3 accepted papers
Parallel neurosymbolic architectures have been applied effectively in NLP by distilling knowledge from a logic theory into a deep model. However, prior art faces several limitations including supporting restricted forms of logic theories and relying on the assumption of independence between the logi…
Structure learning is a core problem in AI central to the fields of neuro-symbolic AI and statistical relational learning. It consists in automatically learning a logical theory from data. The basis for structure learning is mining repeating patterns in the data, known as structural motifs. Finding…
Exact inference in probabilistic graphical models is particularly challenging in the presence of relational and other deterministic constraints. For discrete domains, weighted model counting has emerged as an effective and general approach in a variety of formalisms. Weighted first-order model count…