{"total":22,"rows":[{"id":"2402.04247","title":"Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy","created":"2024-02-06","cited_by_count":49,"area":null},{"id":"2312.00066","title":"Exploring Factors Affecting Pedestrian Crash Severity Using TabNet: A Deep Learning Approach","created":"2023-11-29","cited_by_count":4,"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":"2109.12111","title":"Accurate Remaining Useful Life Prediction with Uncertainty Quantification: a Deep Learning and Nonstationary Gaussian Process Approach","created":"2021-09-23","cited_by_count":2,"area":null},{"id":"2209.03352","title":"A hybrid Bayesian network for medical device risk assessment and management","created":"2022-09-07","cited_by_count":2,"area":null},{"id":"2210.12061","title":"Validation of Composite Systems by Discrepancy Propagation","created":"2022-10-21","cited_by_count":2,"area":null},{"id":"2402.02696","title":"Causal Feature Selection for Responsible Machine Learning","created":"2024-02-05","cited_by_count":2,"area":null},{"id":"2505.22152","title":"Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory","created":"2025-05-28","cited_by_count":2,"area":null},{"id":"2107.03869","title":"Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review","created":"2021-07-08","cited_by_count":1,"area":null},{"id":"2309.03986","title":"Noisy Computing of the $\\mathsf{OR}$ and $\\mathsf{MAX}$ Functions","created":"2023-09-07","cited_by_count":1,"area":null},{"id":"2409.04708","title":"Harnessing physics-informed operators for high-dimensional reliability analysis problems","created":"2024-09-07","cited_by_count":1,"area":null},{"id":"2509.04536","title":"Q-SafeML: Safety Assessment of Quantum Machine Learning via Quantum Distance Metrics","created":"2025-09-04","cited_by_count":1,"area":null},{"id":"2109.11579","title":"Remaining useful life prediction with uncertainty quantification: development of a highly accurate model for rotating machinery","created":"2021-09-23","cited_by_count":0,"area":null},{"id":"2111.14324","title":"Is the Rush to Machine Learning Jeopardizing Safety? Results of a Survey","created":"2021-11-29","cited_by_count":0,"area":null},{"id":"2302.10771","title":"Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load","created":"2023-02-03","cited_by_count":0,"area":null},{"id":"2302.11947","title":"Real-Time Damage Detection in Fiber Lifting Ropes Using Lightweight Convolutional Neural Networks","created":"2023-02-23","cited_by_count":0,"area":null},{"id":"2409.07482","title":"VSLLaVA: a pipeline of large multimodal foundation model for industrial vibration signal analysis","created":"2024-09-03","cited_by_count":0,"area":null},{"id":"2410.04238","title":"Towards the Best Solution for Complex System Reliability: Can Statistics Outperform Machine Learning?","created":"2024-10-05","cited_by_count":0,"area":null},{"id":"2509.18128","title":"Accounting for Uncertainty in Machine Learning Surrogates: A Gauss-Hermite Quadrature Approach to Reliability Analysis","created":"2025-09-11","cited_by_count":0,"area":null},{"id":"2605.08190","title":"Synergistic Simplex: Cooperative Runtime Assurance for Safety-Critical Autonomous Systems","created":"2026-05-05","cited_by_count":0,"area":null},{"id":"2609.16314","title":"Robust Fault Detection in Mechanical Multimodal Time Series via Self-Supervised Cross-Modal Reconstruction","created":"2026-09-14","cited_by_count":0,"area":null},{"id":"2610.17381","title":"Robustly Reliable Learning for Regression","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}