Approximate Message

Approximate message passing (AMP) is a family of iterative algorithms used to solve high-dimensional inference problems, primarily focusing on efficiently estimating signals from noisy observations in various statistical models. Current research emphasizes extending AMP's applicability to increasingly complex models, including those with non-separable functions, multi-layer structures, and non-Gaussian noise, often employing Bayesian frameworks and leveraging techniques like expectation-maximization. These advancements improve the accuracy and robustness of AMP for diverse applications such as signal processing, machine learning (including deep learning and embedding generation), and statistical regression, offering computationally efficient alternatives to traditional methods.