aleatoric epistemic uncertainty

Enhancing Deep Learning Accuracy with Model Uncertainty Thresholding Techniques

Researchers explore thresholding techniques to enhance model uncertainty in deep learning. By refining how models handle uncertain predictions, these methods aim to improve decision-making and performance, potentially leading to more reliable AI applications across various fields.