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洞見 - Uncertainty Quantification in Deep Learning
医用画像セグメンテーションのためのエビデンスディープラーニングモデルにおける不確実性とエラーの相関関係
医用画像セグメンテーションにおいて、エビデンスディープラーニング（EDL）モデルは、従来の不確実性定量化手法と比較して、予測エラーと不確実性の間に優れた相関関係を示す。
Lightning UQ Box: A Comprehensive PyTorch-Based Framework for Benchmarking Uncertainty Quantification Methods in Deep Learning
Lightning UQ Box is a new comprehensive and accessible open-source framework designed to simplify the application and evaluation of uncertainty quantification (UQ) methods in deep learning, particularly for computer vision tasks.
A Survey on Uncertainty Quantification for Deep Learning: An Uncertainty Source’s Perspective
Deep learning models sometimes make overconfident but incorrect predictions, leading to serious consequences. Uncertainty quantification methods aim to estimate confidence beyond accuracy.
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation
MixupMP proposes a novel approach to uncertainty quantification in deep learning, outperforming existing methods.