Probabilistic Abstract Interpretation on Neural Networks via Grids Approximation

1. 1Apply probabilistic abstract interpretation theory to neural networks
2. 2Discretize the input space using grids approximation
3. 3Analyze the density distribution flow of all possible inputs
4. 4Evaluate the effectiveness of the approach on various neural network architectures

AI engineers and researchers on a team can benefit from this paper as it provides a theoretical framework for analyzing neural networks, while data scientists can apply the findings to improve model interpretability

💡 Probabilistic abstract interpretation can be used to analyze neural networks with uncountably many or infinitely many inputs

💡 Probabilistic abstract interpretation for neural networks