用图结构优化自回归模型变量顺序，提升生成质量
Selecting Optimal Variable Order in Autoregressive Ising Models
- 基于数据图结构推断变量依赖关系，构建最优排序
- 在二维伊辛模型上，新顺序使条件分布更简单，生成样本更真实
- 适合需要高质量生成的图像建模任务
自回归模型通过变量顺序因子化实现概率分布采样，但性能高度依赖条件分布的复杂度。本文提出学习数据的马尔可夫随机场，利用图结构构建优化变量顺序。在二维图像类模型中，结构感知排序可缩小条件集，降低模型复杂度。数值实验表明，在离散数据的伊辛模型上，基于图的排序生成样本保真度显著优于随机顺序。
原文摘要 · Abstract (English)
Autoregressive models enable tractable sampling from learned probability distributions, but their performance critically depends on the variable ordering used in the factorization via complexities of the resulting conditional distributions. We propose to learn the Markov random field describing the underlying data, and use the inferred graphical model structure to construct optimized variable orderings. We illustrate our approach on two-dimensional image-like models where a structure-aware ordering leads to restricted conditioning sets, thereby reducing model complexity. Numerical experiments on Ising models with discrete data demonstrate that graph-informed orderings yield higher-fidelity generated samples compared to naive variable orderings.
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