Name
Abstract | ||
|---|---|---|
Deep learning, as the most important architecture of current computational intelligence, achieves super performance to predict the cloud workload for industry informatics. However, it is a nontrivial task to train a deep learning model efficiently since the deep learning model often includes a great number of parameters. In this paper, an efficient deep learning model based on the canonical polyad... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TII.2018.2808910 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Tensile stress,Informatics,Cloud computing,Machine learning,Virtual machining,Industries,Computational modeling | Workload prediction,Informatics,PlanetLab,Virtual machine,Computational intelligence,Workload,Computer science,Real-time computing,Artificial intelligence,Deep learning,Machine learning,Cloud computing | Journal |
Volume | Issue | ISSN |
14 | 7 | 1551-3203 |
Citations | PageRank | References |
23 | 0.75 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingchen Zhang | 1 | 372 | 19.17 |
| Laurence T. Yang | 2 | 6870 | 682.61 |
| Zheng Yan | 3 | 923 | 67.53 |
| Zhikui Chen | 4 | 692 | 66.76 |
| P. Li | 5 | 214 | 28.84 |