Sparse Network

Sparse networks, characterized by a significantly reduced number of connections compared to dense networks, aim to improve efficiency and reduce computational costs in various machine learning applications, particularly deep learning. Current research focuses on developing efficient pruning algorithms (e.g., iterative magnitude pruning, random search) and training strategies for creating and optimizing these sparse architectures, including exploring different initialization methods and activation functions. This research is significant because it addresses the growing need for resource-efficient models, impacting areas such as deep learning deployment on resource-constrained devices and improving the scalability of large-scale machine learning tasks.