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A new termination analysis approach based on the generation of well-founded transition invariants that leverages Craig interpolation for transition invariant generation, capturing the structural reasons for termination.
This paper addresses formal explainability of classifying neural networks by introducing a flexible symbolic framework for an efficient, guided computation of explanations of the NN behavior, parametrized by the activations of internal neurons, and using logical engines such as SMT solvers.
This work introduces space explanations, a logic-based notion of explanation that represents sufficient conditions for a neural network to predict a given class over a (potentially large and geometrically complex) subset of the feature space and demonstrates that the interpolation-based explanations are more meaningful...
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