{"total":34,"rows":[{"id":"2305.11391","title":"A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation","created":"2023-05-19","cited_by_count":93,"area":null},{"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":"2211.16158","title":"Out-Of-Distribution Detection Is Not All You Need","created":"2022-11-29","cited_by_count":23,"area":null},{"id":"2212.14268","title":"Detection of out-of-distribution samples using binary neuron activation patterns","created":"2022-12-29","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":"2208.14660","title":"Unifying Evaluation of Machine Learning Safety Monitors","created":"2022-08-31","cited_by_count":10,"area":null},{"id":"2308.02594","title":"SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents","created":"2023-08-03","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":"2510.10238","title":"The Achilles' Heel of LLMs: How Altering a Handful of Neurons Can Cripple Language Abilities","created":"2025-10-11","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":"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":"2310.20349","title":"A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks","created":"2023-10-31","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":"2103.15456","title":"Monitoring Object Detection Abnormalities via Data-Label and Post-Algorithm Abstractions","created":"2021-03-29","cited_by_count":4,"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":"2301.13476","title":"An investigation of challenges encountered when specifying training data and runtime monitors for safety critical ML applications","created":"2023-01-31","cited_by_count":4,"area":null},{"id":"2310.14211","title":"LUNA: A Model-Based Universal Analysis Framework for Large Language Models","created":"2023-10-22","cited_by_count":4,"area":null},{"id":"2410.02384","title":"Unveiling AI's Blind Spots: An Oracle for In-Domain, Out-of-Domain, and Adversarial Errors","created":"2024-10-03","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":"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":"2503.07330","title":"Revisiting Out-of-Distribution Detection in Real-time Object Detection: From Benchmark Pitfalls to a New Mitigation Paradigm","created":"2025-03-10","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":"2310.03999","title":"Runtime Monitoring DNN-Based Perception","created":"2023-10-06","cited_by_count":1,"area":null},{"id":"2506.23703","title":"A New Perspective On AI Safety Through Control Theory Methodologies","created":"2025-06-30","cited_by_count":1,"area":null},{"id":"2201.10436","title":"Safe AI -- How is this Possible?","created":"2022-01-25","cited_by_count":0,"area":null},{"id":"2307.04533","title":"Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework","created":"2023-07-10","cited_by_count":0,"area":null},{"id":"2307.16433","title":"Detecting Out-of-distribution Objects Using Neuron Activation Patterns","created":"2023-07-31","cited_by_count":0,"area":null},{"id":"2412.14020","title":"Landscape of AI safety concerns -- A methodology to support safety assurance for AI-based autonomous systems","created":"2024-12-18","cited_by_count":0,"area":null},{"id":"2505.14407","title":"Explaining Unreliable Perception in Automated Driving: A Fuzzy-based Monitoring Approach","created":"2025-05-20","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":"2604.26411","title":"Unifying Runtime Monitoring Approaches for Safety-Critical Machine Learning: Application to Vision-Based Landing","created":"2026-04-29","cited_by_count":0,"area":null},{"id":"2609.13514","title":"Operational Range Bounding in Spectroscopy: A Safety Cage Framework for Machine Learning Models","created":"2026-09-11","cited_by_count":0,"area":null}],"next_cursor":null}