论文标题
通过神经网络学习信号的时间逻辑，以解释分类
Learning Signal Temporal Logic through Neural Network for Interpretable Classification
论文作者
论文摘要
使用神经网络的机器学习技术已取得了有希望的时间序列数据分类的成功。但是，他们生产的模型挑战验证和解释。在本文中，我们提出了一个可解释的神经符号框架，用于分类时间序列行为。特别是，我们使用一种表达的形式语言，即信号时间逻辑（STL）来限制对神经网络的计算图的搜索。我们设计了一种新颖的时间功能和稀疏的软磁性功能，以提高神经-STL框架的声音和精度。结果，我们可以通过基于现成的梯度工具有效地学习一个紧凑的STL公式，以分类时间序列数据。与最新基准相比，我们通过驱动方案和海军监视案例研究来证明所提出方法的计算效率，紧凑性和解释性。
Machine learning techniques using neural networks have achieved promising success for time-series data classification. However, the models that they produce are challenging to verify and interpret. In this paper, we propose an explainable neural-symbolic framework for the classification of time-series behaviors. In particular, we use an expressive formal language, namely Signal Temporal Logic (STL), to constrain the search of the computation graph for a neural network. We design a novel time function and sparse softmax function to improve the soundness and precision of the neural-STL framework. As a result, we can efficiently learn a compact STL formula for the classification of time-series data through off-the-shelf gradient-based tools. We demonstrate the computational efficiency, compactness, and interpretability of the proposed method through driving scenarios and naval surveillance case studies, compared with state-of-the-art baselines.
