mlsanityData to 7 Oct 2026

Learning Signal Temporal Logic through Neural Network for Interpretable Classification

arXiv 2210.01910 ↗First posted 4 Oct 2022cs.FL cs.LGcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 12 of its 26 references that are in the map. First posted on arXiv 4 Oct 2022.

Builds on

12 of 26 references.

References · 12

  1. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,031
  2. 2017End-to-End Differentiable Provingcited by 159
  3. 2016Training Sparse Neural Networkscited by 73
  4. 2020Backpropagation through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methodscited by 51
  5. 2020Specifying User Preferences using Weighted Signal Temporal Logiccited by 22
  6. 2019Arithmetic-Geometric Mean Robustness for Control from Signal Temporal Logic Specificationscited by 19
  7. 2019Interpretable Classification of Time-Series Data using Efficient Enumerative Techniquescited by 19
  8. 2015Mining Parametric Temporal Logic Properties in Model Based Design for Cyber-Physical Systemscited by 16
  9. 2021Classification of Time-Series Data Using Boosted Decision Treescited by 9
  10. 2021Neural Network for Weighted Signal Temporal Logiccited by 5
  11. 2021Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networkscited by 4
  12. 2022Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Networkcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 0; 2026: 3.

10 papers.

  1. 2024TLINet: Differentiable Neural Network Temporal Logic Inferencecited by 8
  2. 2024stl2vec: Semantic and Interpretable Vector Representation of Temporal Logiccited by 3
  3. 2024Interpretable Imitation Learning via Generative Adversarial STL Inference and Controlcited by 2
  4. 2024Multi-class Temporal Logic Neural Networkscited by 2
  5. 2026ClarifySTL: An Interactive LLM Agent Framework for STL Transformation through Requirements Clarificationcited by 2
  6. 2023On Generating Explanations for Reinforcement Learning Policies: An Empirical Studycited by 1
  7. 2023A Safe Preference Learning Approach for Personalization with Applications to Autonomous Vehiclescited by 1
  8. 2026Conformalized Signal Temporal Logic Inference under Covariate Shiftcited by 1
  9. 2026SCP-NL2TL: Selective Conformal Prediction with Semantic Verification for Natural Language to Temporal Logic Specificationscited by 1
  10. 2024Learning Optimal Signal Temporal Logic Decision Trees for Classification: A Max-Flow MILP Formulationcited by 0