mlsanityData to 13 Oct 2026

Verification for Machine Learning, Autonomy, and Neural Networks Survey

arXiv 1810.01989 ↗First posted 3 Oct 2018cs.AI cs.LGcited by 24

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Abstract (arXiv)

This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent advances in artificial intelligence (AI) and machine learning (ML) through approaches such as deep neural networks (DNNs), embedded in so-called learning enabled components (LECs) that accomplish tasks from classification to control. Recently, the formal methods and formal verification community has developed methods to characterize behaviors in these LECs with eventual goals of formally verifying specifications for LECs, and this article presents a survey of many of these recent approaches.

Key facts. Cited by 24 papers in the map (data through 13 Oct 2026). First posted on arXiv 3 Oct 2018.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 7; 2022: 9; 2023: 2; 2024: 4; 2025: 0; 2026: 2.

24 papers.

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  1. 2022Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspectivecited by 42
  2. 2024A review on data-driven constitutive laws for solidscited by 35
  3. 2022SoK: Explainable Machine Learning for Computer Security Applicationscited by 20
  4. 2021Neural Network Verification in Controlcited by 14
  5. 2022Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systemscited by 9
  6. 2021Bisimulations for Neural Network Reductioncited by 6
  7. 2022Provable Fairness for Neural Network Models using Formal Verificationcited by 6
  8. 2021Formal Verification of Unknown Dynamical Systems via Gaussian Process Regressioncited by 5
  9. 2023Using Z3 for Formal Modeling and Verification of FNN Global Robustnesscited by 5
  10. 2021Agility in Software 2.0 -- Notebook Interfaces and MLOps with Buttresses and Rebarscited by 4
  11. 2026TorchLean: Formalizing Neural Networks in Leancited by 4
  12. 2021Reachability analysis of neural networks using mixed monotonicitycited by 3
12 more
  1. 2021Formal Verification of Stochastic Systems with ReLU Neural Network Controllerscited by 2
  2. 2022Deep Binary Reinforcement Learning for Scalable Verificationcited by 2
  3. 2021A Novel Framework for Threat Analysis of Machine Learning-based Smart Healthcare Systemscited by 1
  4. 2022Verification of Sigmoidal Artificial Neural Networks using iSATcited by 1
  5. 2022Safe AI -- How is this Possible?cited by 0
  6. 2022An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehiclescited by 0
  7. 2022Comparative analysis of real bugs in open-source Machine Learning projects -- A Registered Reportcited by 0
  8. 2023Reachability Analysis of Neural Networks with Uncertain Parameterscited by 0
  9. 2024Secure Multiparty Generative AIcited by 0
  10. 2024Physics-model-guided Worst-case Sampling for Safe Reinforcement Learningcited by 0
  11. 2024A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving Systemcited by 0
  12. 2026The Role of Rigor in Artificial Intelligencecited by 0