Results 11 to 20 of about 7,687 (182)
Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation
IEEE Transactions on Big Data, 2023 Historical interactions are the default choice for recommender model training, which typically exhibit high sparsity, i.e., most user-item pairs are unobserved missing data. A standard choice is treating the missing data as negative training samples and estimating interaction likelihood between user-item pairs along with the observed interactions.
Chenxu Wang +5 more
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Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty [PDF]
Information Sciences, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Senge, Robin +6 more
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Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. [PDF]
Neurocomputing (Amst), 2019 Wang G +5 more
europepmc +2 more sources
LogSE: An Uncertainty-Based Multi-Task Loss Function for Learning Two Regression Tasks [PDF]
Journal of Universal Computer Science, 2022 Multi-task learning (MTL) is a popular method in machine learning which utilizes related information of multi tasks to learn a task more efficiently and accurately.
Zeinab Ghasemi-Naraghi +2 more
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Fairness through Aleatoric Uncertainty
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023 We propose a simple yet effective solution to tackle the often-competing goals of fairness and utility in classification tasks. While fairness ensures that the model's predictions are unbiased and do not discriminate against any particular group or individual, utility focuses on maximizing the model's predictive performance.
Anique Tahir, Lu Cheng, Huan Liu
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Intrinsic randomness in epidemic modelling beyond statistical uncertainty
Communications Physics, 2023 Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters). The majority of frameworks assessing infectious disease risk consider only epistemic uncertainty.
Matthew J. Penn +9 more
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Looking at the posterior: accuracy and uncertainty of neural-network predictions
Machine Learning: Science and Technology, 2023 Bayesian inference can quantify uncertainty in the predictions of neural networks using posterior distributions for model parameters and network output.
Hampus Linander +3 more
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Quantifying uncertainty of machine learning methods for loss given default
Frontiers in Applied Mathematics and Statistics, 2022 Machine learning has increasingly found its way into the credit risk literature. When applied to forecasting credit risk parameters, the approaches have been found to outperform standard statistical models. The quantification of prediction uncertainty is
Matthias Nagl +2 more
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SN Applied Sciences, 2022 The deep convolutional neural network (ConvNet) achieves significant segmentation performance on medical images of various modalities. However, the isolated errors in a large testing set with various tumor conditions are not acceptable in clinical ...
Ge Zhang, Hao Dang, Yulong Xu
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Aleatoric and Epistemic Uncertainty with Random Forests [PDF]
, 2020 10 pages, 4 ...
Shaker, Mohammad Hossein +1 more
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