Learning Signal Temporal Logic through Neural Network for Interpretable Classification
This addresses the need for verifiable and interpretable models in time-series applications like driving and surveillance, though it is incremental as it builds on existing neural-symbolic and STL methods.
The paper tackles the problem of interpretability in neural network-based time-series classification by proposing a neural-symbolic framework that learns Signal Temporal Logic (STL) formulas, resulting in efficient and compact models with improved soundness and precision.
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.