Name
Title | ||
|---|---|---|
An Empirical Study on Improving the Speed and Generalization of Neural Networks Using a Parallel Circuit Approach. |
Abstract | ||
|---|---|---|
One of the common problems of neural networks, especially those with many layers, consists of their lengthy training time. We attempt to solve this problem at the algorithmic level, proposing a simple parallel design which is inspired by the parallel circuits found in the human retina. To avoid large matrix calculations, we split the original network vertically into parallel circuits and let the backpropagation algorithm flow in each subnetwork independently. Experimental results have shown the speed advantage of the proposed approach but also point out that this advantage is affected by multiple dependencies. The results also suggest that parallel circuits improve the generalization ability of neural networks presumably due to automatic problem decomposition. By studying network sparsity, we partly justified this theory and proposed a potential method for improving the design. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s10766-016-0435-4 | International Journal of Parallel Programming |
Keywords | Field | DocType |
Neural networks, Parallel circuits, Problem decomposition, Backpropagation, Sparsity | Potential method,Computer science,Matrix (mathematics),Algorithm,Theoretical computer science,Series and parallel circuits,Backpropagation,Artificial neural network,Subnetwork,Empirical research | Journal |
Volume | Issue | ISSN |
45 | 4 | 1573-7640 |
Citations | PageRank | References |
1 | 0.35 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kien Tuong Phan | 1 | 2 | 1.07 |
| T. H. Maul | 2 | 17 | 6.41 |
| Tuong Thuy Vu | 3 | 37 | 5.47 |