Probabilistic Circuits for Uncertainty Quantification
English summary
Deep learning models often exhibit blind confidence on out-of-distribution data due to poor epistemic uncertainty quantification. This paper reviews Probabilistic Circuits (PCs), a tractable probabilistic modeling framework that enforces structural constraints—smoothness, decomposability, and determinism—to enable exact, polynomial-time computation of marginal, conditional, and moment queries without retraining. The work discusses the advantages of PCs for rigorous uncertainty quantification and highlights their potential for tractable UQ in high-dimensional problems.
Chinese summary
深度学习模型在分布外数据上常表现出盲目的自信心，原因在于其认知不确定性量化能力不足。本文综述了概率电路（PC）这一易处理的概率建模框架，该框架通过施加平滑性、可分解性和确定性等结构约束，无需重新训练即可在多项式时间内精确计算边缘、条件和矩查询。文章探讨了概率电路在严格不确定性量化方面的优势，并强调了其在高维问题中实现易处理 UQ 的潜力。
Key points
Deep learning models frequently show miscalibrated epistemic uncertainty, leading to overconfidence on out-of-distribution inputs.
深度学习模型经常表现出认知不确定性校准偏差，在分布外输入上过度自信。
Probabilistic Circuits guarantee tractable exact inference for marginals, conditionals, and moments by imposing structural properties like smoothness and decomposability.
概率电路通过施加平滑性和可分解性等结构特性，保证对边缘、条件和矩进行易处理的精确推理。
The paper reviews PCs as a framework for rigorous uncertainty quantification and discusses their applicability to high-dimensional problems.
论文综述了概率电路作为严格不确定性量化框架的应用，并讨论了其在高维问题中的适用性。