CASIA OpenIR
(本次检索基于用户作品认领结果)

浏览/检索结果: 共4条,第1-4条 帮助

限定条件                
已选(0)清除 条数/页:   排序方式:
DATA: Differentiable ArchiTecture Approximation With Distribution Guided Sampling 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2021, 卷号: 43, 期号: 9, 页码: 2905-2920
作者:  Zhang, Xinbang;  Chang, Jianlong;  Guo, Yiwen;  Meng, Gaofeng;  Xiang, Shiming;  Lin, Zhouchen;  Pan, Chunhong
Adobe PDF(1346Kb)  |  收藏  |  浏览/下载:304/50  |  提交时间:2021/11/02
Computer architecture  Search problems  Optimization  Task analysis  Bridges  Binary codes  Estimation  Neural architecture search(NAS)  ensemble gumbel-softmax  distribution guided sampling  
MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 卷号: 23, 期号: 7, 页码: 14
作者:  Fang, Shen;  Prinet, Veronique;  Chang, Jianlong;  Werman, Michael;  Zhang, Chunxia;  Xiang, Shiming;  Pan, Chunhong
收藏  |  浏览/下载:217/0  |  提交时间:2022/01/27
Feature extraction  Convolution  Predictive models  Data models  Correlation  Roads  Kernel  Graph convolution  deep attention mechanism  traffic network  traffic flow prediction  artificial intelligence  deep learning  
Decoupled Representation Learning for Character Glyph Synthesis 期刊论文
IEEE Transactions on Multimedia, 2021, 卷号: 2021, 期号: 2021, 页码: 1-13
作者:  Xiyan Liu;  Gaofeng Meng;  Jianlong Chang;  Ruiguang Hu;  Shiming Xiang;  Chunhong Pan
Adobe PDF(4588Kb)  |  收藏  |  浏览/下载:183/48  |  提交时间:2022/01/24
Character glyph synthesis  Decoupled representation  generative adversarial networks  
Deep Self-Evolution Clustering 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2020, 卷号: 42, 期号: 4, 页码: 809-823
作者:  Chang, Jianlong;  Meng, Gaofeng;  Wang, Lingfeng;  Xiang, Shiming;  Pan, Chunhong
浏览  |  Adobe PDF(4817Kb)  |  收藏  |  浏览/下载:408/89  |  提交时间:2020/06/02
Task analysis  Unsupervised learning  Training  Clustering methods  Pattern analysis  Clustering  deep self-evolution clustering  self-evolution clustering training  deep unsupervised learning