Sparsity Ratio

Sparsity ratio, the proportion of non-zero elements in a data structure or model, is a key focus in machine learning research aiming to improve efficiency and interpretability. Current efforts concentrate on developing algorithms and architectures, such as sparse deep predictive coding networks and generalized additive models, that effectively leverage sparsity while maintaining or improving performance in tasks like video feature extraction and large language model fine-tuning. This research is significant because reducing computational complexity through sparsity enables faster training and inference, particularly crucial for resource-constrained environments like edge devices, and also enhances model interpretability by reducing the number of relevant features.