Work

Sampling-free Uncertainty Estimation in Gated Recurrent Units with Applications to Normative Modeling in Neuroimaging.

Year: 2019

Type: conference-paper

Abstract: uncertainty estimation for powerful sequential models such as GRUs.

Source: PubMed

Authors: Seong Jae Hwang, Ronak R Mehta, Hyunwoo J Kim, Sterling C Johnson, Vikas Singh

Institutions: University of Wisconsin–Madison, Korea University, Korea University, William S. Middleton Memorial Veterans Hospital

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institute on Aging, National Institute of Allergy and Infectious Diseases, National Institute of Biomedical Imaging and Bioengineering, U.S. National Library of Medicine

Awards: Maximizing Power in AD Clinical Trials via Multimodal Machine Learning, The Center for Predictive Computational Phenotyping-1 Overall, Algebraic Formulations for Characterizing Structural Brain Connectivity Changes and Pathology Transmission Networks in Preclinical Alzheimer's Disease, Manifold-valued statistical models for longitudinal morphometic analysis in preclinical Alzheimer's disease (AD), Wisconsin Alzheimer's Disease Research Center, CRCNS: Pooling Multisite data and Staistical Tests for analyzing biomarkers of Alzheimer's disease, The Longitudinal Course of Imaging Biomarkers in People At Risk for Alzheimer’s Disease, Wisconsin Registry for Alzheimer's Prevention, Wisconsin Alzheimer's Disease Research Center, Bio-Data Science Training Program

OpenAlex ID: W2964832966

FWCI: 0.407

Cites: 28

Cited by: 6

Related to: 10