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Neural dynamics solver for time-dependent infinity-norm optimization based on ACP framework with robot application 期刊论文
NEUROCOMPUTING, 2024, 卷号: 567, 页码: 9
作者:  Ma, Dexiu;  Liu, Mei;  Shang, Mingsheng
收藏  |  浏览/下载：9/0  |  提交时间：2024/02/23
Neural dynamics (ND)  ACP framework  Time-dependent infinity-norm optimization (TDINO)  Activation functions  
Hyperparameter Learning for Deep Learning-Based Recommender Systems 期刊论文
IEEE TRANSACTIONS ON SERVICES COMPUTING, 2023, 卷号: 16, 期号: 4, 页码: 2699-2712
作者:  Wu, Di;  Sun, Bo;  Shang, Mingsheng
收藏  |  浏览/下载：12/0  |  提交时间：2023/12/25
Terms-Deep learning  differential evolution  grid search  hyperparameter tuning  online services  recommender systems  
Non-Negative Latent Factor Model Based on beta-Divergence for Recommender Systems 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 卷号: 51, 期号: 8, 页码: 4612-4623
作者:  Xin, Luo;  Yuan, Ye;  Zhou, MengChu;  Liu, Zhigang;  Shang, Mingsheng
收藏  |  浏览/下载：130/0  |  提交时间：2021/08/20
beta-divergence  big data  high-dimensional and sparse (HiDS) matrix  industrial application  learning algorithm  non-negative latent factor (NLF) analysis  recommender system  
Algorithms of Unconstrained Non-Negative Latent Factor Analysis for Recommender Systems 期刊论文
IEEE TRANSACTIONS ON BIG DATA, 2021, 卷号: 7, 期号: 1, 页码: 227-240
作者:  Luo, Xin;  Zhou, Mengchu;  Li, Shuai;  Wu, Di;  Liu, Zhigang;  Shang, Mingsheng
收藏  |  浏览/下载：147/0  |  提交时间：2021/05/17
Data models  Training  Sparse matrices  Recommender systems  Computational modeling  Big Data  Scalability  Non-negative latent factor analysis  non-negativity  latent factor analysis  unconstrained optimization  high-dimensional and sparse matrix  collaborative filtering  recommender system  big data  
An Inherently Nonnegative Latent Factor Model for High-Dimensional and Sparse Matrices from Industrial Applications 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 卷号: 14, 期号: 5, 页码: 2011-2022
作者:  Luo, Xin;  Zhou, MengChu;  Li, Shuai;  Shang, MingSheng
收藏  |  浏览/下载：508/0  |  提交时间：2018/07/02
Big data  high-dimensional and sparse matrix  learning algorithms  missing-data estimation  nonnegative latent factor analysis  optimization methods recommender system  
On minimizing total energy consumption in the scheduling of virtual machine reservations 期刊论文
Journal of Network and Computer Applications, 2018, 卷号: 113, 页码: 64-74
作者:  Tian, Wenhong;  He, Majun;  Guo, Wenxia;  Huang, Wenqiang;  Shi, Xiaoyu;  Shang, Mingsheng;  Toosi, Adel Nadjaran;  Buyya, Rajkumar
Adobe PDF(4775Kb)  |  收藏  |  浏览/下载：139/0  |  提交时间：2019/06/26
An inherently nonnegative latent factor model for high-dimensional and sparse matrices from industrial applications 期刊论文
IEEE Transactions on Industrial Informatics, 2018, 卷号: 14, 期号: 5, 页码: 2011-2022
作者:  Luo, Xin;  Zhou, Mengchu;  Li, Shuai;  Shang, Mingsheng
Adobe PDF(805Kb)  |  收藏  |  浏览/下载：416/0  |  提交时间：2019/06/26
TCR: Temporal-CNN for Reviews Based Recommendation System 会议论文
2nd International Conference on Deep Learning Technologies, ICDLT 2018, Chongqing, China, June 27, 2018 - June 29, 2018
作者:  Mao, Yelu;  Shi, Xiaoyu;  Shang, Ming-Sheng;  Zhang, Ying
Adobe PDF(508Kb)  |  收藏  |  浏览/下载：905/1  |  提交时间：2019/06/25
Symmetric and Nonnegative Latent Factor Models for Undirected, High-Dimensional, and Sparse Networks in Industrial Applications 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2017, 卷号: 13, 期号: 6, 页码: 3098-3107
作者:  Luo, Xin;  Sun, Jianpei;  Wang, Zidong;  Li, Shuai;  Shang, Mingsheng
Adobe PDF(803Kb)  |  收藏  |  浏览/下载：428/0  |  提交时间：2018/03/05
Big data application  high-dimensional, and sparse (SHiDS) matrix  nonnegative latent factor (NLF) model  symmetry  undirected HiDS network  
Water eutrophication assessment based on rough set and multidimensional cloud model 期刊论文
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2017, 卷号: 164, 页码: 103-112
作者:  Yan, Huyong;  Wu, Di;  Huang, Yu;  Wang, Guoyin;  Shang, Mingsheng;  Xu, Jianjun;  Shi, Xiaoyu;  Shan, Kun;  Zhou, Botian;  Zhao, Yufei
Adobe PDF(2910Kb)  |  收藏  |  浏览/下载：115/0  |  提交时间：2018/03/05
Eutrophication  Rough set  Multidimensional cloud model