Autoregressive Generative Model

Autoregressive generative models are a class of machine learning models that sequentially predict the next element in a sequence, building up complex data structures like text, images, or time series one step at a time. Current research emphasizes improving these models' efficiency, diversity of outputs, and ability to incorporate contextual information, often leveraging transformer architectures and techniques like diffusion processes. These advancements are driving progress in diverse fields, including scientific modeling (e.g., predicting weather patterns or protein sequences), creative applications (e.g., music and image generation), and efficient knowledge distillation for resource-constrained devices.