| A Simple Baseline for Bayesian Uncertainty in Deep Learning | Feb 7, 2019 | Bayesian InferenceDeep Learning | CodeCode Available | 1 | 5 |
| Probabilistic Circuits That Know What They Don't Know | Feb 13, 2023 | Uncertainty Quantification | CodeCode Available | 1 | 5 |
| Edge Tracing using Gaussian Process Regression | Nov 5, 2021 | regressionUncertainty Quantification | CodeCode Available | 1 | 5 |
| Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks | Feb 6, 2023 | Crowd CountingUncertainty Quantification | CodeCode Available | 1 | 5 |
| Deep active subspaces - a scalable method for high-dimensional uncertainty propagation | Feb 27, 2019 | Dimensionality ReductionUncertainty Quantification | CodeCode Available | 1 | 5 |
| Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling | Nov 15, 2023 | Uncertainty Quantification | CodeCode Available | 1 | 5 |
| Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures? | Sep 7, 2022 | Uncertainty Quantification | CodeCode Available | 1 | 5 |
| Quantifying Uncertainty in Deep Spatiotemporal Forecasting | May 25, 2021 | Decision Makingquantile regression | CodeCode Available | 1 | 5 |
| A Head to Predict and a Head to Question: Pre-trained Uncertainty Quantification Heads for Hallucination Detection in LLM Outputs | May 13, 2025 | HallucinationUncertainty Quantification | CodeCode Available | 1 | 5 |
| Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning | Jun 6, 2015 | Bayesian InferenceDeep Reinforcement Learning | CodeCode Available | 1 | 5 |
