Autoregressive Coding

Autoregressive coding leverages sequential prediction to learn efficient data representations, aiming to minimize information loss during compression or generation. Current research explores diverse applications, from image and voice processing to code generation and graph-based recommendations, employing architectures like nested latent variable models, transformers, and LSTMs. These advancements improve performance in tasks requiring robust feature extraction and efficient data handling, impacting fields ranging from visual inspection to personalized voice recognition and large language models for code.