{"total":19,"rows":[{"id":"2107.12045","title":"How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review","created":"2021-07-26","cited_by_count":39,"area":null},{"id":"2111.09533","title":"DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior","created":"2021-11-18","cited_by_count":17,"area":null},{"id":"2205.06439","title":"AEON: A Method for Automatic Evaluation of NLP Test Cases","created":"2022-05-13","cited_by_count":15,"area":null},{"id":"2110.01232","title":"Benchmarking Safety Monitors for Image Classifiers with Machine Learning","created":"2021-10-04","cited_by_count":14,"area":null},{"id":"2204.07874","title":"Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System","created":"2022-04-16","cited_by_count":14,"area":null},{"id":"2102.00902","title":"Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring","created":"2021-02-01","cited_by_count":11,"area":null},{"id":"2404.18573","title":"Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification","created":"2024-04-29","cited_by_count":11,"area":null},{"id":"2103.15580","title":"Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks","created":"2021-03-29","cited_by_count":9,"area":null},{"id":"2207.10495","title":"Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing","created":"2022-07-21","cited_by_count":8,"area":null},{"id":"2204.12378","title":"Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks","created":"2022-04-26","cited_by_count":7,"area":null},{"id":"2401.17013","title":"Evaluation of Out-of-Distribution Detection Performance on Autonomous Driving Datasets","created":"2024-01-30","cited_by_count":5,"area":null},{"id":"2202.03059","title":"Evaluation of Runtime Monitoring for UAV Emergency Landing","created":"2022-02-07","cited_by_count":4,"area":null},{"id":"2405.13254","title":"System Safety Monitoring of Learned Components Using Temporal Metric Forecasting","created":"2024-05-21","cited_by_count":4,"area":null},{"id":"2203.16225","title":"Exploring ML testing in practice -- Lessons learned from an interactive rapid review with Axis Communications","created":"2022-03-30","cited_by_count":3,"area":null},{"id":"2212.07118","title":"Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines","created":"2022-12-14","cited_by_count":3,"area":null},{"id":"2405.10350","title":"Monitizer: Automating Design and Evaluation of Neural Network Monitors","created":"2024-05-16","cited_by_count":2,"area":null},{"id":"2510.21254","title":"Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systems","created":"2025-10-24","cited_by_count":2,"area":null},{"id":"2204.12402","title":"Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systems","created":"2022-04-26","cited_by_count":1,"area":null},{"id":"2208.11552","title":"CheapET-3: Cost-Efficient Use of Remote DNN Models","created":"2022-08-24","cited_by_count":1,"area":null}],"next_cursor":null}