Auto Regressive

Autoregressive (AR) models are a class of predictive models that sequentially generate data points, conditioning each new point on previously generated ones. Current research focuses on improving the efficiency and scalability of AR models, particularly for image and 3D shape generation, often addressing limitations through techniques like speculative decoding and latent vector representations. These advancements are impacting diverse fields, from image synthesis and time series forecasting to drug discovery and causal inference, by enabling more efficient and accurate modeling of complex, sequential data. The development of robust and efficient AR models continues to be a significant area of research, driving progress in various scientific and practical applications.