Autoregressive Model

Autoregressive models are a class of generative models that predict sequential data by probabilistically modeling the next element based on preceding ones. Current research focuses on enhancing their controllability for tasks like image and 3D shape generation, improving their efficiency through parallel processing and non-autoregressive techniques, and applying them to diverse domains including time series forecasting, speech recognition, and even scientific modeling of physical systems. These advancements are driving improvements in various applications, from more accurate weather forecasting and efficient speech recognition to novel approaches in drug discovery and materials science.