Random Sparsification

Random sparsification techniques aim to reduce the computational and memory demands of large-scale machine learning models, particularly deep neural networks and large language models, by strategically removing less important parameters or activations while preserving performance. Current research focuses on developing efficient algorithms for sparsification, including those based on Bayesian inference, alternating minimization, and thresholding methods applied to various model architectures like transformers and graph neural networks. These advancements are significant for deploying large models on resource-constrained devices and improving the efficiency of distributed training paradigms such as federated learning, impacting both scientific understanding and practical applications.