论文标题
关于概率电路与确定点过程之间的关系
On the Relationship Between Probabilistic Circuits and Determinantal Point Processes
论文作者
论文摘要
将概率模型缩放到大型现实问题和数据集中是机器学习的关键挑战。这项工作的核心是开发可进行的概率模型（TPMS）：结构保证有效概率推理算法的模型。 TPM的当前景观是分散的：存在各种具有不同优势和劣势的TPM。 TPM最突出的两个类别是确定点过程（DPP）和概率电路（PC）。本文提供了对其关系的首次系统研究。我们提出了一种统一的分析和共享语言，用于讨论DPP和PC。然后，我们建立了统一这两个家庭的理论障碍，并证明在某些情况下，DPP没有紧凑的代表作为一类PC。我们对统一这些可拖动模型的核心问题的观点结束。
Scaling probabilistic models to large realistic problems and datasets is a key challenge in machine learning. Central to this effort is the development of tractable probabilistic models (TPMs): models whose structure guarantees efficient probabilistic inference algorithms. The current landscape of TPMs is fragmented: there exist various kinds of TPMs with different strengths and weaknesses. Two of the most prominent classes of TPMs are determinantal point processes (DPPs) and probabilistic circuits (PCs). This paper provides the first systematic study of their relationship. We propose a unified analysis and shared language for discussing DPPs and PCs. Then we establish theoretical barriers for the unification of these two families, and prove that there are cases where DPPs have no compact representation as a class of PCs. We close with a perspective on the central problem of unifying these tractable models.
