{"total":154,"rows":[{"id":"2107.03342","title":"A Survey of Uncertainty in Deep Neural Networks","created":"2021-07-07","cited_by_count":680,"area":null},{"id":"2104.14421","title":"What Are Bayesian Neural Network Posteriors Really Like?","created":"2021-04-29","cited_by_count":308,"area":null},{"id":"2102.06571","title":"Bayesian Neural Network Priors Revisited","created":"2021-02-12","cited_by_count":121,"area":null},{"id":"2107.08924","title":"Epistemic Neural Networks","created":"2021-07-19","cited_by_count":120,"area":null},{"id":"2105.06868","title":"Priors in Bayesian Deep Learning: A Review","created":"2021-05-14","cited_by_count":101,"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":"2103.13084","title":"Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases","created":"2021-03-24","cited_by_count":77,"area":null},{"id":"2210.03895","title":"ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints","created":"2022-10-08","cited_by_count":48,"area":null},{"id":"2103.01133","title":"Posterior Meta-Replay for Continual Learning","created":"2021-03-01","cited_by_count":45,"area":null},{"id":"2103.14835","title":"LiBRe: A Practical Bayesian Approach to Adversarial Detection","created":"2021-03-27","cited_by_count":40,"area":null},{"id":"2102.06559","title":"Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations","created":"2021-02-12","cited_by_count":38,"area":null},{"id":"2110.04286","title":"Is MC Dropout Bayesian?","created":"2021-10-08","cited_by_count":35,"area":null},{"id":"2305.15027","title":"A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods","created":"2023-05-24","cited_by_count":32,"area":null},{"id":"2204.07415","title":"Universal approximation property of invertible neural networks","created":"2022-04-15","cited_by_count":30,"area":null},{"id":"2212.03281","title":"Copula Conformal Prediction for Multi-step Time Series Forecasting","created":"2022-12-06","cited_by_count":29,"area":null},{"id":"2105.04030","title":"A Bit More Bayesian: Domain-Invariant Learning with Uncertainty","created":"2021-05-09","cited_by_count":27,"area":null},{"id":"2309.16314","title":"A Primer on Bayesian Neural Networks: Review and Debates","created":"2023-09-28","cited_by_count":27,"area":null},{"id":"2103.04181","title":"Contextual Dropout: An Efficient Sample-Dependent Dropout Module","created":"2021-03-06","cited_by_count":26,"area":null},{"id":"2205.09940","title":"Conformal Prediction with Temporal Quantile Adjustments","created":"2022-05-20","cited_by_count":25,"area":null},{"id":"2103.10919","title":"Robustness via Cross-Domain Ensembles","created":"2021-03-19","cited_by_count":23,"area":null},{"id":"2107.07114","title":"Uncertainty-Aware Reliable Text Classification","created":"2021-07-15","cited_by_count":23,"area":null},{"id":"2111.12506","title":"Generalized Normalizing Flows via Markov Chains","created":"2021-11-24","cited_by_count":23,"area":null},{"id":"2402.14335","title":"HyperFast: Instant Classification for Tabular Data","created":"2024-02-22","cited_by_count":22,"area":null},{"id":"2308.12965","title":"POCO: 3D Pose and Shape Estimation with Confidence","created":"2023-08-24","cited_by_count":21,"area":null},{"id":"2402.03008","title":"Diffusive Gibbs Sampling","created":"2024-02-05","cited_by_count":21,"area":null},{"id":"2102.12959","title":"Bayesian OOD detection with aleatoric uncertainty and outlier exposure","created":"2021-02-24","cited_by_count":16,"area":null},{"id":"2105.14594","title":"Sparse Uncertainty Representation in Deep Learning with Inducing Weights","created":"2021-05-30","cited_by_count":16,"area":null},{"id":"2106.03904","title":"When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting","created":"2021-06-07","cited_by_count":16,"area":null},{"id":"2309.15533","title":"Uncertainty Quantification via Neural Posterior Principal Components","created":"2023-09-27","cited_by_count":16,"area":null},{"id":"2101.04117","title":"A Bayesian neural network predicts the dissolution of compact planetary systems","created":"2021-01-11","cited_by_count":15,"area":null},{"id":"2104.11667","title":"Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure","created":"2021-04-23","cited_by_count":15,"area":null},{"id":"2205.10041","title":"Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks","created":"2022-05-20","cited_by_count":14,"area":null},{"id":"2110.04020","title":"Pathologies in priors and inference for Bayesian transformers","created":"2021-10-08","cited_by_count":13,"area":null},{"id":"2404.13056","title":"Variational Bayesian Optimal Experimental Design with Normalizing Flows","created":"2024-04-08","cited_by_count":13,"area":null},{"id":"2111.11629","title":"Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation","created":"2021-11-23","cited_by_count":12,"area":null},{"id":"2405.16475","title":"Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models","created":"2024-05-26","cited_by_count":12,"area":null},{"id":"2209.03580","title":"Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey","created":"2022-09-08","cited_by_count":11,"area":null},{"id":"2309.10852","title":"Using AI Uncertainty Quantification to Improve Human Decision-Making","created":"2023-09-19","cited_by_count":11,"area":null},{"id":"2410.24054","title":"EigenVI: score-based variational inference with orthogonal function expansions","created":"2024-10-31","cited_by_count":11,"area":null},{"id":"2107.12248","title":"Are Bayesian neural networks intrinsically good at out-of-distribution detection?","created":"2021-07-26","cited_by_count":10,"area":null},{"id":"2302.02713","title":"Flat Seeking Bayesian Neural Networks","created":"2023-02-06","cited_by_count":10,"area":null},{"id":"2304.08309","title":"Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization","created":"2023-04-17","cited_by_count":10,"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":"2205.12940","title":"Conformal Prediction Intervals with Temporal Dependence","created":"2022-05-25","cited_by_count":9,"area":null},{"id":"2402.01344","title":"Monotone, Bi-Lipschitz, and Polyak-Lojasiewicz Networks","created":"2024-02-02","cited_by_count":9,"area":null},{"id":"2206.01319","title":"Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling","created":"2022-06-02","cited_by_count":8,"area":null},{"id":"2206.02435","title":"Tackling covariate shift with node-based Bayesian neural networks","created":"2022-06-06","cited_by_count":8,"area":null},{"id":"2210.07745","title":"Confidence estimation of classification based on the distribution of the neural network output layer","created":"2022-10-14","cited_by_count":8,"area":null},{"id":"2304.04824","title":"Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning","created":"2023-04-10","cited_by_count":8,"area":null},{"id":"2102.07927","title":"Structured Dropout Variational Inference for Bayesian Neural Networks","created":"2021-02-16","cited_by_count":7,"area":null},{"id":"2402.08182","title":"Variational Continual Test-Time Adaptation","created":"2024-02-13","cited_by_count":7,"area":null},{"id":"2406.16540","title":"Improving robustness to corruptions with multiplicative weight perturbations","created":"2024-06-24","cited_by_count":7,"area":null},{"id":"2102.01586","title":"U-LanD: Uncertainty-Driven Video Landmark Detection","created":"2021-02-02","cited_by_count":6,"area":null},{"id":"2107.12542","title":"Energy-based Unknown Intent Detection with Data Manipulation","created":"2021-07-27","cited_by_count":6,"area":null},{"id":"2110.00276","title":"TyXe: Pyro-based Bayesian neural nets for Pytorch","created":"2021-10-01","cited_by_count":6,"area":null},{"id":"2110.06020","title":"On out-of-distribution detection with Bayesian neural networks","created":"2021-10-12","cited_by_count":6,"area":null},{"id":"2106.11641","title":"Confidence-Aware Learning for Camouflaged Object Detection","created":"2021-06-22","cited_by_count":5,"area":null},{"id":"2109.12869","title":"Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks","created":"2021-09-27","cited_by_count":5,"area":null},{"id":"2207.03859","title":"Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study","created":"2022-07-08","cited_by_count":5,"area":null},{"id":"2211.15211","title":"What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems","created":"2022-11-28","cited_by_count":5,"area":null},{"id":"2212.08123","title":"Bayesian posterior approximation with stochastic ensembles","created":"2022-12-15","cited_by_count":5,"area":null},{"id":"2303.01577","title":"DeepLens: Interactive Out-of-distribution Data Detection in NLP Models","created":"2023-03-02","cited_by_count":5,"area":null},{"id":"2305.00934","title":"Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty","created":"2023-05-01","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":"2406.14838","title":"Bayesian neural networks for predicting uncertainty in full-field material response","created":"2024-06-21","cited_by_count":5,"area":null},{"id":"2410.04196","title":"Improving Generalization with Flat Hilbert Bayesian Inference","created":"2024-10-05","cited_by_count":5,"area":null},{"id":"2501.18665","title":"BARNN: A Bayesian Autoregressive and Recurrent Neural Network","created":"2025-01-30","cited_by_count":5,"area":null},{"id":"2101.11353","title":"Variational Nested Dropout","created":"2021-01-27","cited_by_count":4,"area":null},{"id":"2106.06033","title":"Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties","created":"2021-06-10","cited_by_count":4,"area":null},{"id":"2111.07716","title":"Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration","created":"2021-11-15","cited_by_count":4,"area":null},{"id":"2201.05938","title":"GradTail: Learning Long-Tailed Data Using Gradient-based Sample Weighting","created":"2022-01-16","cited_by_count":4,"area":null},{"id":"2205.03109","title":"Controlled Dropout for Uncertainty Estimation","created":"2022-05-06","cited_by_count":4,"area":null},{"id":"2301.04421","title":"Failure Detection for Motion Prediction of Autonomous Driving: An Uncertainty Perspective","created":"2023-01-11","cited_by_count":4,"area":null},{"id":"2504.18346","title":"Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review","created":"2025-04-25","cited_by_count":4,"area":null},{"id":"2509.25017","title":"Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting","created":"2025-09-29","cited_by_count":4,"area":null},{"id":"2104.12953","title":"Exploring Uncertainty in Deep Learning for Construction of Prediction Intervals","created":"2021-04-27","cited_by_count":3,"area":null},{"id":"2107.01248","title":"Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints","created":"2021-07-02","cited_by_count":3,"area":null},{"id":"2111.10722","title":"A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel","created":"2021-11-21","cited_by_count":3,"area":null},{"id":"2112.02622","title":"Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting","created":"2021-12-05","cited_by_count":3,"area":null},{"id":"2201.05890","title":"Robust uncertainty estimates with out-of-distribution pseudo-inputs training","created":"2022-01-15","cited_by_count":3,"area":null},{"id":"2202.00792","title":"AdaAnn: Adaptive Annealing Scheduler for Probability Density Approximation","created":"2022-02-01","cited_by_count":3,"area":null},{"id":"2202.05650","title":"Bernstein Flows for Flexible Posteriors in Variational Bayes","created":"2022-02-11","cited_by_count":3,"area":null},{"id":"2203.17089","title":"Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines","created":"2022-03-31","cited_by_count":3,"area":null},{"id":"2206.05194","title":"Learning the Space of Deep Models","created":"2022-06-10","cited_by_count":3,"area":null},{"id":"2206.06779","title":"Benchmarking Bayesian neural networks and evaluation metrics for regression tasks","created":"2022-06-08","cited_by_count":3,"area":null},{"id":"2310.02913","title":"ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering","created":"2023-10-04","cited_by_count":3,"area":null},{"id":"2312.07804","title":"Uncertainty Visualization via Low-Dimensional Posterior Projections","created":"2023-12-12","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":"2505.11738","title":"Automated Real-time Assessment of Intracranial Hemorrhage Detection AI Using an Ensembled Monitoring Model (EMM)","created":"2025-05-16","cited_by_count":3,"area":null},{"id":"2506.07247","title":"Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models","created":"2025-06-08","cited_by_count":3,"area":null},{"id":"2103.08497","title":"Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise","created":"2021-03-15","cited_by_count":2,"area":null},{"id":"2111.11763","title":"Uncertainty estimation under model misspecification in neural network regression","created":"2021-11-23","cited_by_count":2,"area":null},{"id":"2202.02906","title":"Universality of parametric Coupling Flows over parametric diffeomorphisms","created":"2022-02-07","cited_by_count":2,"area":null},{"id":"2203.00512","title":"A Deep Bayesian Neural Network for Cardiac Arrhythmia Classification with Rejection from ECG Recordings","created":"2022-02-26","cited_by_count":2,"area":null},{"id":"2204.00382","title":"Autoencoder Attractors for Uncertainty Estimation","created":"2022-04-01","cited_by_count":2,"area":null},{"id":"2301.03991","title":"Constraining cosmological parameters from N-body simulations with Variational Bayesian Neural Networks","created":"2023-01-09","cited_by_count":2,"area":null},{"id":"2305.03395","title":"Sparsifying Bayesian neural networks with latent binary variables and normalizing flows","created":"2023-05-05","cited_by_count":2,"area":null},{"id":"2308.08461","title":"CDR: Conservative Doubly Robust Learning for Debiased Recommendation","created":"2023-08-13","cited_by_count":2,"area":null},{"id":"2308.09367","title":"On the Approximation of Bi-Lipschitz Maps by Invertible Neural Networks","created":"2023-08-18","cited_by_count":2,"area":null},{"id":"2407.13929","title":"Unmasking Social Bots: How Confident Are We?","created":"2024-07-18","cited_by_count":2,"area":null}],"next_cursor":"100"}