Neural Estimator

Neural estimators leverage deep learning to approximate complex functions and quantities that are computationally intractable using traditional methods. Current research focuses on applying these estimators to diverse problems, including estimating Shapley values for explainable AI, mutual information for communication systems and image registration, and rate-distortion functions for data compression, often employing architectures like generative networks and variational methods. This rapidly developing field offers significant potential for advancing various scientific disciplines and practical applications by providing scalable and accurate solutions to challenging estimation problems.