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Combined Physical Process and Deep Learning for Daily Water Level Simulations across Multiple Sites in the Three Gorges Reservoir, China 期刊论文
WATER, 2023, 卷号: 15, 期号: 18, 页码: 16
作者:  Xie, Mingjiang;  Shan, Kun;  Zeng, Sidong;  Wang, Lan;  Gong, Zhigang;  Wu, Xuke;  Yang, Bing;  Shang, Mingsheng
收藏  |  浏览/下载：12/0  |  提交时间：2023/12/25
water level prediction  Three Gorges Reservoir  deep learning  hybrid model  
A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices 期刊论文
IEEE TRANSACTIONS ON BIG DATA, 2022, 卷号: 8, 期号: 3, 页码: 784-794
作者:  Yuan, Ye;  He, Qiang;  Luo, Xin;  Shang, Mingsheng
收藏  |  浏览/下载：75/0  |  提交时间：2022/08/22
Computational modeling  Sparse matrices  Big Data  Data models  Stochastic processes  Training  Software algorithms  Big data  latent factor analysis  generally multilayered structure  deep forest  multilayered extreme learning machine  randomized-learning  high-dimensional and sparse matrix  stochastic gradient descent  randomized model  
A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 卷号: 51, 期号: 7, 页码: 4285-4296
作者:  Wu, Di;  Luo, Xin;  Shang, Mingsheng;  He, Yi;  Wang, Guoyin;  Zhou, MengChu
收藏  |  浏览/下载：204/0  |  提交时间：2021/08/20
Big data  deep model  high-dimensional and sparse (HiDS) matrix  latent factor (LF) analysis  recommender system (RS)  
Extensive exploration of comprehensive vehicle attributes using D-CNN with weighted multi-attribute strategy 期刊论文
IET INTELLIGENT TRANSPORT SYSTEMS, 2018, 卷号: 12, 期号: 3, 页码: 186-193
作者:  Yan, Zhuo;  Feng, Youji;  Cheng, Cheng;  Fu, Jianting;  Zhou, Xiangdong;  Yuan, Jiahu
Adobe PDF(3776Kb)  |  收藏  |  浏览/下载：240/0  |  提交时间：2018/06/04
Object Recognition  Feedforward Neural Nets  Learning (Artificial Intelligence)  Comprehensive Vehicle Attributes  D-cnn  Weighted Multiattribute Strategy  Deep Convolutional Neural Network  Surveillance Images  Vehicle Model Recognition  Make Recognition  Mtl Methods  Multitask Learning  Compcars Vehicle Dataset  Manufacturer Recognition