CSpace
(本次检索基于用户作品认领结果)

浏览/检索结果: 共12条,第1-10条 帮助

限定条件        
已选(0)清除 条数/页:   排序方式:
MCFL: multi-label contrastive focal loss for deep imbalanced pedestrian attribute recognition 期刊论文
NEURAL COMPUTING & APPLICATIONS, 2022, 页码: 15
作者:  Chen, Lin;  Song, Jingkuan;  Zhang, Xuerui;  Shang, Mingsheng
收藏  |  浏览/下载:98/0  |  提交时间:2022/08/22
Pedestrian attribute recognition  Multi-label contrastive loss  Deep convolutional neural network  Multi-label learning  Imbalanced learning  
Dynamic Neural Network for Bicriteria Weighted Control of Robot Manipulators 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 14
作者:  Liu, Mei;  He, Li;  Shang, Mingsheng
收藏  |  浏览/下载:54/0  |  提交时间:2022/08/22
Manipulators  Robots  Indexes  Mathematical models  Manipulator dynamics  Optimization  Kinematics  Bicriteria weighted (BCW) scheme  dynamic neural network (DNN)  quadratic programming (QP) problem  robustness  
Activated Gradients for Deep Neural Networks 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 13
作者:  Liu, Mei;  Chen, Liangming;  Du, Xiaohao;  Jin, Long;  Shang, Mingsheng
收藏  |  浏览/下载:50/0  |  提交时间:2022/08/22
Training  Deep learning  Neural networks  Optimization  Visualization  Newton method  Eigenvalues and eigenfunctions  Exploding gradient problems  gradient activation function (GAF)  ill-conditioned problems  saddle point problems  vanishing gradient problems  
An L-1-and-L-2-Norm-Oriented Latent Factor Model for Recommender Systems 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 14
作者:  Wu, Di;  Shang, Mingsheng;  Luo, Xin;  Wang, Zidong
收藏  |  浏览/下载:42/0  |  提交时间:2022/08/22
High-dimensional and sparse (HiDS) matrix  latent factor (LF) analysis  L-1 norm  L-2 norm  recommender system (RS)  
An alpha -beta -Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2021, 页码: 13
作者:  Shang, Mingsheng;  Yuan, Ye;  Luo, Xin;  Zhou, MengChu
收藏  |  浏览/下载:53/0  |  提交时间:2022/08/22
Computational modeling  Sparse matrices  Convergence  Data models  Predictive models  Linear programming  Euclidean distance  -divergence  big data  convergence analysis  high-dimensional and sparse (HiDS) data  momentum  machine learning  missing data estimation  non-negative latent factor analysis (NLFA)  recommender system (RS)  
DCCR: Deep Collaborative Conjunctive Recommender for Rating Prediction 期刊论文
IEEE ACCESS, 2019, 卷号: 7, 页码: 60186-60198
作者:  Wang, Qingxian;  Peng, Binbin;  Shi, Xiaoyu;  Shang, Tianqi;  Shang, Mingsheng
Adobe PDF(4751Kb)  |  收藏  |  浏览/下载:143/0  |  提交时间:2019/06/24
Recommender systems  collaborative filtering  rating prediction  denoising autoencoders  multi layered perceptron  
Self-training semi-supervised classification based on density peaks of data 期刊论文
NEUROCOMPUTING, 2018, 卷号: 275, 页码: 180-191
作者:  Wu, Di;  Shang, Mingsheng;  Luo, Xin;  Xu, Ji;  Yan, Huyong;  Deng, Weihui;  Wang, Guoyin
收藏  |  浏览/下载:167/0  |  提交时间:2018/03/05
Density peaks  Self-training  Semi-supervised classification  Supervised learning  
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:415/0  |  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:902/1  |  提交时间:2019/06/25
Long-term performance of collaborative filtering based recommenders in temporally evolving systems 期刊论文
NEUROCOMPUTING, 2017, 卷号: 267, 页码: 635-643
作者:  Shi, Xiaoyu;  Luo, Xin;  Shang, Mingsheng;  Gu, Liang
收藏  |  浏览/下载:108/0  |  提交时间:2018/03/05
Learning system  Recommender system  One-step recommendation  Long-term effect  Temporally evolving system  Bipartite network