{"total":14,"rows":[{"id":"2101.10865","title":"On managing vulnerabilities in AI/ML systems","created":"2021-01-22","cited_by_count":5,"area":null},{"id":"2311.17453","title":"Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions","created":"2023-11-29","cited_by_count":4,"area":null},{"id":"2104.02425","title":"The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions","created":"2021-04-06","cited_by_count":3,"area":null},{"id":"2402.06357","title":"The SkipSponge Attack: Sponge Weight Poisoning of Deep Neural Networks","created":"2024-02-09","cited_by_count":3,"area":null},{"id":"2206.14153","title":"Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Research","created":"2022-05-30","cited_by_count":2,"area":null},{"id":"2109.04865","title":"Emerging AI Security Threats for Autonomous Cars -- Case Studies","created":"2021-09-10","cited_by_count":1,"area":null},{"id":"2205.03790","title":"MLSmellHound: A Context-Aware Code Analysis Tool","created":"2022-05-08","cited_by_count":1,"area":null},{"id":"2308.03363","title":"A reading survey on adversarial machine learning: Adversarial attacks and their understanding","created":"2023-08-07","cited_by_count":1,"area":null},{"id":"2101.03989","title":"Technology Readiness Levels for Machine Learning Systems","created":"2021-01-11","cited_by_count":0,"area":null},{"id":"2403.15207","title":"Robust optimization for adversarial learning with finite sample complexity guarantees","created":"2024-03-22","cited_by_count":0,"area":null},{"id":"2411.00222","title":"Protecting Feed-Forward Networks from Adversarial Attacks Using Predictive Coding","created":"2024-10-31","cited_by_count":0,"area":null},{"id":"2606.09414","title":"AI Assurance in UK Defence: Challenges in Operationalising JSP 936","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2606.21488","title":"Robustness Cannot be Reduced to Regularization: Studying Adversarial Training Beyond the Linear Case","created":"2026-06-19","cited_by_count":0,"area":null},{"id":"2606.31589","title":"From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems","created":"2026-06-30","cited_by_count":0,"area":null}],"next_cursor":null}