| Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings | Nov 18, 2021 | Out-of-Distribution DetectionUncertainty Quantification | CodeCode Available | 0 | 5 |
| Exploring the Potential for Large Language Models to Demonstrate Rational Probabilistic Beliefs | Apr 18, 2025 | Information RetrievalUncertainty Quantification | CodeCode Available | 0 | 5 |
| Expectation consistency for calibration of neural networks | Mar 5, 2023 | Uncertainty Quantification | CodeCode Available | 0 | 5 |
| Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time series data | Sep 13, 2022 | Time SeriesTime Series Analysis | CodeCode Available | 0 | 5 |
| Flat Posterior Does Matter For Bayesian Model Averaging | Jun 21, 2024 | modelTransfer Learning | CodeCode Available | 0 | 5 |
| Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networks | Jan 10, 2025 | Anomaly DetectionBenchmarking | CodeCode Available | 0 | 5 |
| Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events | Dec 18, 2024 | Deep LearningUncertainty Quantification | CodeCode Available | 0 | 5 |
| A Study on the Calibration of In-context Learning | Dec 7, 2023 | In-Context LearningNatural Language Understanding | CodeCode Available | 0 | 5 |
| On double-descent in uncertainty quantification in overparametrized models | Oct 23, 2022 | Binary ClassificationUncertainty Quantification | CodeCode Available | 0 | 5 |
| Active Learning for Deep Gaussian Process Surrogates | Dec 15, 2020 | Active LearningGaussian Processes | CodeCode Available | 0 | 5 |
