论文标题
PGMAX：离散概率图形模型的因子图和jax中的循环信念传播
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX
论文作者
论文摘要
PGMAX是一个开源Python软件包，用于（a）易于将离散概率图形模型（PGM）指定为因子图； （b）在JAX中自动运行有效且可扩展的循环信念传播（LBP）。 PGMAX支持具有可拖动因子的一般因素图，并利用GPU等现代加速器进行推理。与现有替代方案相比，PGMAX获得了更高质量的推理结果，最多三个刻度的推理时间加速。 PGMAX还与快速增长的JAX生态系统无缝相互作用，开辟了新的研究可能性。我们的源代码，示例和文档可在https://github.com/deepmind/pgmax上找到。
PGMax is an open-source Python package for (a) easily specifying discrete Probabilistic Graphical Models (PGMs) as factor graphs; and (b) automatically running efficient and scalable loopy belief propagation (LBP) in JAX. PGMax supports general factor graphs with tractable factors, and leverages modern accelerators like GPUs for inference. Compared with existing alternatives, PGMax obtains higher-quality inference results with up to three orders-of-magnitude inference time speedups. PGMax additionally interacts seamlessly with the rapidly growing JAX ecosystem, opening up new research possibilities. Our source code, examples and documentation are available at https://github.com/deepmind/PGMax.
