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- W2521848294 abstract "In order to handle real-world problems, state-of-the-art probabilistic logic and learning frameworks, such as ProbLog, reduce the expensive inference to an efficient Weighted Model Counting. To do so ProbLog employs a sequence of transformation steps, called an inference pipeline. Each step in the probabilistic inference pipeline is called a pipeline component. The choice of the mechanism to implement a component can be crucial to the performance of the system. In this paper we describe in detail different ProbLog pipelines and investigate which are the crucial components with respect to the efficiency. Our main contributions are the thorough analysis of ProbLog inference pipelines and the introduction of new pipelines, one of which performs very well on our benchmarks. In this document we describe in detail our benchmarks and report our full results. Then we discuss our results and determine the dependency between the implementation of the components and the overall pipeline performance." @default.
- W2521848294 created "2016-09-30" @default.
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- W2521848294 date "2015-03-01" @default.
- W2521848294 modified "2023-09-25" @default.
- W2521848294 title "Implementation and performance of probabilistic inference pipelines: Data and results" @default.
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