{"total":82,"rows":[{"id":"2107.03342","title":"A Survey of Uncertainty in Deep Neural Networks","created":"2021-07-07","cited_by_count":680,"area":null},{"id":"2107.08924","title":"Epistemic Neural Networks","created":"2021-07-19","cited_by_count":120,"area":null},{"id":"2207.07411","title":"Plex: Towards Reliability using Pretrained Large Model Extensions","created":"2022-07-15","cited_by_count":102,"area":null},{"id":"2107.07455","title":"Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks","created":"2021-07-15","cited_by_count":101,"area":null},{"id":"2108.00106","title":"Soft Calibration Objectives for Neural Networks","created":"2021-07-30","cited_by_count":92,"area":null},{"id":"2402.19460","title":"Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks","created":"2024-02-29","cited_by_count":78,"area":null},{"id":"2202.06985","title":"Deep Ensembles Work, But Are They Necessary?","created":"2022-02-14","cited_by_count":63,"area":null},{"id":"2211.06291","title":"Do Bayesian Neural Networks Need To Be Fully Stochastic?","created":"2022-11-11","cited_by_count":63,"area":null},{"id":"2109.10254","title":"Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification","created":"2021-09-21","cited_by_count":57,"area":null},{"id":"2205.00403","title":"A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness","created":"2022-05-01","cited_by_count":49,"area":null},{"id":"2211.12717","title":"Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks","created":"2022-11-23","cited_by_count":49,"area":null},{"id":"2207.06214","title":"Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation","created":"2022-07-13","cited_by_count":43,"area":null},{"id":"2302.06235","title":"A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models","created":"2023-02-13","cited_by_count":39,"area":null},{"id":"2302.11874","title":"What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers","created":"2023-02-23","cited_by_count":35,"area":null},{"id":"2304.08653","title":"On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study","created":"2023-04-17","cited_by_count":25,"area":null},{"id":"2206.15407","title":"Shifts 2.0: Extending The Dataset of Real Distributional Shifts","created":"2022-06-30","cited_by_count":24,"area":null},{"id":"2110.04629","title":"The Neural Testbed: Evaluating Joint Predictions","created":"2021-10-09","cited_by_count":22,"area":null},{"id":"2210.12928","title":"GFlowOut: Dropout with Generative Flow Networks","created":"2022-10-24","cited_by_count":22,"area":null},{"id":"2207.07235","title":"Single Model Uncertainty Estimation via Stochastic Data Centering","created":"2022-07-14","cited_by_count":20,"area":null},{"id":"2306.12306","title":"Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift","created":"2023-06-21","cited_by_count":20,"area":null},{"id":"2312.17162","title":"Function-Space Regularization in Neural Networks: A Probabilistic Perspective","created":"2023-12-28","cited_by_count":20,"area":null},{"id":"2402.08733","title":"Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs","created":"2024-02-13","cited_by_count":19,"area":null},{"id":"2208.12904","title":"A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives","created":"2022-08-27","cited_by_count":18,"area":null},{"id":"2206.05259","title":"Is Self-Supervised Learning More Robust Than Supervised Learning?","created":"2022-06-10","cited_by_count":17,"area":null},{"id":"2307.03810","title":"URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates","created":"2023-07-07","cited_by_count":17,"area":null},{"id":"2409.20534","title":"End-to-End Conformal Calibration for Optimization Under Uncertainty","created":"2024-09-30","cited_by_count":15,"area":null},{"id":"2302.04019","title":"Fortuna: A Library for Uncertainty Quantification in Deep Learning","created":"2023-02-08","cited_by_count":14,"area":null},{"id":"2111.08190","title":"Learning Augmentation Distributions using Transformed Risk Minimization","created":"2021-11-16","cited_by_count":13,"area":null},{"id":"2202.11782","title":"Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworks","created":"2022-02-23","cited_by_count":13,"area":null},{"id":"2510.20369","title":"Ask a Strong LLM Judge when Your Reward Model is Uncertain","created":"2025-10-23","cited_by_count":13,"area":"alignment"},{"id":"2210.03102","title":"Ambiguous Images With Human Judgments for Robust Visual Event Classification","created":"2022-10-06","cited_by_count":12,"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":"2301.12860","title":"Massively Scaling Heteroscedastic Classifiers","created":"2023-01-30","cited_by_count":11,"area":null},{"id":"2306.10060","title":"MUBen: Benchmarking the Uncertainty of Molecular Representation Models","created":"2023-06-14","cited_by_count":11,"area":null},{"id":"2202.13509","title":"Evaluating High-Order Predictive Distributions in Deep Learning","created":"2022-02-28","cited_by_count":10,"area":null},{"id":"2212.01574","title":"Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS","created":"2022-12-03","cited_by_count":10,"area":null},{"id":"2303.01806","title":"When does Privileged Information Explain Away Label Noise?","created":"2023-03-03","cited_by_count":10,"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":"2212.04412","title":"Task Bias in Vision-Language Models","created":"2022-12-08","cited_by_count":8,"area":"mllm"},{"id":"2301.05983","title":"On the role of Model Uncertainties in Bayesian Optimization","created":"2023-01-14","cited_by_count":8,"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":"2206.10566","title":"Ensembling over Classifiers: a Bias-Variance Perspective","created":"2022-06-21","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":"2206.00794","title":"Sequential Bayesian Neural Subnetwork Ensembles","created":"2022-06-01","cited_by_count":5,"area":null},{"id":"2211.02578","title":"Data Models for Dataset Drift Controls in Machine Learning With Optical Images","created":"2022-11-04","cited_by_count":5,"area":null},{"id":"2310.06643","title":"Implicit Variational Inference for High-Dimensional Posteriors","created":"2023-10-10","cited_by_count":5,"area":null},{"id":"2406.09864","title":"LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal Data","created":"2024-06-14","cited_by_count":5,"area":null},{"id":"2109.09048","title":"A framework for benchmarking uncertainty in deep regression","created":"2021-09-10","cited_by_count":4,"area":null},{"id":"2307.07753","title":"Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks","created":"2023-07-15","cited_by_count":4,"area":null},{"id":"2403.10182","title":"Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification","created":"2024-03-15","cited_by_count":4,"area":null},{"id":"2511.10282","title":"Torch-Uncertainty: A Deep Learning Framework for Uncertainty Quantification","created":"2025-11-13","cited_by_count":4,"area":null},{"id":"2301.04414","title":"How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?","created":"2023-01-11","cited_by_count":3,"area":null},{"id":"2402.18888","title":"Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos","created":"2024-02-29","cited_by_count":3,"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":"2410.03390","title":"Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning","created":"2024-10-04","cited_by_count":3,"area":null},{"id":"2510.08389","title":"Revisiting Hallucination Detection with Effective Rank-based Uncertainty","created":"2025-10-09","cited_by_count":3,"area":null},{"id":"2207.13341","title":"Towards Clear Expectations for Uncertainty Estimation","created":"2022-07-27","cited_by_count":2,"area":null},{"id":"2210.10897","title":"Window-Based Distribution Shift Detection for Deep Neural Networks","created":"2022-10-19","cited_by_count":2,"area":null},{"id":"2211.10193","title":"Layer-Stack Temperature Scaling","created":"2022-11-18","cited_by_count":2,"area":null},{"id":"2310.06600","title":"Pi-DUAL: Using Privileged Information to Distinguish Clean from Noisy Labels","created":"2023-10-10","cited_by_count":2,"area":null},{"id":"2510.20242","title":"What Does It Take to Build a Performant Selective Classifier?","created":"2025-10-23","cited_by_count":2,"area":null},{"id":"2605.30188","title":"CalArena: A Large-Scale Post-Hoc Calibration Benchmark","created":"2026-05-28","cited_by_count":2,"area":null},{"id":"2205.11151","title":"Split personalities in Bayesian Neural Networks: the case for full marginalisation","created":"2022-05-23","cited_by_count":1,"area":null},{"id":"2206.02152","title":"Which models are innately best at uncertainty estimation?","created":"2022-06-05","cited_by_count":1,"area":null},{"id":"2208.01705","title":"Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry","created":"2022-08-02","cited_by_count":1,"area":null},{"id":"2212.12411","title":"Benchmark for Uncertainty & Robustness in Self-Supervised Learning","created":"2022-12-23","cited_by_count":1,"area":null},{"id":"2302.04525","title":"An Epistemic and Aleatoric Decomposition of Arbitrariness to Constrain the Set of Good Models","created":"2023-02-09","cited_by_count":1,"area":null},{"id":"2305.10625","title":"Measuring and Mitigating Local Instability in Deep Neural Networks","created":"2023-05-18","cited_by_count":1,"area":null},{"id":"2308.06129","title":"Uncertainty Quantification for Image-based Traffic Prediction across Cities","created":"2023-08-11","cited_by_count":1,"area":null},{"id":"2401.08830","title":"Stochastic Subnetwork Annealing: A Regularization Technique for Fine Tuning Pruned Subnetworks","created":"2024-01-16","cited_by_count":1,"area":null},{"id":"2605.05592","title":"When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2112.01537","title":"Improving mathematical questioning in teacher training","created":"2021-12-02","cited_by_count":0,"area":null},{"id":"2211.13402","title":"MP-GELU Bayesian Neural Networks: Moment Propagation by GELU Nonlinearity","created":"2022-11-24","cited_by_count":0,"area":null},{"id":"2308.00231","title":"Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks","created":"2023-08-01","cited_by_count":0,"area":null},{"id":"2308.05903","title":"Comparing the quality of neural network uncertainty estimates for classification problems","created":"2023-08-11","cited_by_count":0,"area":null},{"id":"2309.13587","title":"Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Shape Reconstruction","created":"2023-09-24","cited_by_count":0,"area":null},{"id":"2310.01202","title":"Unified Uncertainty Calibration","created":"2023-10-02","cited_by_count":0,"area":null},{"id":"2406.05469","title":"On Uniform, Bayesian, and PAC-Bayesian Deep Ensembles","created":"2024-06-08","cited_by_count":0,"area":null},{"id":"2510.03162","title":"Calibrated Uncertainty Sampling for Active Learning","created":"2025-10-03","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},{"id":"2610.04239","title":"Conformal Prediction with Paraphrase-Aware Scoring for LLM Uncertainty Quantification","created":"2026-10-03","cited_by_count":0,"area":null},{"id":"2610.09498","title":"SpatialUQ: Post-Hoc Uncertainty Quantification from Spatial Consistency in Black-Box Vision Models","created":"2026-10-07","cited_by_count":0,"area":null}],"next_cursor":null}