A Unified Framework for Human-Allied Learning of Probabilistic Circuits

arXiv:2405.02413v1 Announce Type: new
Abstract: Probabilistic Circuits (PCs) have emerged as an efficient framework for representing and learning complex probability distributions. Nevertheless, the existing body of research on PCs predominantly concentrates on data-driven parameter learning, often neglecting the potential of knowledge-intensive learning, a particular issue in data-scarce/knowledge-rich domains such as healthcare. To bridge this gap, we propose a novel unified framework that can systematically integrate diverse domain knowledge into the parameter learning process of PCs. Experiments on several benchmarks as …

abstract arxiv circuits cs.ai cs.lg data data-driven domains framework healthcare human issue knowledge pcs probability research type