Juice: A Julia Package for Logic and Probabilistic Circuits

Authors

  • Meihua Dang University of California, Los Angeles
  • Pasha Khosravi University of California, Los Angeles
  • Yitao Liang University of California, Los Angeles
  • Antonio Vergari University of California, Los Angeles
  • Guy Van den Broeck University of California, Los Angeles

Keywords:

Tractable Probabilistic Models, Probabilistic Reasoning, Logical Reasoning, Open Source Library

Abstract

Juice is an open-source Julia package providing tools for logic and probabilistic reasoning and learning based on logic circuits (LCs) and probabilistic circuits (PCs). It provides a range of efficient algorithms for probabilistic inference queries, such as computing marginal probabilities (MAR), as well as many more advanced queries. Certain structural circuit properties are needed to achieve this tractability, which Juice helps validate. Additionally, it supports several parameter and structure learning algorithms proposed in the recent literature. By leveraging parallelism (on both CPU and GPU), Juice provides a fast implementation of circuit-based algorithms, which makes it suitable for tackling large-scale datasets and models.

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Published

2021-05-18

How to Cite

Dang, M., Khosravi, P., Liang, Y., Vergari, A., & Van den Broeck, G. (2021). Juice: A Julia Package for Logic and Probabilistic Circuits. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16020-16023. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17999