{"total":164,"rows":[{"id":"2107.03342","title":"A Survey of Uncertainty in Deep Neural Networks","created":"2021-07-07","cited_by_count":680,"area":null},{"id":"2204.11423","title":"Trusted Multi-View Classification with Dynamic Evidential Fusion","created":"2022-04-25","cited_by_count":168,"area":null},{"id":"2209.03302","title":"Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?","created":"2022-09-07","cited_by_count":125,"area":null},{"id":"2107.08924","title":"Epistemic Neural Networks","created":"2021-07-19","cited_by_count":120,"area":null},{"id":"2102.08501","title":"DEUP: Direct Epistemic Uncertainty Prediction","created":"2021-02-16","cited_by_count":104,"area":null},{"id":"2107.10161","title":"Evidential Deep Learning for Open Set Action Recognition","created":"2021-07-21","cited_by_count":96,"area":null},{"id":"2109.02123","title":"Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representations","created":"2021-09-05","cited_by_count":82,"area":null},{"id":"2306.02050","title":"Provable Dynamic Fusion for Low-Quality Multimodal Data","created":"2023-06-03","cited_by_count":82,"area":null},{"id":"2302.13425","title":"A Survey on Uncertainty Quantification Methods for Deep Learning","created":"2023-02-26","cited_by_count":81,"area":null},{"id":"2110.14012","title":"Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification","created":"2021-10-26","cited_by_count":80,"area":null},{"id":"2110.03051","title":"Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation","created":"2021-10-06","cited_by_count":71,"area":null},{"id":"2203.06102","title":"Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation","created":"2022-03-11","cited_by_count":66,"area":null},{"id":"2107.00649","title":"On the Practicality of Deterministic Epistemic Uncertainty","created":"2021-07-01","cited_by_count":62,"area":null},{"id":"2303.02045","title":"Uncertainty Estimation by Fisher Information-based Evidential Deep Learning","created":"2023-03-03","cited_by_count":58,"area":null},{"id":"2302.09656","title":"Credal Bayesian Deep Learning","created":"2023-02-19","cited_by_count":45,"area":null},{"id":"2306.04459","title":"Uncertainty in Natural Language Processing: Sources, Quantification, and Applications","created":"2023-06-05","cited_by_count":44,"area":null},{"id":"2301.12736","title":"On Second-Order Scoring Rules for Epistemic Uncertainty Quantification","created":"2023-01-30","cited_by_count":38,"area":null},{"id":"2109.06352","title":"Uncertainty-Aware Machine Translation Evaluation","created":"2021-09-13","cited_by_count":35,"area":null},{"id":"2402.09056","title":"Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?","created":"2024-02-14","cited_by_count":35,"area":null},{"id":"2212.07359","title":"Post-hoc Uncertainty Learning using a Dirichlet Meta-Model","created":"2022-12-14","cited_by_count":31,"area":null},{"id":"2211.00385","title":"Behavioral Intention Prediction in Driving Scenes: A Survey","created":"2022-11-01","cited_by_count":30,"area":null},{"id":"2306.11113","title":"Learn to Accumulate Evidence from All Training Samples: Theory and Practice","created":"2023-06-19","cited_by_count":29,"area":null},{"id":"2503.04207","title":"Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior","created":"2025-03-06","cited_by_count":29,"area":null},{"id":"2107.08976","title":"OODformer: Out-Of-Distribution Detection Transformer","created":"2021-07-19","cited_by_count":27,"area":null},{"id":"2206.01558","title":"Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning","created":"2022-06-03","cited_by_count":27,"area":null},{"id":"2208.11113","title":"Towards Open Set Video Anomaly Detection","created":"2022-08-23","cited_by_count":26,"area":null},{"id":"2409.04720","title":"A Comprehensive Survey on Evidential Deep Learning and Its Applications","created":"2024-09-07","cited_by_count":25,"area":null},{"id":"2211.08701","title":"Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction","created":"2022-11-16","cited_by_count":22,"area":null},{"id":"2406.18580","title":"Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models","created":"2024-06-05","cited_by_count":22,"area":"diffusion"},{"id":"2307.05772","title":"Random-Set Neural Networks (RS-NN)","created":"2023-07-11","cited_by_count":19,"area":null},{"id":"2506.09800","title":"Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving","created":"2025-06-11","cited_by_count":19,"area":"driving"},{"id":"2402.06160","title":"Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?","created":"2024-02-09","cited_by_count":18,"area":null},{"id":"2107.08785","title":"On Out-of-distribution Detection with Energy-based Models","created":"2021-07-03","cited_by_count":17,"area":null},{"id":"2210.05959","title":"JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks","created":"2022-10-12","cited_by_count":15,"area":null},{"id":"2410.00393","title":"Revisiting Essential and Nonessential Settings of Evidential Deep Learning","created":"2024-10-01","cited_by_count":15,"area":null},{"id":"2404.12667","title":"Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models","created":"2024-04-19","cited_by_count":14,"area":"diffusion"},{"id":"2506.13083","title":"Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach","created":"2025-06-16","cited_by_count":14,"area":null},{"id":"2105.04471","title":"Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions","created":"2021-05-10","cited_by_count":13,"area":null},{"id":"2206.10691","title":"Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective","created":"2022-06-21","cited_by_count":13,"area":null},{"id":"2405.15047","title":"Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification","created":"2024-05-23","cited_by_count":13,"area":null},{"id":"2302.06495","title":"Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts","created":"2023-02-13","cited_by_count":12,"area":null},{"id":"2306.14916","title":"Uncertainty Estimation for Molecules: Desiderata and Methods","created":"2023-06-20","cited_by_count":11,"area":null},{"id":"2404.10980","title":"Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty","created":"2024-04-17","cited_by_count":11,"area":null},{"id":"2405.03164","title":"The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning","created":"2024-05-06","cited_by_count":11,"area":null},{"id":"2311.08726","title":"Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission","created":"2023-11-15","cited_by_count":10,"area":null},{"id":"2510.18322","title":"Uncertainty Estimation by Flexible Evidential Deep Learning","created":"2025-10-21","cited_by_count":10,"area":null},{"id":"2305.16966","title":"Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection","created":"2023-05-26","cited_by_count":9,"area":null},{"id":"2505.23506","title":"Position: Epistemic uncertainty estimation methods are fundamentally incomplete","created":"2025-05-29","cited_by_count":9,"area":null},{"id":"2110.02609","title":"Deep Classifiers with Label Noise Modeling and Distance Awareness","created":"2021-10-06","cited_by_count":8,"area":null},{"id":"2312.11230","title":"Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks","created":"2023-12-18","cited_by_count":8,"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":"2505.04950","title":"Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'","created":"2025-05-08","cited_by_count":8,"area":null},{"id":"2512.05526","title":"Credal and Interval Deep Evidential Classifications","created":"2025-12-05","cited_by_count":8,"area":null},{"id":"2211.16466","title":"Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation","created":"2022-11-29","cited_by_count":7,"area":null},{"id":"2302.13875","title":"Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts","created":"2023-02-27","cited_by_count":7,"area":null},{"id":"2303.05796","title":"Training, Architecture, and Prior for Deterministic Uncertainty Methods","created":"2023-03-10","cited_by_count":7,"area":null},{"id":"2305.17854","title":"E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition","created":"2023-05-29","cited_by_count":7,"area":null},{"id":"2308.09065","title":"Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression","created":"2023-08-17","cited_by_count":7,"area":null},{"id":"2311.02719","title":"Uncertainty Estimation for Safety-critical Scene Segmentation via Fine-grained Reward Maximization","created":"2023-11-05","cited_by_count":7,"area":null},{"id":"2311.06481","title":"Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning","created":"2023-11-11","cited_by_count":7,"area":null},{"id":"2403.05600","title":"Density-Regression: Efficient and Distance-Aware Deep Regressor for Uncertainty Estimation under Distribution Shifts","created":"2024-03-07","cited_by_count":7,"area":null},{"id":"2405.11337","title":"A Unified Approach Towards Active Learning and Out-of-Distribution Detection","created":"2024-05-18","cited_by_count":7,"area":null},{"id":"2503.02491","title":"Joint Out-of-Distribution Filtering and Data Discovery Active Learning","created":"2025-03-04","cited_by_count":7,"area":null},{"id":"2504.04841","title":"Prior2Former -- Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation","created":"2025-04-07","cited_by_count":7,"area":null},{"id":"2602.08470","title":"Learning Credal Ensembles via Distributionally Robust Optimization","created":"2026-02-09","cited_by_count":7,"area":null},{"id":"2206.05675","title":"A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning","created":"2022-06-12","cited_by_count":6,"area":null},{"id":"2302.08669","title":"Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories","created":"2023-02-17","cited_by_count":6,"area":"wm"},{"id":"2309.07403","title":"Flexible Visual Recognition by Evidential Modeling of Confusion and Ignorance","created":"2023-09-14","cited_by_count":6,"area":null},{"id":"2503.20011","title":"Hyperdimensional Uncertainty Quantification for Multimodal Uncertainty Fusion in Autonomous Vehicles Perception","created":"2025-03-25","cited_by_count":6,"area":null},{"id":"2506.13265","title":"Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning","created":"2025-06-16","cited_by_count":6,"area":null},{"id":"2203.08549","title":"Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space","created":"2022-03-16","cited_by_count":5,"area":null},{"id":"2311.05795","title":"Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization","created":"2023-11-10","cited_by_count":5,"area":null},{"id":"2501.18268","title":"Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition","created":"2025-01-30","cited_by_count":5,"area":null},{"id":"2505.02277","title":"Epistemic Wrapping for Uncertainty Quantification","created":"2025-05-04","cited_by_count":5,"area":null},{"id":"2506.00594","title":"Graph Evidential Learning for Anomaly Detection","created":"2025-05-31","cited_by_count":5,"area":null},{"id":"2106.12070","title":"Towards Consistent Predictive Confidence through Fitted Ensembles","created":"2021-06-22","cited_by_count":4,"area":null},{"id":"2302.02628","title":"Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness","created":"2023-02-06","cited_by_count":4,"area":null},{"id":"2306.01452","title":"dugMatting: Decomposed-Uncertainty-Guided Matting","created":"2023-06-02","cited_by_count":4,"area":null},{"id":"2404.12599","title":"QUTE: Quantifying Uncertainty in TinyML with Early-exit-assisted ensembles for model-monitoring","created":"2024-04-19","cited_by_count":4,"area":"inference"},{"id":"2405.04405","title":"Weakly-Supervised Residual Evidential Learning for Multi-Instance Uncertainty Estimation","created":"2024-05-07","cited_by_count":4,"area":null},{"id":"2405.09536","title":"Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning","created":"2024-05-15","cited_by_count":4,"area":null},{"id":"2510.07206","title":"EigenScore: OOD Detection using Covariance in Diffusion Models","created":"2025-10-08","cited_by_count":4,"area":"diffusion"},{"id":"2209.08418","title":"Sample-based Uncertainty Quantification with a Single Deterministic Neural Network","created":"2022-09-17","cited_by_count":3,"area":null},{"id":"2406.04041","title":"Linear Opinion Pooling for Uncertainty Quantification on Graphs","created":"2024-06-06","cited_by_count":3,"area":null},{"id":"2407.15161","title":"FFHFlow: Diverse and Uncertainty-Aware Dexterous Grasp Generation via Flow Variational Inference","created":"2024-07-21","cited_by_count":3,"area":null},{"id":"2601.08064","title":"Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations","created":"2026-01-12","cited_by_count":3,"area":null},{"id":"2605.27668","title":"Aligning LLMs with Human Uncertainty: A Beta-Bernoulli Calibrator for LLM Forecasting","created":"2026-05-26","cited_by_count":3,"area":null},{"id":"2608.10398","title":"ELVAE: Evidential Learning-Based Variational Autoencoder for Uncertainty-Aware Generation","created":"2026-08-11","cited_by_count":3,"area":null},{"id":"2109.03596","title":"Learn2Agree: Fitting with Multiple Annotators without Objective Ground Truth","created":"2021-09-08","cited_by_count":2,"area":null},{"id":"2211.01999","title":"Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS","created":"2022-11-03","cited_by_count":2,"area":null},{"id":"2307.01317","title":"Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly","created":"2023-07-03","cited_by_count":2,"area":null},{"id":"2310.12663","title":"Knowledge from Uncertainty in Evidential Deep Learning","created":"2023-10-19","cited_by_count":2,"area":null},{"id":"2310.16587","title":"Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations","created":"2023-10-25","cited_by_count":2,"area":null},{"id":"2402.12664","title":"Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods","created":"2024-02-20","cited_by_count":2,"area":null},{"id":"2405.11139","title":"RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts","created":"2024-05-18","cited_by_count":2,"area":"offlinerl"},{"id":"2405.13699","title":"Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior","created":"2024-05-22","cited_by_count":2,"area":null},{"id":"2405.20986","title":"Predictive Uncertainty Quantification for Bird's Eye View Segmentation: A Benchmark and Novel Loss Function","created":"2024-05-31","cited_by_count":2,"area":null},{"id":"2406.12082","title":"Uncertainty modeling for fine-tuned implicit functions","created":"2024-06-17","cited_by_count":2,"area":null},{"id":"2407.18428","title":"Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift","created":"2024-07-25","cited_by_count":2,"area":null},{"id":"2411.17917","title":"DECODE: Domain-aware Continual Domain Expansion for Motion Prediction","created":"2024-11-26","cited_by_count":2,"area":null}],"next_cursor":"100"}