Moment Propagation

Moment propagation is a technique for efficiently estimating the uncertainty in neural network predictions, avoiding the computational cost of traditional sampling methods. Current research focuses on developing analytical solutions for moment propagation through various nonlinearities, including ReLU and GELU activation functions, and applying these methods to Bayesian neural networks (BNNs) and Kalman filters for improved uncertainty quantification. This approach enhances the reliability and robustness of deep learning models, particularly in resource-constrained environments and applications requiring real-time uncertainty estimation, such as autonomous systems and embedded devices.