Score Function
Score functions, representing the gradient of a log probability density, are central to various machine learning tasks, primarily aiming to efficiently estimate and utilize probability distributions. Current research focuses on improving score function estimation techniques, particularly for high-dimensional data and discrete domains, employing methods like score matching, neural networks, and nearest-neighbor approaches within models such as diffusion generative models and physics-informed neural networks. These advancements enhance the accuracy and efficiency of generative modeling, Bayesian inference, and optimization problems involving uncertainty, impacting fields ranging from image generation to reinforcement learning and decision-making under uncertainty.