ʿ ʿʦ
Jinwen Ma, Professor, Ph. D.
ѧѧѧѧԺϢѧϵ
Department of Information and Computational
Sciences, School of Mathematical Sciences, Peking
University
ͨŵַбѧѧѧѧԺϢѧϵ, 100871
Mail
Address: Department
of Information Science, School of Mathematical Sciences, Peking University, Beijing, 100871, China
Phone: 86-10-62760609, Fax: 86-10-62751801, Email: jwma[at]math[dot]pku[dot]edu[dot]cn
(Profile)
1992Ͽѧͳרҵҵѧʿѧλ뵽ͷѧѧϵѧоչģͺѧϰ㷨о1999Ӧѧרҵʸ20019¼뵽ѧѧѧѧԺΪϢѧϵڡʿʦ
19952004꣬θĴѧѧ빤ѧϵзоȺθоԱ(Research Associate)оԱ(Research Fellow)2005920068ձѧо(RIKEN)ԿѧоAmariопѧооѧ(Research Scientist)2011920122˹ҽԺоϵͳҽѧ﹤ϵзоοѧ(Scientist)
Ҫо硢ѧϰ(ICA)ӾϢѧĿǰѷѧ300ƪбSCI¼60ƪ4200ΡȺе˹ȻѧĿ8صзƻ4ʡѧоĿ3ͺо10ֵйѧʿźŴֻḱίԱϢоѧйֻϢרίΣMathematical ComputationࡢComputerized Medical Imaging and Graphics ࡢMathematics༭ͱίźŴ־ίѧְεISNNICICICONIPICSPȹѧijίԱίԱ1999йźŴѧijίԱϯ͵ܿѧʻ(ICIS 2018)֯ίԱϯѡAcemapѧͬ2017AIӰѧߺ˹̹ѧ2020ȫǰ2%ѧҡӰ
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 networks, machine learning, independent component analysis (ICA), computer vision, and bioinformatics. He is the author or coauthor of more than 300 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 4200 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 Fellow of Chinese Institute of Electronics (CIE), the Vice Director Member of the Signal Processing Society of CIE. He also serves as the Editor-in-Chief of Mathematical Computation, the Associate Editor of Computerized Medical Imaging and Graphics, the Guest Editor of Mathematics, 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 Worlds Top 2% Scientists 2020 (Career Scientific Impact) released by Stanford University.
Ҫ(Main Published Papers)
1.˹̻ģ͡߾ʹھ(Mixtures of Gaussian
Processes, Curve Clustering and Big
Data Mining)
[1.01] Xiangyang
Guo, Tao Hong and Jinwen Ma, Automatic model
selection algorithm based on BYY harmony learning for mixture of Gaussian
process functional regressions models. Proc. of the 19th
International Conference on Intelligent
Computing (ICIC), LNCS, vol.14089, pp:391-403,2023. [Download(pdf)]
[1.02] Chengxin
Gong and Jinwen Ma, Automated model selection of the
two-layer mixtures of Gaussian process functional regressions for curve
clustering and prediction, Mathematics, 11(12):
2592, 2023. [Download(pdf)]
[1.03] Tao Li and Jinwen
Ma, Hidden Markov mixture of Gaussian process functional regression: utilizing
multi-scale structure for time series forecasting, Mathematics, 11(5):
1259, 2023. [Download(pdf)]
[1.04] Tao Li and Jinwen
Ma, A variational hardcut EM algorithm for the
mixtures of Gaussian processes, Science China Information Sciences, 66(3):
139103, 2023. [Download(pdf)]
[1.05] Tao Li and Jinwen
Ma, Dirichlet process mixture of Gaussian process functional regressions and
its variational EM algorithm, Pattern Recognition, vol. 134: 109129,
2023. [Download(pdf)]
[1.06] Tao Li and Jinwen
Ma, Attention mechanism based mixture of Gaussian
processes, Pattern Recognition Letters, vol.161: 130-136, 2022. [Download(pdf)]
[1.07] Xiangyang
Guo, Daqing Wu and Jinwen Ma, Federated sparse
Gaussian processes, Proc. of the 18th International Conference on Intelligent Computing
(ICIC), LNAI, vol.13395, pp. 267C276,
2022. [Download(pdf)]
[1.08] 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)]
[1.09] Tao Li, Xiao Luo and Jinwen Ma, Average mean functions based EM algorithm for mixtures of Gaussian processes, Proc. of the 28th International Conference On Neural Information Processing (ICONIP), CCIS, vol.1516, pp:549-557,2021. [Download(pdf)]
[1.10] Xiangyang
Guo, Daqing Wu, Tao Hong and Jinwen Ma, NSF-based
mixture of Gaussian processes and its variational EM algorithm, Proc. of the 28th International
Conference On Neural Information Processing (ICONIP), CCIS, vol.1516, pp:498C505, 2021. [Download(pdf)]
[1.11] 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 15th IEEE International Conference on Signal
Processing (ICSP), pp:289-294, 2020.
[Download(pdf)]
[1.12] 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)]
[1.13] 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)]
[1.14] Longbo
Zhao and Jinwen Ma, A dynamic model selection
algorithm for mixtures of Gaussian processes, Proc. of the 13th IEEE International Conference on
Signal Processing (ICSP),
pp:1095-1099,2016. [Download(pdf)]
[1.15] Zhe Qiang, Jiahui Luo and Jinwen Ma, Curve clustering via the split learning of
mixtures of Gaussian processes, Proc. of
the 13th
IEEE International Conference on Signal Processing (ICSP), pp:1089- 1094, 2016.
[Download(pdf)]
[1.16] Shuanglong
Liu and Jinwen Ma, Stock price prediction through the
mixture of Gaussian processes via the precise hard-cut EM algorithm, Proc. of the 12th International Conference on
Intelligent Computing (ICIC), LNAI, vol. 9773, pp:282-293,
2016. [Download(pdf)]
[1.17] Di Wu and Jinwen
Ma, A DAEM algorithm for mixtures of Gaussian process functional regressions, Proc.
of the 12th
International Conference on
Intelligent Computing (ICIC), LNAI, vol. 9773. pp:294C303,
2016. [Download(pdf)]
[1.18] 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)]
[1.19] Longbo
Zhao, Ziyi Chen and Jinwen
Ma, An Effective Model Selection Criterion for Mixtures of Gaussian Processes,
Proc. of the 12th International Symposium on Neural
Networks (ISNN), LNCS, vol. 9377,
pp: 345-354,
2015. [Download(pdf)]
[1.20] 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: 335C344,2015. [Download(pdf)]
[1.21] Ziyi
Chen, Jinwen Ma, and Yatong
Zhou, A precise hard-cut EM algorithm for mixtures of Gaussian processes, Proc.
of the 10th International Conference on Intelligent Computing (ICIC), LNCS, vol.
8589. pp. 68C75, 2014. [Download(pdf)]
[1.22] Yan Yang and Jinwen
Ma, An efficient EM approach to parameter learning of the mixture of Gaussian
processes, Proc. of the 8th
International Symposium on Neural Networks (ISNN), LNCS, vol. 6676.
pp. 165C174, 2011. [Download(pdf)]
2.ģ͡Զģѡ;(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 12th 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 2nd
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 9th
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 (IJCNN06), 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 (SIGIR04), Sheffield, UK, July
25-29, 2004, pp: 210-217. [Download(pdf)]
3.ѧϰɶԿһѧϰ(Deep Learning, Generative Adversarial
Network (GAN) and General Learning
Theory)
[3.01] Jingyang Deng, Xingjian Li, Haoyi
Xiong, Xiaoguang Hu, and Jinwen
Ma, Geometry-guided conditional adaptation for surrogate models of large-scale
3D PDEs on arbitrary geometries, Proc. of the 33th International Joint Conference on
Artificial Intelligence (IJCAI), in
press, 2024. [Download(pdf)]
[3.02] MD. Azizur
Rahman and Jinwen Ma, Two-Stage probe-based search
optimization algorithm for the traveling salesman problems, Mathematics,
vol.12(9): 1340,2024. [Download(pdf)]
[3.03] Zeren Zhang, Ran Chen and Jinwen Ma, Improving seismic fault recognition with self-supervised pre-training: a study of 3D transformer-based with multi-scale decoding and fusion, Remote Sensing, vol.16(5): 922, 2024. [Download(pdf)]
[3.04] Zhijian Zhuo, Yifei Wang, Jinwen Ma and Yisen Wang, Towads a unified theoretical understanding of
non-contrastive learning via rank differential mechanism, ICLR 2023. [Download(pdf)]
[3.05] Jingyang Deng, Shuyi Zhang and Jinwen Ma, Self-attention-based deep convolution LSTM
framework for sensor-based badminton activity recognition, Sensors,
vol.23(20): 8373, 2023. [Download(pdf)]
[3.06] Tao Hong, Zeren Zhang and Jinwen Ma, PCSalmix: Gradient
saliency-based mix augmentation for point cloud classification, Proc. of 2023
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP
2023),2023. [Download(pdf)]
[3.07] Zhengyang Shen, Yibo Yang, Qi
She, Changhu Wang, Jinwen
Ma and Zhouchen Lin, Newton design: designing CNNs
with the family of Newtons methods, Science China Information Sciences, vol.
66, no.162101, 2023.
[Download(pdf)]
[3.08] 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 35th AAAI
Conference on Artificial Intelligence, vol.35, pp:7806-7814,
2021. [Download(pdf)]
[3.09] 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)]
[3.10] 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)]
[3.11] 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 30th International Joint Conference on Artificial Intelligence
(IJCAI), pp:902-908, 2021. [Download(pdf)]
[3.12] 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)]
[3.13] Tao Li and Jinwen Ma, T-SVD based non-convex tensor completion and
robust principal component analysis, Proc. of the 25th International
Conference on Pattern Recognition (ICPR),
pp:6980-6987, 2021. [Download(pdf)]
[3.14] Tao Hong, Yajun Zou and Jinwen Ma,
STDA-inf: style transfer for data augmentation through in-data training and
fusion inference, Proc. of the 17th International Conference on Intelligent
Computing (ICIC), LNCS, vol.12837,
pp: 76-90, 2021. [Download(pdf)]
[3.15] 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)]
[3.16] 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)]
[3.17] 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)]
[3.18] 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)]
[3.19] Ya Wang, Jinwen Ma, Xiangchen Li and Albert Zhong, Hierarchical multi-classification for sensor-based badminton activity recognition, Proc. of the 15th IEEE International Conference on Signal Processing (ICSP), pp: 371-375, 2020. [Download(pdf)]
[3.20] Zhengyang Shen, Lingshen He, Zhouchen Lin, and Jinwen Ma, PDO-eConvs: partial differential operator based equivariant
convolutions, Proc. of the the 37th International Conference on
Machine Learning (ICML), pp:
8697-8706, 2020. [Download(pdf)]
[3.21] 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)]
[3.22] 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 36th
International Conference on Machine Learning (ICML), vol.97, 2019. [Download(pdf)]
[3.23] Tao Li and Jinwen Ma, Swarm intelligence based ensemble learning of
deep neural networks, Proc. of the 26th International Conference on
Neural Information Processing (ICONIP), CCIS, vol.1142, pp:256C264, 2019. [Download(pdf)]
[3.24] 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 28th International Joint Conference on Artificial
Intelligence (IJCAI) ,
pp:5010-5016,2019.
[Download(pdf)]
[3.25] Jie An, Jingfeng Wu and Jinwen Ma, Automatic cloud segmentation based on fused
fully convolutional networks, Proc. of
the 15th International Conference on Intelligent Computing (ICIC), LNCS, vol.11643, pp: 520-528,
2019. [Download(pdf)]
[3.26] Taihong Xiao, Jiapeng Hong and Jinwen Ma,
ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face
Attributes, Proc. of the 15th European Conference on Computer Vision (ECCV), LNCS vol. 11214, pp:172-187,
2018. [Download(pdf)]
[3.27] Shuanglong Liu, Chao Zhang and Jinwen
Ma, CNN-LSTM Neural Network Model for Quantitative Strategy Analysis in Stock
Markets, Proc. of the 24th International Conference on
Neural Information Processing (ICONIP),
LNCS, vol.10635, pp:198-206,2017. [Download(pdf)]
[3.28] 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)]
[3.29] 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.ȻԴͰ(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 17th
International Conference on Intelligent Computing (ICIC),LNCS, vol.12837, pp: 484C498, 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 28th International Conference on
Computational Linguistics (COLING),
pp:3257C3267,2020. [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 28th 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 15th IEEE International Conference on
Signal Processing (ICSP), pp:
274C280, 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 28th 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 14th
International Conference on Intelligent Computing (ICIC), LNAI, vol. 10956, pp: 266C277, 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: 211C220, 2018. [Download(pdf)]
[4.08] Yixin Li and Jinwen Ma, A unified
deep neural network for scene text detection, Proc. of the 13th International Conference on
Intelligent Computing (ICIC), LNCS,
vol.10361, pp:101C112, 2017. [Download(pdf)]
[4.09] Wei Zhao and Jinwen Ma, End-to-end scene text recognition with character
centroid prediction, Proc. of the 24th International Conference on
Neural Information Processing (ICONIP), LNCS, vol.10636, pp: 291C299, 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 (IJCNN06), 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 (SIGKDD04), 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ʱѧϰ(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 (IJCNN02), 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)]