Sparsification Method

Sparsification methods aim to reduce the computational complexity and memory footprint of large models, such as deep neural networks and large language models, while preserving performance. Current research focuses on developing efficient sparsification algorithms, including those integrated into training processes (e.g., "always-sparse" training) and post-training techniques that remove redundant parameters or reduce dimensionality. These advancements are crucial for deploying large models on resource-constrained devices and improving the efficiency of machine learning in various applications, including federated learning and control systems.