Interference of Signals and Generalization in Feedforward Neural Networks

Title: Interference of Signals and Generalization in Feedforward Neural Networks

Research Question: How does the interference of signals in feedforward neural networks affect the generalization ability?

Methodology: The study investigates the concept of strong propagation regions in the input spaces of neurons. It discusses how these regions can lead to interference of signals, which in turn can impact the generalization ability of the network.

Results: The research found that interference of signals can improve generalization in some cases, while in others it can lead to highly random changes in the network's generalizing function. This can result in a deterioration of the network's generalization ability.

Implications: The study suggests that the interference of signals in feedforward neural networks is a complex phenomenon that can have a significant impact on the network's performance. Understanding this interference can help in designing more effective neural networks for various tasks.

Link to Article: https://arxiv.org/abs/0310009v1 Authors: arXiv ID: 0310009v1