Moment Matching Denoising Gibbs Sampling

• URL: http://arxiv.org/abs/2305.11650v6
• Date: Tue, 19 Mar 2024 09:54:43 GMT
• Title: Moment Matching Denoising Gibbs Sampling
• Authors: Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber,
• Abstract summary: Energy-Based Models (EBMs) offer a versatile framework for modeling complex data distributions. The widely-used Denoising Score Matching (DSM) method for scalable EBM training suffers from inconsistency issues. We propose an efficient sampling framework: (pseudo)-Gibbs sampling with moment matching.
• Score: 14.75945343063504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Energy-Based Models (EBMs) offer a versatile framework for modeling complex data distributions. However, training and sampling from EBMs continue to pose significant challenges. The widely-used Denoising Score Matching (DSM) method for scalable EBM training suffers from inconsistency issues, causing the energy model to learn a `noisy' data distribution. In this work, we propose an efficient sampling framework: (pseudo)-Gibbs sampling with moment matching, which enables effective sampling from the underlying clean model when given a `noisy' model that has been well-trained via DSM. We explore the benefits of our approach compared to related methods and demonstrate how to scale the method to high-dimensional datasets.

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