Variational Gaussian

Variational Gaussian methods leverage the tractability of Gaussian distributions to approximate complex probability distributions, primarily aiming to improve efficiency and scalability in Bayesian inference and machine learning. Current research focuses on extending variational Gaussian approaches to handle high-dimensional data and complex models, including applications in 3D reconstruction, online supervised learning, and neural network compression, often employing techniques like distributed inference and novel parameterizations to enhance performance. These advancements are significant for tackling computationally intensive problems in various fields, enabling efficient probabilistic modeling and inference in large-scale applications such as robotics and sensor networks.