Activation function impact on Sparse Neural Networks
This addresses a previously overlooked factor in sparse network optimization, potentially improving efficiency for resource-constrained AI applications.
This research investigated how different activation functions affect the performance of sparse neural networks at various sparsity levels, finding that certain functions significantly improve accuracy and training stability compared to standard choices.
While the concept of a Sparse Neural Network has been researched for some time, researchers have only recently made notable progress in the matter. Techniques like Sparse Evolutionary Training allow for significantly lower computational complexity when compared to fully connected models by reducing redundant connections. That typically takes place in an iterative process of weight creation and removal during network training. Although there have been numerous approaches to optimize the redistribution of the removed weights, there seems to be little or no study on the effect of activation functions on the performance of the Sparse Networks. This research provides insights into the relationship between the activation function used and the network performance at various sparsity levels.