Optimizing Backpropagation with PC Flows

PyJuice introduces a highly efficient way to train probabilistic circuit (PC) models by optimizing backpropagation and reducing GPU memory usage. Through experiments on structured and unstructured PCs, as well as large image datasets, it demonstrates faster training, better scalability, and more flexible applications compared to existing implementations—offering both a practical tool and a benchmark for advancing PC research.