Probabilistic Abstract Interpretation on Neural Networks via Grids Approximation
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. We show how this theoretical framework works in neural networks and then discuss different abstract domains and corresponding Moore-Penrose pseudo-inverses together with abstract transformers used in the framework. We also present experimental examples to show how this framework helps to analyse real wo...
| Authors | Zhuofan Zhang, Herbert Wiklicky |
|---|---|
| Primary category | cs.AI |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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