Benchmarking uncertainty disentanglement: Specialized uncertainties for specialized tasks
Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks,
including abstained prediction, out-of-distribution detection, and aleatoric uncertainty …
including abstained prediction, out-of-distribution detection, and aleatoric uncertainty …
Uncertainties of latent representations in computer vision
M Kirchhof - arXiv preprint arXiv:2408.14281, 2024 - arxiv.org
Uncertainty quantification is a key pillar of trustworthy machine learning. It enables safe
reactions under unsafe inputs, like predicting only when the machine learning model detects …
reactions under unsafe inputs, like predicting only when the machine learning model detects …
Robust and generalizable AI for medical image processing
E Konuk - 2024 - diva-portal.org
Artificial intelligence (AI) offers significant potential to enhance the accuracy and efficiency of
medical diagnosis, monitoring, and treatment. In ovarian cancer, where 70% of cases are …
medical diagnosis, monitoring, and treatment. In ovarian cancer, where 70% of cases are …
[PDF][PDF] Trustworthiness Score for Echo State Networks by Analysis of the Reservoir Dynamics
JM Enguita, D Garcıa, AA Cuadrado, D Pena… - esann.org
Epistemic uncertainty arises from input data areas where models lack exposure during
training and may result in significant performance degradation in deployment. Echo State …
training and may result in significant performance degradation in deployment. Echo State …