Name
Title | ||
|---|---|---|
A parallel circuit approach for improving the speed and generalization properties of neural networks |
Abstract | ||
|---|---|---|
One of the common problems of neural networks, especially those with many layers consists of their lengthy training times. We attempted to solve this problem at the algorithmic (not hardware) level, proposing a simple parallel design 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 BP algorithm flow in each subnetwork independently. Experimental results have shown the speed advantage of the proposed approach but also pointed out that the reduction 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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICNC.2015.7377956 | 2015 11th International Conference on Natural Computation (ICNC) |
Keywords | Field | DocType |
Neural Networks,Parallel Circuits,Problem Decomposition,Backpropagation | Mathematical optimization,Matrix (mathematics),Computer science,Types of artificial neural networks,Time delay neural network,Artificial intelligence,Series and parallel circuits,Deep learning,Backpropagation,Artificial neural network,Subnetwork,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.38 | 6 |
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 |