Sparsity Level

Sparsity level, referring to the proportion of non-zero elements in a data structure or model, is a crucial concept across numerous fields, aiming to reduce computational complexity and improve efficiency while maintaining performance. Current research focuses on optimizing sparsity levels in various contexts, including neural network training (e.g., through pruning and bit-level sparsity), feature selection (using techniques like L1 regularization and generalized singular value problems), and signal recovery (employing algorithms like iterative hard thresholding and alternating minimization). These advancements have significant implications for accelerating deep learning, enhancing the interpretability of models, and improving the efficiency of various signal processing and machine learning applications.