ReNN：规则嵌入式神经网络
ReNN: Rule-embedded Neural Networks
摘要：
人工神经网络显示出强大的推理能力，但由于缺乏可解释性和对大数据集的前提要求，仍受到批评。本文提出了嵌入规则的神经网络（ReNN）来克服这些不足。ReNN首先进行基于局部的推理以检测局部模式，然后使用基于局部模式的领域知识的规则来生成规则调制图。然后，ReNN进行基于全局的推理，综合局部模式和规则调制的地图。为了解决由规则引起的优化问题，我们采用两阶段优化策略对ReNN模型进行训练。通过在ReNN中引入规则，我们可以增强传统神经网络的长期依赖性，从而提高推理精度。由于不使用神经连接对长期依赖性进行建模，因此可以降低神经网络的复杂性，并且因此可以减少优化神经网络所需的数据量。此外，ReNN的推理可以用局部模式和规则进行分析，因此具有更好的可解释性。在本文中，ReNN已经验证了时间序列检测问题。
英文摘要：
The artificial neural network shows powerful ability of inference, but it is still criticized for lack of interpretability and prerequisite needs of big dataset. This paper proposes the Rule-embedded Neural Network (ReNN) to overcome the shortages. ReNN first makes local-based inferences to detect local patterns, and then uses rules based on domain knowledge about the local patterns to generate rule-modulated map. After that, ReNN makes global-based inferences that synthesizes the local patterns and the rule-modulated map. To solve the optimization problem caused by rules, we use a two-stage optimization strategy to train the ReNN model. By introducing rules into ReNN, we can strengthen traditional neural networks with long-term dependencies which are difficult to learn with limited empirical dataset, thus improving inference accuracy. The complexity of neural networks can be reduced since long-term dependencies are not modeled with neural connections, and thus the amount of data needed to optimize the neural networks can be reduced. Besides, inferences from ReNN can be analyzed with both local patterns and rules, and thus have better interpretability. In this paper, ReNN has been validated with a time-series detection problem.
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。
