Name
Abstract | ||
|---|---|---|
Being able to learn from complex data with phase information is imperative for many signal processing applications. Today’s real-valued deep neural networks (DNNs) have shown efficiency in latent information analysis but fall short when applied to the complex domain. Deep complex networks (DCN), in contrast, can learn from complex data, but have high computational costs; therefore, they cannot sat... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ASAP52443.2021.00021 | 2021 IEEE 32nd International Conference on Application-specific Systems, Architectures and Processors (ASAP) |
Keywords | DocType | ISSN |
Convolution,Computational modeling,Neural networks,Memory management,Signal processing algorithms,Throughput,Hardware | Conference | 2160-0511 |
ISBN | Citations | PageRank |
978-1-6654-2701-2 | 1 | 0.37 |
References | Authors | |
0 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongwu Peng | 1 | 6 | 1.47 |
| Shanglin Zhou | 2 | 3 | 2.51 |
| Scott Weitze | 3 | 1 | 0.71 |
| Jiaxin Li | 4 | 1 | 0.37 |
| Sahidul Islam | 5 | 2 | 1.42 |
| Tong Geng | 6 | 57 | 14.16 |
| Ang Li | 7 | 201 | 29.68 |
| Wei Zhang | 8 | 1 | 0.37 |
| Minghu Song | 9 | 239 | 24.23 |
| Mimi Xie | 10 | 1 | 1.39 |
| Hang Liu | 11 | 32 | 7.77 |
| Caiwen Ding | 12 | 142 | 26.52 |