{"total":19,"rows":[{"id":"2201.01978","title":"An Abstraction-Refinement Approach to Verifying Convolutional Neural Networks","created":"2022-01-06","cited_by_count":24,"area":null},{"id":"2202.03898","title":"Verification-Aided Deep Ensemble Selection","created":"2022-02-08","cited_by_count":18,"area":null},{"id":"2208.14426","title":"Correct-by-Construction Runtime Enforcement in AI -- A Survey","created":"2022-08-30","cited_by_count":12,"area":null},{"id":"2302.05745","title":"Verifying Generalization in Deep Learning","created":"2023-02-11","cited_by_count":11,"area":null},{"id":"2208.14660","title":"Unifying Evaluation of Machine Learning Safety Monitors","created":"2022-08-31","cited_by_count":10,"area":null},{"id":"2212.07773","title":"Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networks","created":"2022-12-15","cited_by_count":7,"area":null},{"id":"2104.14435","title":"Customizable Reference Runtime Monitoring of Neural Networks using Resolution Boxes","created":"2021-04-25","cited_by_count":6,"area":null},{"id":"2106.01917","title":"SpecRepair: Counter-Example Guided Safety Repair of Deep Neural Networks","created":"2021-06-03","cited_by_count":6,"area":null},{"id":"2307.11784","title":"What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems","created":"2023-07-20","cited_by_count":6,"area":null},{"id":"2404.08517","title":"Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward","created":"2024-04-12","cited_by_count":6,"area":null},{"id":"2404.04662","title":"Learning Minimal Neural Specifications","created":"2024-04-06","cited_by_count":3,"area":null},{"id":"2412.06869","title":"Safety Monitoring of Machine Learning Perception Functions: a Survey","created":"2024-12-09","cited_by_count":3,"area":null},{"id":"2410.06051","title":"Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces","created":"2024-10-08","cited_by_count":2,"area":null},{"id":"2410.08491","title":"Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions","created":"2024-10-11","cited_by_count":2,"area":null},{"id":"2511.05982","title":"Runtime Safety Monitoring of Deep Neural Networks for Perception: A Survey","created":"2025-11-08","cited_by_count":2,"area":null},{"id":"2103.03704","title":"Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features","created":"2021-03-05","cited_by_count":1,"area":null},{"id":"2312.00237","title":"Negotiated Representations to Prevent Forgetting in Machine Learning Applications","created":"2023-11-30","cited_by_count":0,"area":null},{"id":"2509.26562","title":"DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis","created":"2025-09-30","cited_by_count":0,"area":null},{"id":"2607.28954","title":"Mining Verdict Boundaries for Neural Network Verification","created":"2026-07-31","cited_by_count":0,"area":null}],"next_cursor":null}