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Found 5 papers, 2 papers with code
Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules
Uncertainty representation and quantification are paramount in machine learning and constitute an important prerequisite for safety-critical applications.
Second-Order Uncertainty Quantification: Variance-Based Measures
Uncertainty quantification is a critical aspect of machine learning models, providing important insights into the reliability of predictions and aiding the decision-making process in real-world applications.
Conformal Prediction with Partially Labeled Data
While the predictions produced by conformal prediction are set-valued, the data used for training and calibration is supposed to be precise.
Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?
The quantification of aleatoric and epistemic uncertainty in terms of conditional entropy and mutual information, respectively, has recently become quite common in machine learning.
Attention-based Multiple Instance Learning with Mixed Supervision on the Camelyon16 Dataset
Since the standardization of Whole Slide Images (WSIs) digitization, the use of deep learning methods for the analysis of histological images has shown much potential.
