Moment Matching Denoising Gibbs Sampling
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917617697030144 |
|---|---|
| author | Zhang, Mingtian Hawkins-Hooker, Alex Paige, Brooks Barber, David |
| author_facet | Zhang, Mingtian Hawkins-Hooker, Alex Paige, Brooks Barber, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_11650 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Moment Matching Denoising Gibbs Sampling Zhang, Mingtian Hawkins-Hooker, Alex Paige, Brooks Barber, David Machine Learning 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. |
| title | Moment Matching Denoising Gibbs Sampling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2305.11650 |