This paper introduces an alternative approach to sampling from autoregressive
models. Autoregressive models are typically sampled sequentially, according to
the transition dynamics defined by the model. Instead, we propose a sampling
procedure that initializes a sequence with white noise and follows a Markov
chain defined by Langevin dynamics on the global log-likelihood of the
sequence. This approach parallelizes the sampling process and generalizes to
conditional sampling. Using an autoregressive model as a Bayesian prior, we can
steer the output of a generative model using a conditional likelihood or
constraints. We apply these techniques to autoregressive models in the visual
and audio domains, with competitive results for audio source separation,
super-resolution, and inpainting.

这篇论文介绍了一种新的从自回归模型中采样的方法，提出了使用 Langevin 动力学构建马尔可夫链的方法，将采样过程并行化，并推广到有条件采样。通过将自回归模型作为贝叶斯先验，并使用条件似然或限制，我们可以控制生成模型的输出，应用这些技术于视觉和音频领域的自回归模型，获得了在声源分离、超分辨与修复等任务上有竞争力的结果。