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Found 5 papers, 1 papers with code
dPASP: A Comprehensive Differentiable Probabilistic Answer Set Programming Environment For Neurosymbolic Learning and Reasoning
We also discuss how gradient-based learning can be performed with neural predicates and probabilistic choices under selected semantics.
Integrating question answering and text-to-SQL in Portuguese
Deep learning transformers have drastically improved systems that automatically answer questions in natural language.
Tractable Inference in Credal Sentential Decision Diagrams
In this work, we develop the credal sentential decision diagrams, a generalisation of their probabilistic counterpart that allows for replacing the local probabilities with (so-called credal) sets of mass functions.
On the Semantics and Complexity of Probabilistic Logic Programs
We examine the meaning and the complexity of probabilistic logic programs that consist of a set of rules and a set of independent probabilistic facts (that is, programs based on Sato's distribution semantics).
The Complexity of Bayesian Networks Specified by Propositional and Relational Languages
We examine the complexity of inference in Bayesian networks specified by logical languages.
