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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Moloud Abdar
- sl:arxiv_num : 2011.06225
- sl:arxiv_published : 2020-11-12T06:41:05Z
- sl:arxiv_summary : Uncertainty quantification (UQ) plays a pivotal role in reduction of
uncertainties during both optimization and decision making processes. It can be
applied to solve a variety of real-world applications in science and
engineering. Bayesian approximation and ensemble learning techniques are two
most widely-used UQ methods in the literature. In this regard, researchers have
proposed different UQ methods and examined their performance in a variety of
applications such as computer vision (e.g., self-driving cars and object
detection), image processing (e.g., image restoration), medical image analysis
(e.g., medical image classification and segmentation), natural language
processing (e.g., text classification, social media texts and recidivism
risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ
methods used in deep learning. Moreover, we also investigate the application of
these methods in reinforcement learning (RL). Then, we outline a few important
applications of UQ methods. Finally, we briefly highlight the fundamental
research challenges faced by UQ methods and discuss the future research
directions in this field.@en
- sl:arxiv_title : A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges@en
- sl:arxiv_updated : 2021-01-06T01:58:12Z
- sl:bookmarkOf : https://arxiv.org/abs/2011.06225
- sl:creationDate : 2022-09-08
- sl:creationTime : 2022-09-08T09:46:24Z
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