# Verification for Machine Learning, Autonomy, and Neural Networks Survey

arXiv: https://arxiv.org/abs/1810.01989 · HTML: https://mlsanity.com/paper/1810.01989/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2018.

## Built on by (most cited first, 24 of 24)

- [Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective](https://mlsanity.com/paper/2206.12227/) (2022)
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- [SoK: Explainable Machine Learning for Computer Security Applications](https://mlsanity.com/paper/2208.10605/) (2022)
- [Neural Network Verification in Control](https://mlsanity.com/paper/2110.01388/) (2021)
- [Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems](https://mlsanity.com/paper/2203.07505/) (2022)
- [Bisimulations for Neural Network Reduction](https://mlsanity.com/paper/2110.03726/) (2021)
- [Provable Fairness for Neural Network Models using Formal Verification](https://mlsanity.com/paper/2212.08578/) (2022)
- [Formal Verification of Unknown Dynamical Systems via Gaussian Process Regression](https://mlsanity.com/paper/2201.00655/) (2021)
- [Using Z3 for Formal Modeling and Verification of FNN Global Robustness](https://mlsanity.com/paper/2304.10558/) (2023)
- [Agility in Software 2.0 -- Notebook Interfaces and MLOps with Buttresses and Rebars](https://mlsanity.com/paper/2111.14142/) (2021)
- [TorchLean: Formalizing Neural Networks in Lean](https://mlsanity.com/paper/2602.22631/) (2026)
- [Reachability analysis of neural networks using mixed monotonicity](https://mlsanity.com/paper/2111.07683/) (2021)
- [Formal Verification of Stochastic Systems with ReLU Neural Network Controllers](https://mlsanity.com/paper/2103.05142/) (2021)
- [Deep Binary Reinforcement Learning for Scalable Verification](https://mlsanity.com/paper/2203.05704/) (2022)
- [A Novel Framework for Threat Analysis of Machine Learning-based Smart Healthcare Systems](https://mlsanity.com/paper/2103.03472/) (2021)
- [Verification of Sigmoidal Artificial Neural Networks using iSAT](https://mlsanity.com/paper/2207.06755/) (2022)
- [Safe AI -- How is this Possible?](https://mlsanity.com/paper/2201.10436/) (2022)
- [An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles](https://mlsanity.com/paper/2205.01419/) (2022)
- [Comparative analysis of real bugs in open-source Machine Learning projects -- A Registered Report](https://mlsanity.com/paper/2209.09932/) (2022)
- [Reachability Analysis of Neural Networks with Uncertain Parameters](https://mlsanity.com/paper/2303.07917/) (2023)
- [Secure Multiparty Generative AI](https://mlsanity.com/paper/2409.19120/) (2024)
- [Physics-model-guided Worst-case Sampling for Safe Reinforcement Learning](https://mlsanity.com/paper/2412.13224/) (2024)
- [A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System](https://mlsanity.com/paper/2412.16762/) (2024)
- [The Role of Rigor in Artificial Intelligence](https://mlsanity.com/paper/2607.03634/) (2026)

## 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.