephen T. C. Wong,  Multiple nuclei tracking using integer programming for quantitative cancer cell cycle analysis, IEEE Transactions on Medical Imaging, vol.29, no.1, pp: 95-105, 2010. [Download(pdf)]

[7.08]   Wei Xiong, Zhibin Cai, and Jinwen Ma, A DSRPCL-SVM approach to informative gene analysis, Genomics, Proteomics & Bioinformatics, vol.6, no.2, pp: 83-90, 2008. [Download(pdf)]

[7.09]   Fuhai Li, Xiaobo Zhou, Jinmin Zhu, Wieming Xia, Jinwen Ma  and  Stephen T. C. Wong, Workflow and methods of high-content time-lapse analysis for quantifying intracellular calcium signals, Neuroinformatics, vol. 6, no.2, pp: 97-108, 2008. [Download(pdf)]

[7.10]   Fuhai Li, Xiaobo Zhou, Jinmin Zhu, Jinwen Ma, Xudong Huang and Stephen T. C. Wong, High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles, BMC Biotechnology , 2007, 7: 66. [Download(pdf)]

[7.11]  Fuhai Li, Xuezhang Zhou, Jinwen Ma and Stephen T. C. Wong, An automated feedback system with the hybrid model of scoring and classification for solving over-segmentation problems in RNAi high content screening, Journal of Microscopy, Vol.226, pt  2, pp: 121-132, 2007.  [Download(pdf)]

[7.12]   Liangliang Wang and Jinwen Ma, Informative gene set selection via distance sensitive rival penalized competitive learning and redundancy analysis, Lecture Notes in Computer Science, vol.4491,  pp: 1227-1236, 2007. [Download(pdf)]

[7.13]   Liangliang Wang and Jinwen Ma, A post-filtering gene selection algorithm based on redundancy and multi-gene analysis, International Journal of Information Technology, vol.11, no.8, pp: 36-44, 2005. [Download(pdf)]

[7.14]   Jinwen Ma, Minghua Deng, Application of DNA microarray data to medicine, Physics (in Chinese), vol.34, no.5, pp: 371-380, 2005. [Download(pdf)]

[7.15]   Jinwen Ma, Fuhai Li, and Jianfeng Liu, Non-parametric statistical tests for informative gene selection, Lecture Notes in Computer Science, vol.3498, pp: 697-702, 2005. [Download(pdf)]

[7.16]   Jun Luo and Jinwen Ma,  A multi-population X-2 test approach to informative gene selection, Lecture Notes in Computer Science, vol. 3578,   pp: 406-413, 2005. [Download(pdf)]

[7.17]   Fei Ge and Jinwen Ma,  An information criterion for informative gene selection , Lecture Notes in Computer Science, vol.3498, pp: 703-708, 2005. [Download(pdf)]

[7.18]   Lin Deng, Jinwen Ma, and Jian Pei, Rank sum method for related gene selection and its application to tumor diagnosis, Chinese Science Bulletin, vol.49, no.15, pp: 1652-1657, 2004. [Download(pdf)](Chinese Version)

[7.19]  Lin Deng, Jian Pei, Jinwen Ma, and Dik Lun Lee,  A rank sum test method for informative gene discovery,  Proc. of the Tenth ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD��04), Seattle, Washington, USA, August 22-25, 2004, pp: 410-419. [Download(pdf)]

 

8.EM�㷨�����Է���(Convergence Analysis of the EM Algorithms)

 

[8.01]   Yan Yang and Jinwen Ma, Asymptotic convergence properties of the EM algorithm for mixture of experts, Neural Computation, vol.23, pp: 2140-2168, 2011.  [Download(pdf)]

[8.02]   Yan Yang and Jinwen Ma, An efficient EM approach to parameter learning of the mixture of Gaussian processes,  Lecture Notes in Computer Science, vol. 6676, pp: 165-174, 2009. [Download(pdf)]

[8.03]   Yan Yang  and Jinwen Ma, A single loop EM algorithm for the mixture of experts architecture, Lecture Notes in Computer Science, vol. 5552, pp: 959-968, 2009. [Download(pdf)]

[8.04]   Jinwen Ma and  Shunqun Fu, On the correct convergence of the EM algorithm for Gaussian mixtures, Pattern Recognition, vol.38, no.12, pp: 2602-2611, 2005. [Download(pdf)]

[8.05]   Jinwen Ma and Lei Xu, Asymptotic convergence properties of the EM algorithm with respect to the overlap in the mixture, Neurocomputing, vol.68, pp: 105-129, 2005. [Download(pdf)]

[8.06]   Jinwen Ma, Lei Xu, and Michael I. Jordan, Asymptotic convergence rate of the EM algorithm for Gaussian mixtures, Neural Computation, vol.12, no.12, pp: 2881-2907, 2000. [Download(pdf)]

 

9.����Hopfield���硢��������ʱ������ѧϰ(Generalized Hopfield Network, Associative Memory and Spatio-temporal Sequence Learning)

 

[9.01]    Fuhai Li, Jinwen Ma, and Dezhi Huang,  MFCC and SVM  based recognition of Chinese vowels, Lecture Notes in Artificial Intelligence, vol.3802,  pp: 812-819, 2005. [Download(pdf)]

[9.02]    Jinwen Ma, The capacity of time-delay recurrent neural network for storing spatio-temporal sequences, Neurocomputing, vol.62, pp: 19-27, 2004. [Download(pdf)]

[9.03]    Jianwei Wu, Jinwen Ma, and Qiansheng Cheng, Further results on the asymptotic memory capacity of the generalized Hopfield network, Neural Processing Letters, vol.20, pp: 23-38, 2004. [Download(pdf)]

[9.04]    Jinwen Ma, A hybrid neural network of addressable and content-addressable memory, International Journal of Neural Systems, vol.13, no.3, pp: 205-213, 2003. [Download(pdf)]

[9.05]    Jinwen Ma and Dezhi Huang, A neural network filter for complex spatio-temporal patterns, Proc of the 2002 International Joint Conference on Neural Networks (IJCNN��02), Hawaii, USA, May 12- 172002, vol.1, pp: 1028-1033. [Download(pdf)]

[9.06]    Jinwen Ma, A neural network approach to real-time pattern recognition, International Journal of Pattern Recognition and Artificial Intelligence, vol.15, no.6, pp: 937-947, 2001. [Download(pdf)]

[9.07]    Jinwen Ma, The asymptotic memory capacity of the generalized Hopfield networks, Neural Networks, vol.12, no.9, pp: 1207-1212, 1999. [Download(pdf)]

[9.08]    Jinwen Ma, The object perceptron learning algorithm on generalised Hopfield networks for associative memory, Neural Computing & Applications, vol.8, no.1, pp: 25-32, 1999. [Download(pdf)]

[9.09]    Jinwen Ma, The stability of the generalized Hopfield networks in randomly asynchronous mode,   Neural Networks, vol.10, no.6, pp: 1109-1116, 1997. [Download(pdf)]

[9.10]    Jinwen Ma, Simplex memory neural networks, Neural Networks, vol.10, no.1, pp: 25-29, 1997. [Download(pdf)]