A review of uncertainty quantification in deep learning: Techniques, applications and challenges
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Abstract
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes. They have been applied to solve a variety of real-world problems in science and engineering. Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification (UQ) methods. 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, investigates the application of these methods in reinforcement learning, and highlights fundamental research challenges and directions associated with UQ.
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The record
- Venue
- Information Fusion
- Topic
- Anomaly Detection Techniques and Applications
- Field
- Computer Science
- Canadian institutions
- Université du Québec à MontréalUniversity of Waterloo
- Funders
- Australian Research CouncilNatural Sciences and Engineering Research Council of Canada
- Keywords
- Computer scienceVariety (cybernetics)Artificial intelligenceField (mathematics)Deep learningMachine learningReinforcement learningUncertainty quantificationData scienceImage processingImage (mathematics)
- Has abstract in OpenAlex
- yes