Autoregressive Context

Autoregressive context modeling focuses on efficiently capturing long-range dependencies within sequential data, improving the accuracy and speed of prediction tasks. Current research emphasizes developing efficient transformer-based architectures, such as Contextformers and Perceiver AR, that address the computational limitations of traditional autoregressive models, particularly for high-dimensional data like images and long text sequences. These advancements are significantly impacting fields like learned image compression and scene text recognition by enabling faster decoding and improved rate-distortion performance, surpassing the capabilities of classical methods. The development of optimized algorithms and novel search strategies for discovering effective architectures further enhances the practical applicability of autoregressive context modeling.