Autoregressive Neural Network

Autoregressive neural networks (ARNNs) are a class of models that predict future data points sequentially, conditioning each prediction on previously generated ones. Current research focuses on extending ARNNs to diverse applications, including robotics, image generation, and time series forecasting, often employing transformer architectures or integrating them with other models like Mamba networks or diffusion models to improve efficiency and performance. This approach is proving valuable for tasks requiring long-range prediction or handling complex dependencies within sequential data, impacting fields ranging from medical image analysis to autonomous systems.