Name
Abstract | ||
|---|---|---|
Future advances in deep learning and its impact on the development of artificial intelligence (AI) in all fields depend heavily on data size and computational power. Sacrificing massive computing resources in exchange for better precision rates of the network model is recognized by many researchers. This leads to huge computing consumption, while satisfactory results are not always expected when c... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MNET.011.2000475 | IEEE Network |
Keywords | DocType | Volume |
Computational modeling,Training,Quality of experience,Adaptation models,Predictive models,Deep learning,Optimization | Journal | 35 |
Issue | ISSN | Citations |
3 | 0890-8044 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Wang | 1 | 3 | 1.44 |
| Min Chen | 2 | 2369 | 142.44 |
| Nadra Guizani | 3 | 274 | 32.70 |
| Yong Li | 4 | 2972 | 218.82 |
| Hamid Gharavi | 5 | 443 | 59.90 |
| Kai Hwang | 6 | 2317 | 210.97 |