Neural Autoregressive Sequence

Neural autoregressive sequence models generate sequences (like text or robot actions) one element at a time, predicting the next element based on the preceding ones. Current research focuses on improving efficiency and performance through architectural innovations like transformers and novel training techniques such as attention interleaving and sample-specific encoder perturbations, aiming to address issues like premature termination and oversmoothing. These advancements are impacting diverse fields, enabling better robotic control, more accurate machine translation, and improved probabilistic querying of sequential data.