Anytime weighted model counting with approximation guarantees for probabilistic inference
Weighted model counting (WMC) plays a central role in probabilistic reasoning. Given that
this problem is# P-hard, harder instances can generally only be addressed using …
this problem is# P-hard, harder instances can generally only be addressed using …
Practical approximate quantifier elimination for non-linear real arithmetic (long version)
Quantifier Elimination (QE) concerns finding a quantifier-free formula that is semantically
equivalent to a quantified formula in a given logic. For the theory of non-linear arithmetic …
equivalent to a quantified formula in a given logic. For the theory of non-linear arithmetic …
PBCounter: weighted model counting on pseudo-boolean formulas
Abstract In Weighted Model Counting (WMC), we assign weights to literals and compute the
sum of the weights of the models of a given propositional formula where the weight of an …
sum of the weights of the models of a given propositional formula where the weight of an …
Disentangling the Gap Between Quantum and# SAT
Weighted model counting (# SAT) has recently been shown to deliver a promising new
method for tackling core problems in quantum circuit analysis. However, the development of …
method for tackling core problems in quantum circuit analysis. However, the development of …
Parameter Learning Using Approximate Model Counting
An emerging class of neurosymbolic methods relies on the use of neural networks to
determine the parameters of symbolic probabilistic models. To train these hybrid models …
determine the parameters of symbolic probabilistic models. To train these hybrid models …