Web: http://arxiv.org/abs/2206.07795

June 17, 2022, 1:13 a.m. | Biraja Ghoshal, Allan Tucker

cs.CV updates on arXiv.org arxiv.org

Estimated uncertainty by approximate posteriors in Bayesian neural networks
are prone to miscalibration, which leads to overconfident predictions in
critical tasks that have a clear asymmetric cost or significant losses. Here,
we extend the approximate inference for the loss-calibrated Bayesian framework
to dropweights based Bayesian neural networks by maximising expected utility
over a model posterior to calibrate uncertainty in deep learning. Furthermore,
we show that decisions informed by loss-calibrated uncertainty can improve
diagnostic performance to a greater extent than straightforward …

arxiv deep deep learning learning lg model on uncertainty

More from arxiv.org / cs.CV updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY