Distribution and Clusters Approximations as Abstract Domains in Probabilistic Abstract Interpretation to Neural Network Analysis
arXiv cs.AI / 3/27/2026
💬 OpinionIdeas & Deep AnalysisModels & Research
Key Points
- The paper proposes a probabilistic abstract interpretation approach for neural network analysis that studies the density distribution flow over all possible inputs.
- It adds two new abstract domains/approximation methods—distribution approximation and clusters approximation—to complement existing grid-based abstractions.
- The authors define corresponding abstract transformers for these methods and provide theoretical justification for how they operate.
- The paper includes illustrative examples to demonstrate the behavior of the approximations in simple settings.
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