{"total":26,"rows":[{"id":"2412.20892","title":"Rethinking Aleatoric and Epistemic Uncertainty","created":"2024-12-30","cited_by_count":42,"area":null},{"id":"2406.04306","title":"Improving Uncertainty Estimation through Semantically Diverse Language Generation","created":"2024-06-06","cited_by_count":35,"area":null},{"id":"2311.08309","title":"Introducing an Improved Information-Theoretic Measure of Predictive Uncertainty","created":"2023-11-14","cited_by_count":28,"area":null},{"id":"2402.10727","title":"From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation","created":"2024-02-16","cited_by_count":28,"area":null},{"id":"2410.10786","title":"On Information-Theoretic Measures of Predictive Uncertainty","created":"2024-10-14","cited_by_count":16,"area":null},{"id":"2505.02874","title":"Uncertainty Quantification for Machine Learning in Healthcare: A Survey","created":"2025-05-04","cited_by_count":14,"area":null},{"id":"2510.12040","title":"Uncertainty Quantification for Hallucination Detection in Large Language Models: Foundations, Methodology, and Future Directions","created":"2025-10-14","cited_by_count":11,"area":null},{"id":"2404.10124","title":"Epistemic Uncertainty Quantification For Pre-trained Neural Network","created":"2024-04-15","cited_by_count":10,"area":null},{"id":"2412.15758","title":"Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles","created":"2024-12-20","cited_by_count":8,"area":null},{"id":"2510.02279","title":"Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation","created":"2025-10-02","cited_by_count":7,"area":null},{"id":"2404.05219","title":"Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey","created":"2024-04-08","cited_by_count":6,"area":null},{"id":"2407.14185","title":"Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models","created":"2024-07-19","cited_by_count":2,"area":null},{"id":"2410.13831","title":"The Disparate Benefits of Deep Ensembles","created":"2024-10-17","cited_by_count":2,"area":null},{"id":"2506.05937","title":"Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning","created":"2025-06-06","cited_by_count":2,"area":null},{"id":"2408.03421","title":"Probabilistic Scores of Classifiers, Calibration is not Enough","created":"2024-08-06","cited_by_count":1,"area":null},{"id":"2410.05468","title":"PH-Dropout: Practical Epistemic Uncertainty Quantification for View Synthesis","created":"2024-10-07","cited_by_count":1,"area":null},{"id":"2502.03982","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","created":"2025-02-06","cited_by_count":1,"area":null},{"id":"2504.08414","title":"Adversarial Examples in Environment Perception for Automated Driving (Review)","created":"2025-04-11","cited_by_count":1,"area":null},{"id":"2608.05995","title":"A Ground-Truth Framework for Uncertainty Disentanglement with Posterior Risk","created":"2026-08-06","cited_by_count":1,"area":null},{"id":"2504.13961","title":"CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee","created":"2025-04-17","cited_by_count":0,"area":null},{"id":"2505.08158","title":"Feature Fitted Online Conformal Prediction for Deep Time Series Forecasting Model","created":"2025-05-13","cited_by_count":0,"area":null},{"id":"2507.07460","title":"Objectomaly: Objectness-Aware Refinement for OoD Segmentation with Structural Consistency and Boundary Precision","created":"2025-07-10","cited_by_count":0,"area":null},{"id":"2508.11961","title":"PEdger++: Practical Edge Detection via Assembling Cross Information","created":"2025-08-16","cited_by_count":0,"area":null},{"id":"2509.24492","title":"Guided Uncertainty Learning Using a Post-Hoc Evidential Meta-Model","created":"2025-09-29","cited_by_count":0,"area":null},{"id":"2606.18860","title":"Quantification of Uncertainty with Adversarial Models in Medical Image Segmentation","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2607.28248","title":"Uncertainty quantification for trustworthy deep learning: Methods and measures","created":"2026-07-30","cited_by_count":0,"area":null}],"next_cursor":null}