Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics

• URL: http://arxiv.org/abs/2105.08164v1
• Date: Mon, 17 May 2021 21:07:02 GMT
• Title: Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics
• Authors: Vivek Jayaram, John Thickstun
• Abstract summary: 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. We apply these techniques to autoregressive models in the visual and audio domains, with competitive results for audio source separation, super-resolution, and inpainting.
• Score: 13.097161185372151
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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