Sparse Model

Sparse models aim to reduce the computational cost and memory footprint of machine learning models by strategically removing less important parameters, while maintaining or improving performance. Current research focuses on developing efficient algorithms for creating and training sparse models, including techniques like pruning, mixture-of-experts (MoE), and various regularization methods, often applied to large language models (LLMs) and vision-language models (VLMs). This work is significant because it addresses the growing need for deploying complex models on resource-constrained devices and improves the efficiency and interpretability of machine learning systems across diverse applications. Furthermore, research is exploring the impact of sparsity on model reliability and fairness, seeking to mitigate potential biases introduced by pruning.