{"total":18,"rows":[{"id":"2210.04714","title":"Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis","created":"2022-10-10","cited_by_count":104,"area":null},{"id":"2105.11982","title":"Quantifying Uncertainty in Deep Spatiotemporal Forecasting","created":"2021-05-25","cited_by_count":48,"area":null},{"id":"2102.02515","title":"HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks","created":"2021-02-04","cited_by_count":38,"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":"2205.09940","title":"Conformal Prediction with Temporal Quantile Adjustments","created":"2022-05-20","cited_by_count":25,"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":"2110.12122","title":"Quantifying Epistemic Uncertainty in Deep Learning","created":"2021-10-23","cited_by_count":10,"area":null},{"id":"2306.05674","title":"Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks","created":"2023-06-09","cited_by_count":10,"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":"2309.06800","title":"Uncertainty-aware Traffic Prediction under Missing Data","created":"2023-09-13","cited_by_count":5,"area":null},{"id":"2602.10097","title":"Step-resolved data attribution for looped transformers","created":"2026-02-10","cited_by_count":5,"area":null},{"id":"2505.23196","title":"JAPAN: Joint Adaptive Prediction Areas with Normalising-Flows","created":"2025-05-29","cited_by_count":4,"area":null},{"id":"2407.06390","title":"JANET: Joint Adaptive predictioN-region Estimation for Time-series","created":"2024-07-08","cited_by_count":3,"area":null},{"id":"2302.11078","title":"Learning Mixture Structure on Multi-Source Time Series for Probabilistic Forecasting","created":"2023-02-22","cited_by_count":1,"area":null},{"id":"2203.04916","title":"Monitoring Time Series With Missing Values: a Deep Probabilistic Approach","created":"2022-03-09","cited_by_count":0,"area":null},{"id":"2504.03350","title":"Partially stochastic deep learning with uncertainty quantification for model predictive heating control","created":"2025-04-04","cited_by_count":0,"area":null},{"id":"2602.04714","title":"Bounded-Abstention Multi-horizon Time-series Forecasting","created":"2026-02-04","cited_by_count":0,"area":null}],"next_cursor":null}