切片与解释：通过领域切片为神经网络提供逻辑基础的解释
Slice and Explain: Logic-Based Explanations for Neural Networks through Domain Slicing
AI总结：
本文通过领域切片方法提高神经网络解释的效率和可扩展性，提供基于逻辑的解释生成方案。
摘要：
神经网络（NNs）在各种领域中普遍存在，但通常缺乏可解释性。为应对日益增长的解释需求，已提出基于逻辑的方法来解释NN的预测，提供正确性保证。然而，这些方法在可扩展性方面仍存在担忧。本文提出了一种利用领域切片来促进NN解释生成的方法。通过切片减少逻辑约束的复杂性，我们通过比较实验表明，解释时间可减少高达40%。我们的发现突显了领域切片在增强NN解释效率方面的有效性。
英文摘要：
Neural networks (NNs) are pervasive across various domains but often lack interpretability. To address the growing need for explanations, logic-based approaches have been proposed to explain predictions made by NNs, offering correctness guarantees. However, scalability remains a concern in these methods. This paper proposes an approach leveraging domain slicing to facilitate explanation generation for NNs. By reducing the complexity of logical constraints through slicing, we decrease explanation time by up to 40\% less time, as indicated through comparative experiments. Our findings highlight the efficacy of domain slicing in enhancing explanation efficiency for NNs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
