Magnitude Based Pruning

Magnitude-based pruning is a neural network compression technique aiming to reduce model size and computational cost while preserving accuracy. Current research focuses on applying this method to various architectures, including vision-language models, graph neural networks, and those used in federated learning and speech recognition, often incorporating iterative pruning strategies and novel metrics to guide the pruning process. This work is significant because it enables deployment of large, powerful models on resource-constrained devices and improves the efficiency of training and inference, impacting both scientific understanding of model sparsity and practical applications across diverse fields.