Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

Cited by (1,004)

Items 701–800 of 1,004

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18. Identifying Anomalous Behavior in Aircraft Landing Trajectory Using a Bayesian Autoencoder
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19. Dynamic Memory-Based Curiosity: A Bootstrap Approach for Exploration in Reinforcement Learning
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20. Empirical loss weight optimization for PINN modeling laser bio-effects on human skin for the 1D heat equation
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22. Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimation
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23. Practical Evaluation of Out-of-Distribution Detection Methods for Image Classification
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24. Uncertainty-based out-of-distribution detection requires suitable function space priors.
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25. Foraging Under Uncertainty Follows the Marginal Value Theorem with Bayesian Updating of Environment Representations
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27. Uncertainty Quantification and Quality Control for Heatmap-Based Landmark Detection Models
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28. Foraging animals use dynamic Bayesian updating to model meta-uncertainty in environment representations
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30. Bayesian mixture density networks for evaluating neutron-induced fission charge yields
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37. Quantifying uncertainty of mineral prediction using a novel Bayesian deep learning framework
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40. Uncertainty quantification of a flow boiling dataset for mini/micro-channels
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41. Evidential deep learning for interatomic potentials
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47. A novel approach to quantifying individual's biological aging using Korea’s national health screening program toward precision public health
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48. Bayesian Neural Networks for Satellite Fog Detection: Quantifying Epistemic and Aleatoric Uncertainties
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49. Uncertainty Quantification of Satellite-Based Essential Climate Variables Derived from Deep Learning
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54. Uncertainty Quantification of Microblock-Based Resource Models and Sequencing of Sampling
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57. Copula-based conformal prediction for prioritized heterogeneous multi-task learning
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58. Use of uncertainty quantification as a surrogate for layer segmentation error in Stargardt disease retinal OCT images
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59. Probabilistic Biomass Estimation with Conditional Generative Adversarial Networks
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60. Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control
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61. Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification
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62. Lightweight Approaches to DNN Regression Error Reduction: An Uncertainty Alignment Perspective
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63. Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification
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74. Active learning for the prediction of shape errors in milling
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