Probabilistic Abstract Interpretation on Neural Networks via Grids Approximation

ArXi:2603.25266v1 Announce Type: new Probabilistic abstract interpretation is a theory used to extract particular properties of a computer program when it is infeasible to test every single inputs. In this paper we apply the theory on neural networks for the same purpose: to analyse density distribution flow of all possible inputs of a neural network when a network has uncountably many or countable but infinitely many inputs.