In this paper we present various approaches for speeding up learning. We first consider a restriction of PLP called Liftable PLP (LPLP) in which clauses in the program share the same predicate (the target). Then we extend this restriction in Hierarchical PLP (HPLP) where predicates and clauses are hierarchically organized and can be translated into Deep Neural Networks or Arithmetic Circuits.
Published at:
17th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2018)