Bayesian networks (BN) are a popular representation for reasoning under uncertainty. The analysis of many real-world use cases, that in principle can be modeled by BNs, suffers however from the computational complexity inference. Inference methods based on Weighted Model Counting (WMC) reduce cost inference exploiting patterns exhibited probabilities associated with BN nodes. However, these req...

Tables of data are used to record vast amounts socioeconomic, scientific, and governmental information. Although humans create tables using underlying organizational principles, unfortunately AI systems struggle understand the contents these tables. This paper introduces an end-to-end system for table understanding, process capturing relational structure in We introduce models that identify cel...