A review of uncertainty quantification in deep learning: Techniques, applications and challenges
出版年份 2021 全文链接
标题
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
作者
关键词
Artificial intelligence, Uncertainty quantification, Deep learning, Machine learning, Bayesian statistics, Ensemble learning
出版物
Information Fusion
Volume 76, Issue -, Pages 243-297
出版商
Elsevier BV
发表日期
2021-05-24
DOI
10.1016/j.inffus.2021.05.008
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Uncertainty quantification for data-driven turbulence modelling with mondrian forests
- (2021) Ashley Scillitoe et al. JOURNAL OF COMPUTATIONAL PHYSICS
- A deep learning just-in-time modeling approach for soft sensor based on variational autoencoder
- (2020) Fan Guo et al. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
- Blind Spot Detection for Safe Sim-to-Real Transfer
- (2020) Ramya Ramakrishnan et al. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
- Deep Learning Uncertainty and Confidence Calibration for the Five-class Polyp Classification from Colonoscopy
- (2020) Gustavo Carneiro et al. MEDICAL IMAGE ANALYSIS
- Demystifying Brain Tumor Segmentation Networks: Interpretability and Uncertainty Analysis
- (2020) Parth Natekar et al. Frontiers in Computational Neuroscience
- Recent advances in deep learning
- (2020) Xizhao Wang et al. International Journal of Machine Learning and Cybernetics
- Bayesian neural networks for flight trajectory prediction and safety assessment
- (2020) Xiaoge Zhang et al. DECISION SUPPORT SYSTEMS
- An improved deep network for tissue microstructure estimation with uncertainty quantification
- (2020) Chuyang Ye et al. MEDICAL IMAGE ANALYSIS
- Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals
- (2020) Catalin Stoean et al. SENSORS
- Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach
- (2020) Thanh Dung Le et al. IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY
- DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images
- (2020) Teresa Araújo et al. MEDICAL IMAGE ANALYSIS
- Uncertainty-aware domain alignment for anatomical structure segmentation
- (2020) Cheng Bian et al. MEDICAL IMAGE ANALYSIS
- Ranking Information Extracted from Uncertainty Quantification of the Prediction of a Deep Learning Model on Medical Time Series Data
- (2020) Ruxandra Stoean et al. Mathematics
- Personalizing Activity Recognition Models Through Quantifying Different Types of Uncertainty Using Wearable Sensors
- (2020) Ali Akbari et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
- (2020) Yingda Xia et al. MEDICAL IMAGE ANALYSIS
- Integrating uncertainty in deep neural networks for MRI based stroke analysis
- (2020) Lisa Herzog et al. MEDICAL IMAGE ANALYSIS
- Calibration of deep probabilistic models with decoupled bayesian neural networks
- (2020) Juan Maroñas et al. NEUROCOMPUTING
- Probabilistic inference of Bayesian neural networks with generalized expectation propagation
- (2020) Jing Zhao et al. NEUROCOMPUTING
- Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation
- (2020) Alireza Mehrtash et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Information Aware max-norm Dirichlet networks for predictive uncertainty estimation
- (2020) Theodoros Tsiligkaridis NEURAL NETWORKS
- A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat
- (2020) Ross Harper et al. IEEE Transactions on Affective Computing
- Scalable Bayesian Uncertainty Quantification in Imaging Inverse Problems via Convex Optimization
- (2019) Audrey Repetti et al. SIAM Journal on Imaging Sciences
- Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control
- (2019) Abhijit Guha Roy et al. NEUROIMAGE
- Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
- (2019) Patrick McDermott et al. Entropy
- Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming
- (2019) Chao Ning et al. COMPUTERS & CHEMICAL ENGINEERING
- The MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty prediction
- (2019) Ruihan Hu et al. APPLIED INTELLIGENCE
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
- (2019) Yinhao Zhu et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks
- (2019) Nicholas Geneva et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Adversarial uncertainty quantification in physics-informed neural networks
- (2019) Yibo Yang et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
- (2019) Guotai Wang et al. NEUROCOMPUTING
- Reliable deep-learning-based phase imaging with uncertainty quantification
- (2019) Yujia Xue et al. Optica
- Distribution-free uncertainty quantification for kernel methods by gradient perturbations
- (2019) Balázs Cs. Csáji et al. MACHINE LEARNING
- Improving predictive uncertainty estimation using Dropout–Hamiltonian Monte Carlo
- (2019) Sergio Hernández et al. SOFT COMPUTING
- Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices
- (2019) Alessandro Brusaferri et al. APPLIED ENERGY
- One-dimensional modeling of fractional flow reserve in coronary artery disease: Uncertainty quantification and Bayesian optimization
- (2019) Minglang Yin et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Modeling and Planning Under Uncertainty Using Deep Neural Networks
- (2019) Daniel L. Marino et al. IEEE Transactions on Industrial Informatics
- Hashing with Mutual Information
- (2019) Fatih Cakir et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Robust Visual Tracking based on Variational Auto-encoding Markov Chain Monte Carlo
- (2019) Junseok Kwon INFORMATION SCIENCES
- Exploring uncertainty measures in deep networks for Multiple Sclerosis lesion detection and segmentation
- (2019) Tanya Nair et al. MEDICAL IMAGE ANALYSIS
- An improved fuzzy ARTMAP and Q-learning agent model for pattern classification
- (2019) Farhad Pourpanah et al. NEUROCOMPUTING
- Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN
- (2019) Hung P. Do et al. MAGNETIC RESONANCE IN MEDICINE
- Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images
- (2019) Davood Karimi et al. MEDICAL IMAGE ANALYSIS
- Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning
- (2019) Mike Walmsley et al. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
- ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning
- (2019) Łukasz Rączkowski et al. Scientific Reports
- Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification
- (2019) Rongrong Liu et al. Light-Science & Applications
- Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model
- (2019) Yongqi Liu et al. APPLIED ENERGY
- Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation
- (2019) Yongchan Kwon et al. COMPUTATIONAL STATISTICS & DATA ANALYSIS
- Deep ensemble learning based probabilistic load forecasting in smart grids
- (2019) Yandong Yang et al. ENERGY
- Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT
- (2019) Philipp Seebock et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Using Bayesian Deep Learning to Capture Uncertainty for Residential Net Load Forecasting
- (2019) Mingyang Sun et al. IEEE TRANSACTIONS ON POWER SYSTEMS
- Targeting the uncertainty of predictions at patient-level using an ensemble of classifiers coupled with calibration methods, Venn-ABERS, and Conformal Predictors: A case study in AD
- (2019) Telma Pereira et al. JOURNAL OF BIOMEDICAL INFORMATICS
- Commensal correlation network between segmentation and direct area estimation for bi-ventricle quantification
- (2019) Gongning Luo et al. MEDICAL IMAGE ANALYSIS
- Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps
- (2019) Kristoffer Wickstrøm et al. MEDICAL IMAGE ANALYSIS
- An attention-based neural framework for uncertainty identification on social media texts
- (2019) Xu Han et al. TSINGHUA SCIENCE AND TECHNOLOGY
- Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling
- (2019) Yuta Hiasa et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra- Observer Variability in 2D Echocardiography Quality Assessment
- (2019) Zhibin Liao et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy
- (2018) Sara Moccia et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
- (2018) Guotai Wang et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Neural Network-Based Uncertainty Quantification: A Survey of Methodologies and Applications
- (2018) H. M. Dipu Kabir et al. IEEE Access
- Deep echo state networks with uncertainty quantification for spatio-temporal forecasting
- (2018) Patrick L. McDermott et al. ENVIRONMETRICS
- Rough extreme learning machine: A new classification method based on uncertainty measure
- (2018) Lin Feng et al. NEUROCOMPUTING
- Crowdsourcing Thousands of Specialized Labels: A Bayesian Active Training Approach
- (2017) Maximilien Servajean et al. IEEE TRANSACTIONS ON MULTIMEDIA
- Linear Maximum Margin Classifier for Learning from Uncertain Data
- (2017) Christos Tzelepis et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- (2017) Vijay Badrinarayanan et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Fully Convolutional Networks for Semantic Segmentation
- (2017) Evan Shelhamer et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Variational Inference: A Review for Statisticians
- (2017) David M. Blei et al. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
- Measures of uncertainty for neighborhood rough sets
- (2017) Yumin Chen et al. KNOWLEDGE-BASED SYSTEMS
- A survey on deep learning in medical image analysis
- (2017) Geert Litjens et al. MEDICAL IMAGE ANALYSIS
- Echo State Networks for data-driven downhole pressure estimation in gas-lift oil wells
- (2017) Eric A. Antonelo et al. NEURAL NETWORKS
- Addressing uncertainty in atomistic machine learning
- (2017) Andrew A. Peterson et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Active learning enabled activity recognition
- (2017) H M Sajjad Hossain et al. Pervasive and Mobile Computing
- Leveraging uncertainty information from deep neural networks for disease detection
- (2017) Christian Leibig et al. Scientific Reports
- Bayesian Recurrent Neural Network for Language Modeling
- (2016) Jen-Tzung Chien et al. IEEE Transactions on Neural Networks and Learning Systems
- A study of active learning methods for named entity recognition in clinical text
- (2015) Yukun Chen et al. JOURNAL OF BIOMEDICAL INFORMATICS
- Sentiment analysis: Bayesian Ensemble Learning
- (2014) E. Fersini et al. DECISION SUPPORT SYSTEMS
- Uncertainty-Aware Guided Volume Segmentation
- (2010) Jorg-Stefan Prassni et al. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
- An ensemble uncertainty aware measure for directed hill climbing ensemble pruning
- (2010) Ioannis Partalas et al. MACHINE LEARNING
- Aleatory or epistemic? Does it matter?
- (2008) Armen Der Kiureghian et al. STRUCTURAL SAFETY
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