Sparsity Prior

Sparsity priors are statistical methods used to encourage sparsity—the presence of many zero or near-zero values—in model parameters, leading to more efficient and interpretable models. Current research focuses on incorporating sparsity priors into various machine learning models, including neural networks and dictionary learning, often employing Bayesian methods like variational inference and algorithms such as ADMM to optimize the resulting models. This approach is proving valuable in diverse applications, such as image processing, signal reconstruction, and continual learning, by reducing computational costs, improving generalization, and enhancing robustness to noise and out-of-distribution data. The resulting sparse models offer advantages in terms of reduced memory footprint, faster inference, and improved interpretability.