Improving Neural Network Generalization by Combining Parallel Circuits with Dropout
This work addresses generalization problems in biologically inspired neural networks for machine learning practitioners, but it is incremental as it builds on existing PC and Dropout methods.
The paper tackled the issue of neural network generalization in Parallel Circuits (PCs) by extending Dropout to this architecture, resulting in improved error rates in most cases while maintaining speed advantages.
In an attempt to solve the lengthy training times of neural networks, we proposed Parallel Circuits (PCs), a biologically inspired architecture. Previous work has shown that this approach fails to maintain generalization performance in spite of achieving sharp speed gains. To address this issue, and motivated by the way Dropout prevents node co-adaption, in this paper, we suggest an improvement by extending Dropout to the PC architecture. The paper provides multiple insights into this combination, including a variety of fusion approaches. Experiments show promising results in which improved error rates are achieved in most cases, whilst maintaining the speed advantage of the PC approach.