易处理电路运算的合成图谱：从简单变换到复杂信息论变换
A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries
摘要：
电路表示法正在成为表达和推理易处理的生成和判别模型的通用语言。在本文中，我们展示了这些模型的复杂推理场景通常出现在机器学习中-从计算决策树集合的期望到深度混合模型的信息理论分歧-可以用电路上易于处理的模块化运算来表示。具体而言，我们的特点的易处理性的词汇的简单的变换-总和，产品，concentents，权力，concentrations，和指数-在足够的结构约束的电路，他们的操作，并提出了新的硬度结果的情况下，这些属性不满意。建立在这些操作，我们推导出一个统一的框架，推理听话的模型，概括了文献中的几个结果，并开辟了新的听话的推理方案。
英文摘要：
Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models. In this paper, we show how complex inference scenarios for these models that commonly arise in machine learning -- from computing the expectations of decision tree ensembles to information-theoretic divergences of deep mixture models -- can be represented in terms of tractable modular operations over circuits. Specifically, we characterize the tractability of a vocabulary of simple transformations -- sums, products, quotients, powers, logarithms, and exponentials -- in terms of sufficient structural constraints of the circuits they operate on, and present novel hardness results for the cases in which these properties are not satisfied. Building on these operations, we derive a unified framework for reasoning about tractable models that generalizes several results in the literature and opens up novel tractable inference scenarios.
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。
