Magnitude Pruning

Magnitude pruning is a neural network compression technique aiming to identify and retain only the most important network weights, thereby reducing model size and computational cost without significant performance loss. Current research focuses on improving the efficiency and effectiveness of iterative magnitude pruning algorithms, particularly within large language models (LLMs) and graph convolutional networks (GCNs), often exploring connections to the lottery ticket hypothesis and employing techniques like learning rate rewinding and weight averaging. This work is significant because it addresses the growing need for efficient and deployable deep learning models, impacting both the scalability of training large models and the resource constraints of edge computing applications.