Sparsity Penalty

Sparsity penalties are used in machine learning and signal processing to encourage simpler, more efficient models by minimizing the number of non-zero parameters. Current research focuses on improving the effectiveness of these penalties, particularly within neural network pruning, exploring techniques like constrained optimization to directly control sparsity levels and novel training methods to mitigate performance degradation during sparsification. These advancements lead to smaller, faster models with reduced computational costs and improved robustness, impacting areas such as resource-constrained device deployment and efficient graph learning.