autoregressive models

Autoregressive models are a class of machine learning models that generate data sequentially, predicting the next token based on previous ones. On WindowsForum.com, discussions focus on autoregressive transformer models used in video generation, particularly a Microsoft Research breakthrough called Diagonal Decoding (DiagD). This training-free inference acceleration algorithm addresses the slow token-by-token decoding bottleneck in autoregressive models, enabling faster video generation while maintaining visual quality. The topic is relevant for Windows developers, digital artists, and tech enthusiasts interested in AI-driven video generation and performance optimization.