Autoregressive Transformer

Autoregressive transformers are a class of neural network models that generate sequential data by predicting the next element in a sequence, one at a time. Current research focuses on improving their efficiency and applicability to diverse data types, including time series, images, 3D shapes, and even analog circuit simulations, often employing novel attention mechanisms and training strategies like packing and contrastive learning to enhance performance. These advancements are significant because they enable the generation of high-quality, complex data across various domains, impacting fields ranging from image synthesis and 3D modeling to natural language processing and scientific simulation.