用引力全息原理构建生成模型，实现更快更高质量的训练。
Holographic generative flows with AdS/CFT
- 将数据流映射为反德西特空间的标量场边界-体映射
- 在棋盘与MNIST数据集上收敛速度和生成质量优于传统流匹配模型
- 提供可物理解释的生成机制，适合对理论物理与生成模型交叉感兴趣的读者
我们提出一种基于量子引力全息原理的生成机器学习框架，具体利用反德西特/共形场论（AdS/CFT）对应关系，结合深度学习与传输理论。通过将数据从基分布到学习分布的流动表示为AdS空间中标量场的体-边界映射，在机器学习语言中，我们以AdS物理增强并重构了流匹配算法。在棋盘玩具数据集和MNIST上，我们的模型相比无物理先验的流匹配模型展现出更快的收敛速度和更高的生成质量。该方法提供了可物理解释的流匹配版本，并为生成建模中引入AdS物理与几何开辟了新范式。
原文摘要 · Abstract (English)
We present a framework for generative machine learning that leverages the holographic principle of quantum gravity, or to be more precise its manifestation as the anti-de Sitter/conformal field theory (AdS/CFT) correspondence, with techniques for deep learning and transport theory. Our proposal is to represent the flow of data from a base distribution to some learned distribution using the bulk-to-boundary mapping of scalar fields in AdS. In the language of machine learning, we are representing and augmenting the flow-matching algorithm with AdS physics. Using a checkerboard toy dataset and MNIST, we find that our model achieves faster and higher quality convergence than comparable physics-free flow-matching models. Our method provides a physically interpretable version of flow matching. More broadly, it establishes the utility of AdS physics and geometry in the development of novel paradigms in generative modeling.
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