A review of uncertainty quantification in deep learning: Techniques, applications and challenges-Reference-Cited by-同舟云学术

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Published:2021-12 Issue: Volume:76 Page:243-297
ISSN:1566-2535
Container-title:Information Fusion
language:en
Short-container-title:Information Fusion

Author:

Abdar Moloud^ORCID,Pourpanah Farhad^ORCID,Hussain Sadiq^ORCID,Rezazadegan Dana,Liu Li,Ghavamzadeh Mohammad,Fieguth Paul^ORCID,Cao Xiaochun^ORCID,Khosravi Abbas,Acharya U. Rajendra,Makarenkov Vladimir^ORCID,Nahavandi Saeid

Funder

Natural Sciences and Engineering Research Council of Canada

Australian Research Council

Publisher

Elsevier BV

Subject

Hardware and Architecture,Information Systems,Signal Processing,Software

Reference736 articles.

1. To trust or not to trust a classifier;Jiang,2018

2. Uncertainty Estimation in Deep Learning with application to Spoken Language Assessment;Malinin,2019

3. The Need for Biases in Learning Generalizations;Mitchell,1980

4. Epistemic uncertainty sampling;Nguyen,2019

5. Active learning: A survey;Aggarwal,2014

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