Web: http://arxiv.org/abs/1910.04858

Jan. 12, 2022, 2:10 a.m. | Lu Mi, Hao Wang, Yonglong Tian, Hao He, Nir Shavit

cs.LG updates on arXiv.org arxiv.org

Uncertainty estimation is an essential step in the evaluation of the
robustness for deep learning models in computer vision, especially when applied
in risk-sensitive areas. However, most state-of-the-art deep learning models
either fail to obtain uncertainty estimation or need significant modification
(e.g., formulating a proper Bayesian treatment) to obtain it. Most previous
methods are not able to take an arbitrary model off the shelf and generate
uncertainty estimation without retraining or redesigning it. To address this
gap, we perform a systematic exploration into training-free uncertainty
estimation for dense regression, an …

arxiv cv for regression training uncertainty

Statistics and Computer Science Specialist

@ Hawk-Research | Remote

Data Scientist, Credit/Fraud Strategy

@ Fora Financial | New York City

Postdoctoral Research Associate - Biomedical Natural Language Processing and Deep Learning

@ Oak Ridge National Laboratory - Oak Ridge, TN | Oak Ridge, TN, United States

Senior Machine Learning / Computer Vision Engineer

@ Glass Imaging | Los Altos, CA

Research Scientist in Biomedical Natural Language Processing and Deep Learning

@ Oak Ridge National Laboratory | Oak Ridge, TN

W3-Professorship for Intelligent Energy Management

@ Universität Bayreuth | Bayreuth, Germany