为什么将提升推理编译成低级语言如此有效？
Why is Compiling Lifted Inference into a Low-Level Language so Effective?
摘要：
一阶知识编译技术已被证明是有效的提升推理。他们将一个关系概率模型编译成一个目标电路，在这个目标电路上可以有效地回答许多推理查询。早期的方法使用数据结构作为其目标电路。在我们的KR-2016论文中，我们展示了编译到低级程序而不是数据结构提供了数量级的加速，从而产生了最先进的提升推理技术。在本文中，我们进行实验来解决关于我们的KR-2016结果的两个问题：1-加速来自更有效的编译还是更有效的目标电路推理？以及2-为什么低级程序比数据结构对目标电路更有效？
英文摘要：
First-order knowledge compilation techniques have proven efficient for lifted inference. They compile a relational probability model into a target circuit on which many inference queries can be answered efficiently. Early methods used data structures as their target circuit. In our KR-2016 paper, we showed that compiling to a low-level program instead of a data structure offers orders of magnitude speedup, resulting in the state-of-the-art lifted inference technique. In this paper, we conduct experiments to address two questions regarding our KR-2016 results: 1- does the speedup come from more efficient compilation or more efficient reasoning with the target circuit?, and 2- why are low-level programs more efficient target circuits than data structures?
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
