Web: http://arxiv.org/abs/2205.03109

May 9, 2022, 1:11 a.m. | Mehedi Hasan, Abbas Khosravi, Ibrahim Hossain, Ashikur Rahman, Saeid Nahavandi

cs.LG updates on arXiv.org arxiv.org

Uncertainty quantification in a neural network is one of the most discussed
topics for safety-critical applications. Though Neural Networks (NNs) have
achieved state-of-the-art performance for many applications, they still provide
unreliable point predictions, which lack information about uncertainty
estimates. Among various methods to enable neural networks to estimate
uncertainty, Monte Carlo (MC) dropout has gained much popularity in a short
period due to its simplicity. In this study, we present a new version of the
traditional dropout layer where we …

arxiv dropout uncertainty

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

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC

Senior Data Science Writer

@ NannyML | Remote

Director of AI/ML Engineering

@ Armis Industries | Remote (US only), St. Louis, California

Digital Analytics Manager

@ Patagonia | Ventura, California