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Neuron Activation-based Computation of Logical Explanations for Deep Neural Networks

arXiv · AI, language, vision and robotics · article · Sep 12, 2026 · UTC

Formal explainability of classifying neural networks (NNs) is an active area of research, providing explanations with provable guarantees of the classification within continuous regions of the input feature space. However, the existing techniques are either limited to individual input features without guarantees on their relations or the provided solutions fail to scale to deep architectures. This paper addresses these issues by introducing a flexible symbolic framework for an efficient, guided computation of explanations of the NN behavior, parametrized by the activations of internal neurons,

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