Name
Abstract | ||
|---|---|---|
Recently, deep learning models exhibit promising performance in various applications. However, most of them converge slowly due to gradient vanishing. To address this problem, we propose a fast convergent fully connected deep learning network in this study. Through constraining the input values of nodes on the fully connected layers, the proposed method is able to well mitigate the gradient vanishing problems in training phase, and thus greatly reduces the training iterations required to reach convergence. Nevertheless, the drop of generalization performance is negligible. Experimental results validate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11063-018-9872-y | Neural Processing Letters |
Keywords | Field | DocType |
Deep learning model,Fast convergent method,Constrained input value of nodes | Convergence (routing),Algorithm,Artificial intelligence,Deep learning,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
49.0 | 3.0 | 1573-773X |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Ding | 1 | 2 | 2.39 |
| Ying Li | 2 | 0 | 0.34 |
| Lei Zhang | 3 | 16 | 4.99 |
| Jinyang Zhang | 4 | 4 | 1.41 |
| Yang Lu | 5 | 53 | 18.68 |
| Wei Wei | 6 | 507 | 68.07 |
| Yong Xia | 7 | 14 | 5.04 |
| Yanning Zhang | 8 | 1613 | 176.32 |