Approximate Message Passing

Approximate message passing (AMP) is a family of iterative algorithms used to solve high-dimensional inference problems, primarily aiming to efficiently estimate signals from noisy observations in various models. Current research focuses on extending AMP's applicability to complex scenarios, including graph neural networks (addressing issues like oversquashing and oversmoothing), matrix factorization with diverse constraints, and robust solutions to problems with adversarial corruptions. These advancements enhance AMP's utility in diverse fields like compressed sensing, signal processing, and machine learning, offering improved accuracy, robustness, and computational efficiency compared to existing methods.