提出新型马尔可夫随机场，实现快速低能耗采样。
A new class of Markov random fields enabling lightweight sampling
- 通过高斯马尔可夫场映射生成离散马尔可夫场，突破传统采样瓶颈。
- 采样速度提升35倍以上，能耗降低37倍以上，接近经典模型性能。
- 适合需要高效采样的图像分割、结构化预测等任务。
本文针对马尔可夫随机场（MRF）的高效采样问题提出新方法。传统基于吉布斯采样的泊茨或伊辛模型计算成本高，本文通过与高斯马尔可夫随机场（GMRF）的关联，构建从实值GMRF到离散值MRF的映射，形成一类新型MRF。该模型具备良好的理论性质，数值实验表明其在计算效率上显著优于吉布斯采样：采样速度至少提升35倍，能耗至少降低37倍，同时保持与经典MRF相近的统计特性。
原文摘要 · Abstract (English)
This work addresses the problem of efficient sampling of Markov random fields (MRF). The sampling of Potts or Ising MRF is most often based on Gibbs sampling, and is thus computationally expensive. We consider in this work how to circumvent this bottleneck through a link with Gaussian Markov Random fields. The latter can be sampled in several cost-effective ways, and we introduce a mapping from real-valued GMRF to discrete-valued MRF. The resulting new class of MRF benefits from a few theoretical properties that validate the new model. Numerical results show the drastic performance gain in terms of computational efficiency, as we sample at least 35x faster than Gibbs sampling using at least 37x less energy, all the while exhibiting empirical properties close to classical MRFs.
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