Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs
Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs
复制标题
快速关系概率推理和学习：通过超图进行近似计数
DOI：
10.1609/aaai.v33i01.33017816
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Sriraam Natarajan
中科院分区：
文献类型：
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作者：
M. Das;D. Dhami;Gautam Kunapuli;K. Kersting;Sriraam Natarajan
计算子句的真实例的数量可以说是关系概率推理和学习中的一个主要瓶颈。我们通过两个步骤来近似计数：（1）将完全接地的关系模型转换为大型超图，并将部分实例化的子句转换为超图模体;（2）由于模体的预期计数可证明是子句计数，因此使用汇总统计（入/出度，边计数等）来近似它们。我们的实验结果证明了这些近似的有效性，这些近似可以应用于许多复杂的统计关系模型，并且在推理和学习方面都比最先进的方法快得多，而不会牺牲有效性。
Counting the number of true instances of a clause is arguably a major bottleneck in relational probabilistic inference and learning. We approximate counts in two steps: (1) transform the fully grounded relational model to a large hypergraph, and partially-instantiated clauses to hypergraph motifs; (2) since the expected counts of the motifs are provably the clause counts, approximate them using summary statistics (in/outdegrees, edge counts, etc). Our experimental results demonstrate the efficiency of these approximations, which can be applied to many complex statistical relational models, and can be significantly faster than state-of-the-art, both for inference and learning, without sacrificing effectiveness.