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Semirings for Probabilistic and Neuro-Symbolic Logic Programming
The field of probabilistic logic programming (PLP) focuses on integrating probabilistic models into programming languages based on logic.
Pruning-Based Extraction of Descriptions from Probabilistic Circuits
As part of this approach we present two contributions: PUTPUT, an algorithm to prune low density regions from a probabilistic circuit while considering both the F1-score and a newly proposed description length that we call aggregated entropy.
Top-Down Knowledge Compilation for Counting Modulo Theories
Propositional model counting (#SAT) can be solved efficiently when the input formula is in deterministic decomposable negation normal form (d-DNNF).
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