Autoregressive Sequence

Autoregressive sequence modeling focuses on probabilistically predicting the next element in a sequence based on preceding elements, aiming to generate coherent and contextually relevant outputs. Current research emphasizes improving the efficiency and capabilities of this approach, particularly using transformer networks and state space models, while addressing challenges like computational cost and the handling of complex data structures (e.g., 3D medical images, graphs). This methodology has significant implications across diverse fields, from natural language processing and image generation to scientific modeling and robotics, offering powerful tools for data representation, generation, and analysis.