A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges

[1]  Wray L. Buntine,et al.  Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users , 2020, IEEE Computational Intelligence Magazine.

[2]  A. Atiya,et al.  SpinalNet: Deep Neural Network With Gradual Input , 2020, IEEE Transactions on Artificial Intelligence.

[3]  Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation , 2020, ArXiv.

[4]  Philipp Hennig,et al.  Fast Predictive Uncertainty for Classification with Bayesian Deep Networks , 2020, UAI.

[5]  Samir Bhatt,et al.  \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi $$\end{document}πVAE: a stochastic process prior for Bayesian deep , 2020, Statistics and Computing.

[6]  Rui Tuo,et al.  Uncertainty Quantification for Bayesian Optimization , 2020, AISTATS.

[7]  Ross Harper,et al.  A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat , 2019, IEEE Transactions on Affective Computing.

[8]  Tuan A. Nguyen,et al.  Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning , 2020, AAAI.

[9]  Shiliang Sun,et al.  Multi-View Representation Learning With Deep Gaussian Processes , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Sebastian W. Ober,et al.  Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes , 2020, ICML.

[11]  Stefan T. Radev,et al.  Amortized Bayesian Model Comparison With Evidential Deep Learning , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Brian Nord,et al.  Deeply uncertain: comparing methods of uncertainty quantification in deep learning algorithms , 2020, Mach. Learn. Sci. Technol..

[13]  Thomas Lukasiewicz,et al.  Deep Bayesian Gaussian processes for uncertainty estimation in electronic health records , 2020, Scientific Reports.

[14]  Hugo Larochelle,et al.  DIBS: Diversity inducing Information Bottleneck in Model Ensembles , 2020, AAAI.

[15]  Mark Girolami,et al.  Uncertainty quantification for data-driven turbulence modelling with Mondrian forests , 2021, J. Comput. Phys..

[16]  A. Uncini,et al.  Bayesian Neural Networks With Maximum Mean Discrepancy Regularization , 2020, Neurocomputing.

[17]  Florian Beutler,et al.  Ensemble slice sampling , 2020, Statistics and Computing.

[18]  Saeid Nahavandi,et al.  Optimal Uncertainty-guided Neural Network Training , 2019, Appl. Soft Comput..

[19]  Isaac L. Chuang,et al.  Confident Learning: Estimating Uncertainty in Dataset Labels , 2019, J. Artif. Intell. Res..

[20]  Willem Waegeman,et al.  Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods , 2019, Machine Learning.

[21]  Shirin Enshaeifar,et al.  Continual Learning Using Bayesian Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Colin White,et al.  BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search , 2019, AAAI.

[23]  Vinay P. Namboodiri,et al.  Probabilistic framework for solving Visual Dialog , 2019, Pattern Recognit..

[24]  Naveen Garg,et al.  DropConnect is effective in modeling uncertainty of Bayesian deep networks , 2019, Scientific Reports.

[25]  Jianfei Cai,et al.  Exploring Uncertainty Measures for Image-caption Embedding-and-retrieval Task , 2019, ACM Trans. Multim. Comput. Commun. Appl..

[26]  Jürgen Pilz,et al.  Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[27]  J. Pauly,et al.  Uncertainty Quantification in Deep MRI Reconstruction , 2019, IEEE Transactions on Medical Imaging.

[28]  Uncertainty Estimation Using a Single Deep Deterministic Neural Network-ML Reproducibility Challenge 2020 , 2021 .

[29]  Iasonas Kokkinos,et al.  Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation , 2020, MICCAI.

[30]  Nicha C. Dvornek,et al.  Efficient Shapley Explanation for Features Importance Estimation Under Uncertainty , 2020, MICCAI.

[31]  Ming Yang,et al.  Deep Reinforcement Active Learning for Medical Image Classification , 2020, MICCAI.

[32]  Zhongchao Shi,et al.  Double-Uncertainty Weighted Method for Semi-supervised Learning , 2020, MICCAI.

[33]  J. Alison Noble,et al.  Uncertainty Estimates as Data Selection Criteria to Boost Omni-Supervised Learning , 2020, MICCAI.

[34]  Yan Li,et al.  An Effective Data Refinement Approach for Upper Gastrointestinal Anatomy Recognition , 2020, MICCAI.

[35]  Elsa D. Angelini,et al.  Heterogeneity Measurement of Cardiac Tissues Leveraging Uncertainty Information from Image Segmentation , 2020, MICCAI.

[36]  Hao Zheng,et al.  Cartilage Segmentation in High-Resolution 3D Micro-CT Images via Uncertainty-Guided Self-training with Very Sparse Annotation , 2020, MICCAI.

[37]  Ertunc Erdil,et al.  RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation , 2020, UNSURE/GRAIL@MICCAI.

[38]  Qiaosong Wang,et al.  Mixup-CAM: Weakly-supervised Semantic Segmentation via Uncertainty Regularization , 2020, BMVC.

[39]  Shuang Yu,et al.  Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling , 2020, MICCAI.

[40]  Claire Donnat,et al.  A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses , 2020, 2007.13847.

[41]  Kai Ma,et al.  Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation , 2020, MICCAI.

[42]  Beate Sick,et al.  Integrating uncertainty in deep neural networks for MRI based stroke analysis , 2020, Medical Image Anal..

[43]  Heri Ramampiaro,et al.  Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles , 2020, UAI.

[44]  Michael I. Jordan,et al.  Transferable Calibration with Lower Bias and Variance in Domain Adaptation , 2020, NeurIPS.

[45]  Bo Li,et al.  On uncertainty estimation in active learning for image segmentation , 2020, ArXiv.

[46]  Jimeng Sun,et al.  SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates , 2020, ICML.

[47]  Yuxin Chen,et al.  Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI , 2020, ArXiv.

[48]  Y. Teh,et al.  Bayesian Deep Ensembles via the Neural Tangent Kernel , 2020, NeurIPS.

[49]  Jasper Snoek,et al.  Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks , 2020, ArXiv.

[50]  Vishal M. Patel,et al.  Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification , 2020, MICCAI.

[51]  Jörg Krüger,et al.  Uncertainty Quantification in Deep Residual Neural Networks , 2020, ArXiv.

[52]  Beate Sick,et al.  Single Shot MC Dropout Approximation , 2020, ArXiv.

[53]  Jian Tang,et al.  Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs , 2020, ICML.

[54]  Frank Kirchner,et al.  Evaluating Uncertainty Estimation Methods on 3D Semantic Segmentation of Point Clouds , 2020, ArXiv.

[55]  Gonzalo Joya,et al.  Ranking Information Extracted from Uncertainty Quantification of the Prediction of a Deep Learning Model on Medical Time Series Data , 2020 .

[56]  Richard E. Turner,et al.  Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes , 2020, NeurIPS.

[57]  Y. Gal,et al.  Identifying Causal Effect Inference Failure with Uncertainty-Aware Models , 2020, NeurIPS.

[58]  Alberto Cano,et al.  Distributed Selection of Continuous Features in Multilabel Classification Using Mutual Information , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[59]  Mihaela van der Schaar,et al.  Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions , 2020, ICML.

[60]  Dong Yang,et al.  Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation , 2020, Medical Image Anal..

[61]  Shiliang Sun,et al.  Probabilistic inference of Bayesian neural networks with generalized expectation propagation , 2020, Neurocomputing.

[62]  Sergey Levine,et al.  Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? , 2020, ICML.

[63]  Alexander F. Kolen,et al.  Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet , 2020, MICCAI.

[64]  A. Gelman,et al.  Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors , 2020, J. Mach. Learn. Res..

[65]  Paul Rayson,et al.  MUMBO: MUlti-task Max-value Bayesian Optimization , 2020, ArXiv.

[66]  Finale Doshi-Velez,et al.  Learned Uncertainty-Aware (LUNA) Bases for Bayesian Regression using Multi-Headed Auxiliary Networks , 2020, ArXiv.

[67]  Walter J. Scheirer,et al.  A Bayesian Evaluation Framework for Ground Truth-Free Visual Recognition Tasks , 2020, ArXiv.

[68]  Max-Heinrich Laves,et al.  Calibration of Model Uncertainty for Dropout Variational Inference , 2020, ArXiv.

[69]  Luigi Acerbi,et al.  Variational Bayesian Monte Carlo with Noisy Likelihoods , 2020, NeurIPS.

[70]  Yee Whye Teh,et al.  Neural Ensemble Search for Performant and Calibrated Predictions , 2020, ArXiv.

[71]  Xiaoning Qian,et al.  NADS: Neural Architecture Distribution Search for Uncertainty Awareness , 2020, ICML.

[72]  Shuang Yu,et al.  Uncertainty-aware domain alignment for anatomical structure segmentation , 2020, Medical Image Anal..

[73]  Yarin Gal,et al.  Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers , 2020, ArXiv.

[74]  Xiaoning Qian,et al.  Bayesian Graph Neural Networks with Adaptive Connection Sampling , 2020, ICML.

[75]  Akshay Pai,et al.  Uncertainty quantification in medical image segmentation with Normalizing Flows , 2020, MLMI@MICCAI.

[76]  Marc Combalia,et al.  Uncertainty Estimation in Deep Neural Networks for Dermoscopic Image Classification , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[77]  Jun Zhu,et al.  Calibrated Reliable Regression using Maximum Mean Discrepancy , 2020, NeurIPS.

[78]  Dustin Tran,et al.  Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness , 2020, NeurIPS.

[79]  Yoichi Sato,et al.  Generalizing Hand Segmentation in Egocentric Videos With Uncertainty-Guided Model Adaptation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[80]  Konstantinos Kamnitsas,et al.  Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty , 2020, NeurIPS.

[81]  Michael Felsberg,et al.  Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[82]  Saeid Nahavandi,et al.  Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals , 2020, Sensors.

[83]  Anand Avati,et al.  CRUDE: Calibrating Regression Uncertainty Distributions Empirically , 2020 .

[84]  Christopher Healy,et al.  Improving Regression Uncertainty Estimates with an Empirical Prior , 2020, ArXiv.

[85]  Luc Van Gool,et al.  SCAN: Learning to Classify Images Without Labels , 2020, ECCV.

[86]  Luc Van Gool,et al.  Learning To Classify Images Without Labels , 2020, ECCV.

[87]  Yong Man Ro,et al.  Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation , 2020, ArXiv.

[88]  Xiaoning Qian,et al.  Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator , 2020, UAI.

[89]  Bishesh Khanal,et al.  Uncertainty Estimation in Deep 2D Echocardiography Segmentation , 2020, ArXiv.

[90]  Michael W. Dusenberry,et al.  Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors , 2020, ICML.

[91]  Stefano Mattoccia,et al.  On the Uncertainty of Self-Supervised Monocular Depth Estimation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[92]  Oleg Granichin,et al.  Simultaneous Perturbation Stochastic Approximation for Few-Shot Learning , 2020, 2020 European Control Conference (ECC).

[93]  Neil D. Lawrence,et al.  Empirical Bayes Transductive Meta-Learning with Synthetic Gradients , 2020, ICLR.

[94]  Hung-Yu Tseng,et al.  Regularizing Meta-Learning via Gradient Dropout , 2020, ACCV.

[95]  Nick Barnes,et al.  UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[96]  Pablo Hernandez-Leal,et al.  Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents , 2020, AAAI.

[97]  Murat Sensoy,et al.  Uncertainty-Aware Deep Classifiers Using Generative Models , 2020, AAAI.

[98]  Janardhan Rao Doppa,et al.  Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization , 2020, AAAI.

[99]  Yao Hu,et al.  Uncertainty Aware Graph Gaussian Process for Semi-Supervised Learning , 2020, AAAI.

[100]  Marco Montali,et al.  Temporal Logics Over Finite Traces with Uncertainty , 2020, AAAI.

[101]  Mijung Park,et al.  Radial and Directional Posteriors for Bayesian Deep Learning , 2020, AAAI.

[102]  Sankaran Mahadevan,et al.  Bayesian neural networks for flight trajectory prediction and safety assessment , 2020, Decis. Support Syst..

[103]  Tao Mei,et al.  X-Linear Attention Networks for Image Captioning , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[104]  Bhavya Kailkhura,et al.  Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning , 2020, ICML.

[105]  Vincent Andrearczyk,et al.  An exploration of uncertainty information for segmentation quality assessment , 2020, Medical Imaging: Image Processing.

[106]  Seong Joon Oh,et al.  An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods , 2020, ArXiv.

[107]  Joost R. van Amersfoort,et al.  Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network , 2020, ICML 2020.

[108]  Gustavo Carneiro,et al.  Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy , 2020, Medical Image Anal..

[109]  Anil K. Seth,et al.  Reinforcement Learning through Active Inference , 2020, ArXiv.

[110]  Jonathon S. Hare,et al.  FMix: Enhancing Mixed Sample Data Augmentation , 2020 .

[111]  Jonathon S. Hare,et al.  Understanding and Enhancing Mixed Sample Data Augmentation , 2020, ArXiv.

[112]  Yuexi Wang,et al.  Uncertainty Quantification for Sparse Deep Learning , 2020, AISTATS.

[113]  Mohammad Emtiyaz Khan,et al.  Training Binary Neural Networks using the Bayesian Learning Rule , 2020, ICML.

[114]  Jasper Snoek,et al.  Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling , 2020, ArXiv.

[115]  Farhad Pourpanah,et al.  Recent advances in deep learning , 2020, International Journal of Machine Learning and Cybernetics.

[116]  M. Gales,et al.  Uncertainty in Structured Prediction , 2020, ArXiv.

[117]  Dmitry Vetrov,et al.  Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning , 2020, ICLR.

[118]  Biao Huang,et al.  A deep learning just-in-time modeling approach for soft sensor based on variational autoencoder , 2020 .

[119]  Jianzhong Zhou,et al.  Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model , 2020 .

[120]  Silvan C. Quax,et al.  Visual Attention Through Uncertainty Minimization in Recurrent Generative Models , 2020, bioRxiv.

[121]  Max Welling,et al.  Simple and Accurate Uncertainty Quantification from Bias-Variance Decomposition , 2020, ArXiv.

[122]  G. Sanguinetti,et al.  Robustness of Bayesian Neural Networks to Gradient-Based Attacks , 2020, NeurIPS.

[123]  Jasper Snoek,et al.  The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks , 2020, ICML.

[124]  Ganapathy Krishnamurthi,et al.  Demystifying Brain Tumor Segmentation Networks: Interpretability and Uncertainty Analysis , 2020, Frontiers in Computational Neuroscience.

[125]  ϵ-shotgun: ϵ-greedy batch bayesian optimisation , 2020, GECCO.

[126]  Myunghee Cho Paik,et al.  Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation , 2020, Comput. Stat. Data Anal..

[127]  Zhuoran Wang,et al.  An attention-based neural framework for uncertainty identification on social media texts , 2020, Tsinghua Science and Technology.

[128]  Yanan Fan,et al.  Cosmo VAE: Variational Autoencoder for CMB Image Inpainting , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

[129]  Rita Noumeir,et al.  Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach , 2020, IEEE Transactions on Applied Superconductivity.

[130]  Mark A. Anastasio,et al.  Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks , 2020, Medical Imaging.

[131]  Raghav Mehta,et al.  Uncertainty Evaluation Metric for Brain Tumour Segmentation , 2020, ArXiv.

[132]  Chuyang Ye,et al.  An improved deep network for tissue microstructure estimation with uncertainty quantification , 2020, Medical Image Anal..

[133]  Wenjun Zeng,et al.  Uncertainty-Aware Multi-Shot Knowledge Distillation for Image-Based Object Re-Identification , 2020, AAAI.

[134]  Sebastian Nowozin,et al.  Hydra: Preserving Ensemble Diversity for Model Distillation , 2020, ArXiv.

[135]  Felix J. Herrmann,et al.  A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification , 2020, EAGE 2020 Annual Conference & Exhibition Online.

[136]  Roozbeh Jafari,et al.  Personalizing Activity Recognition Models Through Quantifying Different Types of Uncertainty Using Wearable Sensors , 2020, IEEE Transactions on Biomedical Engineering.

[137]  Yi Wang,et al.  Using Bayesian Deep Learning to Capture Uncertainty for Residential Net Load Forecasting , 2020, IEEE Transactions on Power Systems.

[138]  Jordi Vitria,et al.  Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability , 2019, FAT*.

[139]  Unsupervised Scene Adaptation with Memory Regularization in vivo , 2019, IJCAI.

[140]  Zhenguo Li,et al.  Meta-Learning PAC-Bayes Priors in Model Averaging , 2020, AAAI.

[141]  Marcella Cornia,et al.  Meshed-Memory Transformer for Image Captioning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[142]  Alzheimer's Disease Neuroimaging Initiative,et al.  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, J. Biomed. Informatics.

[143]  Furong Huang,et al.  Sampling-Free Learning of Bayesian Quantized Neural Networks , 2019, ICLR.

[144]  Jaehoon Lee,et al.  Neural Tangents: Fast and Easy Infinite Neural Networks in Python , 2019, ICLR.

[145]  AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty , 2019, ICLR.

[146]  Kian Hsiang Low,et al.  Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression , 2019, AAAI.

[147]  Purang Abolmaesumi,et al.  Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation , 2019, IEEE Transactions on Medical Imaging.

[148]  Houqiang Li,et al.  Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization , 2019, AAAI.

[149]  Dimitris N. Metaxas,et al.  Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons , 2019, AAAI.

[150]  Nina Miolane,et al.  Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[151]  Moshe Tennenholtz,et al.  VCG Under Sybil (False-name) Attacks - a Bayesian Analysis , 2020, AAAI.

[152]  Purang Abolmaesumi,et al.  On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra- Observer Variability in 2D Echocardiography Quality Assessment , 2019, IEEE Transactions on Medical Imaging.

[153]  Gongning Luo,et al.  Commensal correlation network between segmentation and direct area estimation for bi-ventricle quantification , 2019, Medical Image Anal..

[154]  A. Campilho,et al.  DR|GRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images , 2019, Medical Image Anal..

[155]  Gregory P. Meyer,et al.  Learning an Uncertainty-Aware Object Detector for Autonomous Driving , 2019, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[156]  S. Roberts,et al.  We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric Uncertainty. , 2019, 1910.10793.

[157]  Sparse Orthogonal Variational Inference for Gaussian Processes , 2019, AISTATS.

[158]  W. Zuo,et al.  ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[159]  Daguang Xu,et al.  NeurReg: Neural Registration and Its Application to Image Segmentation , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[160]  Jason J. Corso,et al.  Unified Vision-Language Pre-Training for Image Captioning and VQA , 2019, AAAI.

[161]  Jayaraman J. Thiagarajan,et al.  Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors , 2019, AAAI.

[162]  Richard E. Turner,et al.  On the Expressiveness of Approximate Inference in Bayesian Neural Networks , 2019, NeurIPS.

[163]  Roberto Paredes,et al.  Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks , 2019, Neurocomputing.

[164]  Gustavo Carneiro,et al.  Uncertainty in Model-Agnostic Meta-Learning using Variational Inference , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[165]  Yuta Hiasa,et al.  Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling , 2019, IEEE Transactions on Medical Imaging.

[166]  D. Scaramuzza,et al.  A General Framework for Uncertainty Estimation in Deep Learning , 2019, IEEE Robotics and Automation Letters.

[167]  Michael A. Osborne,et al.  Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning , 2019, AISTATS.

[168]  Jonas Mueller,et al.  Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles , 2019, AAAI.

[169]  Christos Dimitrakakis,et al.  Epistemic Risk-Sensitive Reinforcement Learning , 2019, ESANN.

[170]  Omesh Tickoo,et al.  Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes , 2019, AAAI.

[171]  Jeremy Nixon,et al.  Analyzing the role of model uncertainty for electronic health records , 2019, CHIL.

[172]  Trevor Darrell,et al.  Uncertainty-guided Continual Learning with Bayesian Neural Networks , 2019, ICLR.

[173]  Nassir Navab,et al.  Uncertainty-based Graph Convolutional Networks for Organ Segmentation Refinement , 2019, MIDL.

[174]  Thomas B. Schön,et al.  Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[175]  Eunho Yang,et al.  Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks , 2019, ICLR.

[176]  Georg Langs,et al.  Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT , 2019, IEEE Transactions on Medical Imaging.

[177]  Stefanos Kollias,et al.  Capsule Routing via Variational Bayes , 2019, AAAI.

[178]  Scott Cheng‐Hsin Yang,et al.  Interpretable Deep Gaussian Processes with Moments , 2019, International Conference on Artificial Intelligence and Statistics.

[179]  Andrey Malinin,et al.  Ensemble Distribution Distillation , 2019, ICLR.

[180]  Georgios B. Giannakis,et al.  Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks , 2019, 2020 Information Theory and Applications Workshop (ITA).

[181]  Bernhard Schölkopf,et al.  From Variational to Deterministic Autoencoders , 2019, ICLR.

[182]  Peder A. Olsen,et al.  Crowd Counting with Decomposed Uncertainty , 2019, AAAI.

[183]  Steven L. Waslander,et al.  BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors , 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[184]  Mark van der Wilk,et al.  Bayesian Image Classification with Deep Convolutional Gaussian Processes , 2019, AISTATS.

[185]  Andrew Gordon Wilson,et al.  Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning , 2019, ICLR.

[186]  Yee Whye Teh,et al.  Functional Regularisation for Continual Learning using Gaussian Processes , 2019, ICLR.

[187]  Mohammad Shokrolah Shirazi,et al.  Learning and Reasoning for Robot Sequential Decision Making under Uncertainty , 2019, AAAI.

[188]  Dong Yang,et al.  3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training , 2018, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[189]  Mohamed H. Zaki,et al.  Uncertainty in Neural Networks: Approximately Bayesian Ensembling , 2018, AISTATS.

[190]  Stratos Idreos,et al.  MotherNets: Rapid Deep Ensemble Learning , 2018, MLSys.

[191]  Michael C. Kampffmeyer,et al.  Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps , 2018, Medical Image Anal..

[192]  Matias Valdenegro-Toro,et al.  Improving predictive uncertainty estimation using Dropout–Hamiltonian Monte Carlo , 2018, Soft Comput..

[193]  St,et al.  CosmoVAE: Variational Autoencoder for CMB Image Inpainting* , 2020 .

[194]  Douglas A. Talbert,et al.  Uncertainty Quantification in Multimodal Ensembles of Deep Learners , 2020, FLAIRS.

[195]  Yarin Gal,et al.  A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks , 2019, ArXiv.

[196]  Weijun Hong,et al.  Deep ensemble learning based probabilistic load forecasting in smart grids , 2019 .

[197]  Zhenyue Qin,et al.  Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator , 2019, ArXiv.

[198]  Jun Zhu,et al.  DBSN: Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structures , 2019, ArXiv.

[199]  Stephan Günnemann,et al.  Uncertainty on Asynchronous Time Event Prediction , 2019, NeurIPS.

[200]  Zi Huang,et al.  Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling , 2019, ArXiv.

[201]  Jeremiah Liu,et al.  Accurate Uncertainty Estimation and Decomposition in Ensemble Learning , 2019, NeurIPS.

[202]  Edward O. Pyzer-Knapp,et al.  Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning , 2019, ArXiv.

[203]  Finale Doshi-Velez,et al.  Learning Deep Bayesian Latent Variable Regression Models that Generalize: When Non-identifiability is a Problem , 2019, ArXiv.

[204]  Walter Karlen,et al.  CXPlain: Causal Explanations for Model Interpretation under Uncertainty , 2019, NeurIPS.

[205]  Andrey Malinin,et al.  Uncertainty estimation in deep learning with application to spoken language assessment , 2019 .

[206]  S. Roberts,et al.  We Know Where We Don't Know: 3D Bayesian CNNs for Uncertainty Quantification of Binary Segmentations for Material Simulations , 2019, ArXiv.

[207]  Vanessa Böhm,et al.  Uncertainty Quantification with Generative Models , 2019, ArXiv.

[208]  Willem Waegeman,et al.  Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction , 2019, ArXiv.

[209]  Matias Valdenegro-Toro Deep Sub-Ensembles for Fast Uncertainty Estimation in Image Classification , 2019, arXiv.org.

[210]  Daniel R. Jiang,et al.  BoTorch: Programmable Bayesian Optimization in PyTorch , 2019, ArXiv.

[211]  Elliot J. Crowley,et al.  Deep Kernel Transfer in Gaussian Processes for Few-shot Learning , 2019, ArXiv.

[212]  Johann Marius Zöllner,et al.  Calibrating Uncertainty Models for Steering Angle Estimation , 2019, 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

[213]  R. Krishnan,et al.  Efficient Priors for Scalable Variational Inference in Bayesian Deep Neural Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[214]  Tao Xiang,et al.  Robust Person Re-Identification by Modelling Feature Uncertainty , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[215]  Pascal Mettes,et al.  Bayesian 3D ConvNets for Action Recognition from Few Examples , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[216]  Jie Li,et al.  Universal Adversarial Perturbation via Prior Driven Uncertainty Approximation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[217]  Kian Hsiang Low,et al.  Implicit Posterior Variational Inference for Deep Gaussian Processes , 2019, NeurIPS.

[218]  Matthieu Cord,et al.  Addressing Failure Prediction by Learning Model Confidence , 2019, NeurIPS.

[219]  Purang Abolmaesumi,et al.  Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images , 2019, Medical Image Anal..

[220]  Jasper Snoek,et al.  Refining the variational posterior through iterative optimization , 2019 .

[221]  José Miguel Hernández-Lobato,et al.  Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection , 2019, ArXiv.

[222]  Michael C. Mozer,et al.  Stochastic Prototype Embeddings , 2019, ArXiv.

[223]  Chee Peng Lim,et al.  An improved fuzzy ARTMAP and Q-learning agent model for pattern classification , 2019, Neurocomputing.

[224]  Percy Liang,et al.  Verified Uncertainty Calibration , 2019, NeurIPS.

[225]  Buu Truong Phan,et al.  Bayesian Deep Learning and Uncertainty in Computer Vision , 2019 .

[226]  R Devon Hjelm,et al.  Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning , 2019, ArXiv.

[227]  Jianwei Yu,et al.  Comparative Study of Parametric and Representation Uncertainty Modeling for Recurrent Neural Network Language Models , 2019, INTERSPEECH.

[228]  Majid Abdolshah,et al.  Cost-aware Multi-objective Bayesian optimisation , 2019, ArXiv.

[229]  Andrea Vitali,et al.  Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices , 2019, Applied Energy.

[230]  Sébastien Destercke,et al.  Epistemic Uncertainty Sampling , 2019, DS.

[231]  Thomas L. Griffiths,et al.  Human Uncertainty Makes Classification More Robust , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[232]  Vinay P. Namboodiri,et al.  U-CAM: Visual Explanation Using Uncertainty Based Class Activation Maps , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[233]  Rudolf Mester,et al.  Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[234]  Minglang Yin,et al.  One-dimensional modeling of fractional flow reserve in coronary artery disease: Uncertainty quantification and Bayesian optimization , 2019, Computer Methods in Applied Mechanics and Engineering.

[235]  Sebastian Nowozin,et al.  Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model , 2019, NeurIPS.

[236]  Huanbo Luan,et al.  Improving Back-Translation with Uncertainty-based Confidence Estimation , 2019, EMNLP.

[237]  José Miguel Hernández-Lobato,et al.  Bayesian Batch Active Learning as Sparse Subset Approximation , 2019, NeurIPS.

[238]  Federico Tombari,et al.  Sampling-Free Epistemic Uncertainty Estimation Using Approximated Variance Propagation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[239]  Bangti Jin,et al.  Probabilistic Residual Learning for Aleatoric Uncertainty in Image Restoration , 2019, ArXiv.

[240]  Antonio Criminisi,et al.  Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement , 2019, ArXiv.

[241]  M. Jorge Cardoso,et al.  As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging , 2019, MICCAI.

[242]  Seongok Ryu,et al.  A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification , 2019, Chemical science.

[243]  Terrance E. Boult,et al.  Learning and the Unknown: Surveying Steps toward Open World Recognition , 2019, AAAI.

[244]  Mong-Li Lee,et al.  Building Trust in Deep Learning System towards Automated Disease Detection , 2019, AAAI.

[245]  Andrew Gordon Wilson,et al.  Subspace Inference for Bayesian Deep Learning , 2019, UAI.

[246]  Kaiqi Huang,et al.  Bootstrap Estimated Uncertainty of the Environment Model for Model-Based Reinforcement Learning , 2019, AAAI.

[247]  Lawrence Carin,et al.  Communication-Efficient Stochastic Gradient MCMC for Neural Networks , 2019, AAAI.

[248]  Chi-Wing Fu,et al.  Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation , 2019, MICCAI.

[249]  Takashi Matsubara,et al.  Predictable Uncertainty-Aware Unsupervised Deep Anomaly Segmentation , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[250]  Christian S. Perone,et al.  Deep Active Learning for Axon-Myelin Segmentation on Histology Data , 2019, ArXiv.

[251]  Mauricio Reyes,et al.  Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation , 2019, MICCAI.

[252]  Henkjan Huisman,et al.  Supervised Uncertainty Quantification for Segmentation with Multiple Annotations , 2019, MICCAI.

[253]  Feng Chen,et al.  Uncertainty-based Decision Making Using Deep Reinforcement Learning , 2019, 2019 22th International Conference on Information Fusion (FUSION).

[254]  Marcello Restelli,et al.  Exploiting Action-Value Uncertainty to Drive Exploration in Reinforcement Learning , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[255]  Dawn Song,et al.  Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty , 2019, NeurIPS.

[256]  Richard E. Turner,et al.  'In-Between' Uncertainty in Bayesian Neural Networks , 2019, ArXiv.

[257]  Hannah Lu,et al.  Fast uncertainty quantification of reservoir simulation with variational U-Net , 2019, 1907.00718.

[258]  Soumya Ghosh,et al.  Quality of Uncertainty Quantification for Bayesian Neural Network Inference , 2019, ArXiv.

[259]  Giovanni Ciná,et al.  Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications , 2019, ArXiv.

[260]  Yarin Gal,et al.  BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning , 2019, NeurIPS.

[261]  Dorin Comaniciu,et al.  Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment , 2019, MICCAI.

[262]  Omesh Tickoo,et al.  MOPED: Efficient priors for scalable variational inference in Bayesian deep neural networks , 2019 .

[263]  Hossein Azizpour,et al.  Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation , 2019, ICML 2019.

[264]  Qingyang Wu,et al.  Quantifying Intrinsic Uncertainty in Classification via Deep Dirichlet Mixture Networks , 2019, ArXiv.

[265]  Alexandre Alahi,et al.  MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[266]  Ender Konukoglu,et al.  PHiSeg: Capturing Uncertainty in Medical Image Segmentation , 2019, MICCAI.

[267]  Sebastian Nowozin,et al.  Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift , 2019, NeurIPS.

[268]  Benjamin M. Marlin,et al.  Assessing the Robustness of Bayesian Dark Knowledge to Posterior Uncertainty , 2019, ArXiv.

[269]  Ewa Szczurek,et al.  ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning , 2019, Scientific Reports.

[270]  Melih Kandemir,et al.  Bayesian Prior Networks with PAC Training , 2019, ArXiv.

[271]  Suyash P. Awate,et al.  A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration , 2019, IPMI.

[272]  Matthew D. Smith,et al.  Segmentation Certainty Through Uncertainty: Uncertainty-Refined Binary Volumetric Segmentation Under Multifactor Domain Shift , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[273]  Shuigeng Zhou,et al.  Versatile Multiple Choice Learning and Its Application to Vision Computing , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[274]  Zhou Yu,et al.  Deep Modular Co-Attention Networks for Visual Question Answering , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[275]  Vishal M. Patel,et al.  Uncertainty Guided Multi-Scale Residual Learning-Using a Cycle Spinning CNN for Single Image De-Raining , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[276]  Thomas Brox,et al.  Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[277]  Geoffrey E. Hinton,et al.  Learning Sparse Networks Using Targeted Dropout , 2019, ArXiv.

[278]  Venkatesh Umaashankar,et al.  ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity Recognition , 2019, EMDL '19.

[279]  Andrey Malinin,et al.  Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness , 2019, NeurIPS.

[280]  Yaniv Gurwicz,et al.  Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections , 2019, NeurIPS.

[281]  Klaus H. Maier-Hein,et al.  A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities , 2019, ArXiv.

[282]  Taesup Moon,et al.  Uncertainty-based Continual Learning with Adaptive Regularization , 2019, NeurIPS.

[283]  Gopinath Chennupati,et al.  On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks , 2019, NeurIPS.

[284]  Ji Yi,et al.  Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification , 2019, Light: Science & Applications.

[285]  Doina Precup,et al.  Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data , 2019, MIDL.

[286]  William R. Clements,et al.  Estimating Risk and Uncertainty in Deep Reinforcement Learning , 2019, ArXiv.

[287]  Florian Wenzel,et al.  Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation , 2019, UAI.

[288]  Hod Lipson,et al.  Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network , 2019, ArXiv.

[289]  Daniel L. Marino,et al.  Modeling and Planning Under Uncertainty Using Deep Neural Networks , 2019, IEEE Transactions on Industrial Informatics.

[290]  Yarin Gal,et al.  Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning , 2019, Monthly Notices of the Royal Astronomical Society.

[291]  Srivatsan Srinivasan,et al.  Output-Constrained Bayesian Neural Networks , 2019, ArXiv.

[292]  Tim Pearce,et al.  Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions , 2019, UAI.

[293]  Seong Joon Oh,et al.  CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[294]  Carlos D. Castillo,et al.  Uncertainty Modeling of Contextual-Connections Between Tracklets for Unconstrained Video-Based Face Recognition , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[295]  Nader Karimi,et al.  Exploiting Uncertainty of Deep Neural Networks for Improving Segmentation Accuracy in MRI Images , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[296]  Lei Tian,et al.  Reliable deep-learning-based phase imaging with uncertainty quantification. , 2019, Optica.

[297]  Kuangyan Song,et al.  "Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations , 2019 .

[298]  Gustavo Carneiro,et al.  Bayesian Generative Active Deep Learning , 2019, ICML.

[299]  Anil K. Jain,et al.  Probabilistic Face Embeddings , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[300]  Tassilo Klein,et al.  Uncertainty-Driven Semantic Segmentation through Human-Machine Collaborative Learning , 2019, 1909.00626.

[301]  Subhransu Maji,et al.  A Bayesian Perspective on the Deep Image Prior , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[302]  Hyuk-Jae Lee,et al.  Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[303]  Jiawei He,et al.  A Variational Auto-Encoder Model for Stochastic Point Processes , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[304]  Vincent Marchau,et al.  Decision Making under Deep Uncertainty: From Theory to Practice , 2019 .

[305]  Fengqi You,et al.  Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming , 2019, Comput. Chem. Eng..

[306]  Trevor Darrell,et al.  Variational Adversarial Active Learning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[307]  Diksha Garg,et al.  Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models , 2019, International Journal of Prognostics and Health Management.

[308]  Aliaksandr Hubin,et al.  Combining Model and Parameter Uncertainty in Bayesian Neural Networks , 2019, ArXiv.

[309]  Guodong Zhang,et al.  Functional Variational Bayesian Neural Networks , 2019, ICLR.

[310]  Congcong Liu,et al.  Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[311]  Jürgen Pilz,et al.  Variational Inference to Measure Model Uncertainty in Deep Neural Networks , 2019, ArXiv.

[312]  James Hensman,et al.  Translation Insensitivity for Deep Convolutional Gaussian Processes , 2019, ArXiv.

[313]  Jonathan P. How,et al.  Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[314]  Hao Wang,et al.  The MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty prediction , 2019, Applied Intelligence.

[315]  Pascal Vincent,et al.  Reducing Uncertainty in Undersampled MRI Reconstruction With Active Acquisition , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[316]  Teruhisa Misu,et al.  Unsupervised Data Uncertainty Learning in Visual Retrieval Systems , 2019, ArXiv.

[317]  Andrew Gordon Wilson,et al.  A Simple Baseline for Bayesian Uncertainty in Deep Learning , 2019, NeurIPS.

[318]  Agustinus Kristiadi,et al.  Predictive Uncertainty Quantification with Compound Density Networks , 2019, ArXiv.

[319]  Alpha A. Lee,et al.  Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning , 2019, Chemical science.

[320]  Kimin Lee,et al.  Using Pre-Training Can Improve Model Robustness and Uncertainty , 2019, ICML.

[321]  Wang Ling,et al.  Variational Smoothing in Recurrent Neural Network Language Models , 2019, ICLR.

[322]  Hao Chen,et al.  Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[323]  José Ignacio Orlando,et al.  U2-Net: A Bayesian U-Net Model With Epistemic Uncertainty Feedback For Photoreceptor Layer Segmentation In Pathological OCT Scans , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[324]  Ling Shao,et al.  Striking the Right Balance With Uncertainty , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[325]  Paris Perdikaris,et al.  Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data , 2019, J. Comput. Phys..

[326]  R. Tan,et al.  Decoupled Certainty-Driven Consistency Loss for Semi-supervised Learning , 2019 .

[327]  Uros Seljak,et al.  Posterior inference unchained with EL_2O , 2019, ArXiv.

[328]  Chao Liu,et al.  Neural RGB®D Sensing: Depth and Uncertainty From a Video Camera , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[329]  Yann LeCun,et al.  Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic , 2019, ICLR.

[330]  Tanmoy Bhattacharya,et al.  The need for uncertainty quantification in machine-assisted medical decision making , 2019, Nat. Mach. Intell..

[331]  Stefano Ermon,et al.  Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization , 2018, AISTATS.

[332]  Balázs Cs. Csáji,et al.  Distribution-free uncertainty quantification for kernel methods by gradient perturbations , 2018, Machine Learning.

[333]  Bin Wang,et al.  Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting , 2018, KDD.

[334]  Qi Wu,et al.  What's to Know? Uncertainty as a Guide to Asking Goal-Oriented Questions , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[335]  Yi Guo,et al.  Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN , 2018, Magnetic resonance in medicine.

[336]  Trevor Darrell,et al.  Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[337]  Omesh Tickoo,et al.  Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[338]  Nassir Navab,et al.  Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control , 2018, NeuroImage.

[339]  William Yang Wang,et al.  Quantifying Uncertainties in Natural Language Processing Tasks , 2018, AAAI.

[340]  Paris Perdikaris,et al.  Adversarial Uncertainty Quantification in Physics-Informed Neural Networks , 2018, J. Comput. Phys..

[341]  David Lopez-Paz,et al.  Single-Model Uncertainties for Deep Learning , 2018, NeurIPS.

[342]  Jonathan P. How,et al.  Safe Reinforcement Learning With Model Uncertainty Estimates , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[343]  Max Welling,et al.  The Deep Weight Prior , 2018, ICLR.

[344]  Sebastian Tschiatschek,et al.  Successor Uncertainties: exploration and uncertainty in temporal difference learning , 2018, NeurIPS.

[345]  Thomas G. Dietterich,et al.  Deep Anomaly Detection with Outlier Exposure , 2018, ICLR.

[346]  Sebastian Nowozin,et al.  Deterministic Variational Inference for Robust Bayesian Neural Networks , 2018, ICLR.

[347]  Alex Beatson,et al.  Amortized Bayesian Meta-Learning , 2018, ICLR.

[348]  Siddhartha S. Srinivasa,et al.  Bayesian Policy Optimization for Model Uncertainty , 2018, ICLR.

[349]  Cho-Jui Hsieh,et al.  Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network , 2018, ICLR.

[350]  Xiangyu Zhang,et al.  Bounding Box Regression With Uncertainty for Accurate Object Detection , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[351]  Ville Kyrki,et al.  Deep Network Uncertainty Maps for Indoor Navigation , 2018, 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids).

[352]  Sébastien Ourselin,et al.  Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks , 2018, Neurocomputing.

[353]  Nicholas Geneva,et al.  Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks , 2018, J. Comput. Phys..

[354]  Jon M. Kleinberg,et al.  Direct Uncertainty Prediction for Medical Second Opinions , 2018, ICML.

[355]  T. Lillicrap,et al.  Noise Contrastive Priors for Functional Uncertainty , 2018, UAI.

[356]  Maurizio Filippone,et al.  Calibrating Deep Convolutional Gaussian Processes , 2018, AISTATS.

[357]  Sebastian Nowozin,et al.  Meta-Learning Probabilistic Inference for Prediction , 2018, ICLR.

[358]  Ran El-Yaniv,et al.  Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers , 2018, ICLR.

[359]  Melih Kandemir,et al.  Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation , 2018, UAI.

[360]  Elena Marchiori,et al.  Adversarial Alignment of Class Prediction Uncertainties for Domain Adaptation , 2019, ICPRAM.

[361]  Finale Doshi-Velez,et al.  A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization , 2018, J. Mach. Learn. Res..

[362]  Dmitry Vetrov,et al.  Variance Networks: When Expectation Does Not Meet Your Expectations , 2018, ICLR.

[363]  Audrey Repetti,et al.  Scalable Bayesian Uncertainty Quantification in Imaging Inverse Problems via Convex Optimization , 2018, SIAM J. Imaging Sci..

[364]  Stan Sclaroff,et al.  Hashing with Mutual Information , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[365]  Christopher K. Wikle,et al.  Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data , 2017, Entropy.

[366]  Shenglan Liu,et al.  Rough extreme learning machine: a new classification method based on uncertainty measure , 2017, Neurocomputing.

[367]  Andreas Dengel,et al.  Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks , 2017, 2019 IEEE International Conference on Image Processing (ICIP).

[368]  Soumya Ghosh,et al.  Model Selection in Bayesian Neural Networks via Horseshoe Priors , 2017, J. Mach. Learn. Res..

[369]  Tim G. J. Rudner,et al.  The Natural Neural Tangent Kernel: Neural Network Training Dynamics under Natural Gradient Descent , 2019 .

[370]  Jingxiao Zheng,et al.  Augmented Deep Representations for Unconstrained Still/Video-based Face Recognition , 2019 .

[371]  Jeremiah Zhe Liu Variable Selection with Rigorous Uncertainty Quantification using Bayesian Deep Neural Networks , 2019 .

[372]  Angelos Filos,et al.  Improving MFVI in Bayesian Neural Networks with Empirical Bayes: a Study with Diabetic Retinopathy Diagnosis , 2019 .

[373]  Sebastian Farquhar Try Depth Instead of Weight Correlations: Mean-field is a Less Restrictive Assumption for Variational Inference in Deep Networks , 2019 .

[374]  Tim Z. Xiao Wat heb je gezegd? Detecting Out-of-Distribution Translations with Variational Transformers , 2019 .

[375]  Moloud Abdar,et al.  A Hybrid Latent Space Data Fusion Method for Multimodal Emotion Recognition , 2019, IEEE Access.

[376]  Markus Heinonen,et al.  ODE2VAE: Deep generative second order ODEs with Bayesian neural networks , 2019, NeurIPS.

[377]  Andrea Vedaldi,et al.  Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels , 2019, NeurIPS.

[378]  Aidan N. Gomez,et al.  Benchmarking Bayesian Deep Learning with Diabetic Retinopathy Diagnosis , 2019 .

[379]  Alexander T. Ihler,et al.  Empirical Study of MC-Dropout in Various Astronomical Observing Conditions , 2019, CVPR Workshops.

[380]  Jonathan Kelly,et al.  Deep Probabilistic Regression of Elements of SO(3) using Quaternion Averaging and Uncertainty Injection , 2019, CVPR Workshops.

[381]  Divyansh Srivastava,et al.  Structured Aleatoric Uncertainty in Human Pose Estimation , 2019, CVPR Workshops.

[382]  Kiyoharu Aizawa,et al.  Multi-Task Learning based on Separable Formulation of Depth Estimation and its Uncertainty , 2019, CVPR Workshops.

[383]  Jishnu Mukhoti,et al.  Evaluating Bayesian Deep Learning Methods for Semantic Segmentation , 2018, ArXiv.

[384]  Jose M. Alvarez,et al.  The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning , 2018, ArXiv.

[385]  Stephen J. Roberts,et al.  BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books , 2018, 1811.10041.

[386]  Jishnu Mukhoti,et al.  On the Importance of Strong Baselines in Bayesian Deep Learning , 2018, ArXiv.

[387]  Aaron Mishkin,et al.  SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient , 2018, NeurIPS.

[388]  Luca Ambrogioni,et al.  Wasserstein Variational Gradient Descent: From Semi-Discrete Optimal Transport to Ensemble Variational Inference , 2018, ArXiv.

[389]  David Lopez-Paz,et al.  Frequentist uncertainty estimates for deep learning , 2018, ArXiv.

[390]  Alois Knoll,et al.  Uncertainty Estimation for Deep Neural Object Detectors in Safety-Critical Applications , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[391]  Boris Flach,et al.  Stochastic Normalizations as Bayesian Learning , 2018, ACCV.

[392]  Richard E. Harang,et al.  Towards Principled Uncertainty Estimation for Deep Neural Networks , 2018, 1810.12278.

[393]  Anastasia Borovykh,et al.  A Gaussian Process perspective on Convolutional Neural Networks , 2018, ArXiv.

[394]  Mohamed Zaki,et al.  Uncertainty in Neural Networks: Bayesian Ensembling , 2018, ArXiv.

[395]  Luigi Acerbi,et al.  Variational Bayesian Monte Carlo , 2018, NeurIPS.

[396]  Max Welling,et al.  Predictive Uncertainty through Quantization , 2018, ArXiv.

[397]  Samuel Kaski,et al.  Deep convolutional Gaussian processes , 2018, ECML/PKDD.

[398]  Remus Pop,et al.  Deep Ensemble Bayesian Active Learning : Addressing the Mode Collapse issue in Monte Carlo dropout via Ensembles , 2018, ArXiv.

[399]  Seong Joon Oh,et al.  Modeling Uncertainty with Hedged Instance Embedding , 2018, ICLR 2018.

[400]  Mateusz Susik,et al.  Inhibited Softmax for Uncertainty Estimation in Neural Networks , 2018, ArXiv.

[401]  Xuan Song,et al.  Online Deep Ensemble Learning for Predicting Citywide Human Mobility , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[402]  Yaniv Gurwicz,et al.  Bayesian Structure Learning by Recursive Bootstrap , 2018, NeurIPS.

[403]  Zachary C. Lipton,et al.  Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study , 2018, EMNLP.

[404]  Doina Precup,et al.  Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation , 2018, MICCAI.

[405]  Marc Pollefeys,et al.  Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors , 2018, ShapeMI@MICCAI.

[406]  Min Sun,et al.  Efficient Uncertainty Estimation for Semantic Segmentation in Videos , 2018, ECCV.

[407]  Yee Whye Teh,et al.  Conditional Neural Processes , 2018, ICML.

[408]  Graham W. Taylor,et al.  Leveraging Uncertainty Estimates for Predicting Segmentation Quality , 2018, ArXiv.

[409]  Stefano Ermon,et al.  Accurate Uncertainties for Deep Learning Using Calibrated Regression , 2018, ICML.

[410]  Christopher K. Wikle,et al.  Deep echo state networks with uncertainty quantification for spatio‐temporal forecasting , 2018, Environmetrics.

[411]  Bernt Schiele,et al.  Bayesian Prediction of Future Street Scenes through Importance Sampling based Optimization , 2018, ArXiv.

[412]  Juan José Murillo-Fuentes,et al.  Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo , 2018, NeurIPS.

[413]  Klaus H. Maier-Hein,et al.  A Probabilistic U-Net for Segmentation of Ambiguous Images , 2018, NeurIPS.

[414]  Didrik Nielsen,et al.  Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam , 2018, ICML.

[415]  Yoshua Bengio,et al.  Bayesian Model-Agnostic Meta-Learning , 2018, NeurIPS.

[416]  Mauricio Reyes,et al.  On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation , 2018, MICCAI.

[417]  Murat Sensoy,et al.  Evidential Deep Learning to Quantify Classification Uncertainty , 2018, NeurIPS.

[418]  Andreas Nürnberger,et al.  The Power of Ensembles for Active Learning in Image Classification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[419]  Bin Xu,et al.  Multi-level Fusion Based 3D Object Detection from Monocular Images , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[420]  Sergey Levine,et al.  Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models , 2018, NeurIPS.

[421]  Silvio Savarese,et al.  Deep Learning Under Privileged Information Using Heteroscedastic Dropout , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[422]  Jordi Vitrià,et al.  Uncertainty Gated Network for Land Cover Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[423]  S. Roth,et al.  Lightweight Probabilistic Deep Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[424]  Rong Jin,et al.  Large-Scale Distance Metric Learning with Uncertainty , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[425]  Eunho Yang,et al.  Uncertainty-Aware Attention for Reliable Interpretation and Prediction , 2018, NeurIPS.

[426]  Marilyn A. Walker,et al.  A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation , 2018, NAACL.

[427]  Michael Kampffmeyer,et al.  UNCERTAINTY MODELING AND INTERPRETABILITY IN CONVOLUTIONAL NEURAL NETWORKS FOR POLYP SEGMENTATION , 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).

[428]  Daniela Rus,et al.  Spatial Uncertainty Sampling for End-to-End Control , 2018, ArXiv.

[429]  Sebastian Nowozin,et al.  Deep Directional Statistics: Pose Estimation with Uncertainty Quantification , 2018, ECCV.

[430]  Maya R. Gupta,et al.  To Trust Or Not To Trust A Classifier , 2018, NeurIPS.

[431]  Pawel Budzianowski,et al.  Benchmarking Uncertainty Estimates with Deep Reinforcement Learning for Dialogue Policy Optimisation , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[432]  Josef Kittler,et al.  Infrared and Visible Image Fusion using a Deep Learning Framework , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[433]  Björn Ommer,et al.  A Variational U-Net for Conditional Appearance and Shape Generation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[434]  Elena Marchiori,et al.  Simple Domain Adaptation with Class Prediction Uncertainty Alignment , 2018, ArXiv.

[435]  M. Jorge Cardoso,et al.  Quality control in radiotherapy-treatment planning using multi-task learning and uncertainty estimation , 2018 .

[436]  Philipp Berens,et al.  Test-time Data Augmentation for Estimation of Heteroscedastic Aleatoric Uncertainty in Deep Neural Networks , 2018 .

[437]  Mauricio Reyes,et al.  Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation , 2018, ArXiv.

[438]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[439]  Andrew Gordon Wilson,et al.  Averaging Weights Leads to Wider Optima and Better Generalization , 2018, UAI.

[440]  Hanno Gottschalk,et al.  Deep Bayesian Active Semi-Supervised Learning , 2018, ICMLA.

[441]  Yarin Gal,et al.  Understanding Measures of Uncertainty for Adversarial Example Detection , 2018, UAI.

[442]  Mark J. F. Gales,et al.  Predictive Uncertainty Estimation via Prior Networks , 2018, NeurIPS.

[443]  Marc'Aurelio Ranzato,et al.  Analyzing Uncertainty in Neural Machine Translation , 2018, ICML.

[444]  Mohamed Zaki,et al.  High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach , 2018, ICML.

[445]  Thomas Brox,et al.  Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow , 2018, ECCV.

[446]  Lourdes Agapito,et al.  Structured Uncertainty Prediction Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[447]  Thomas Brox,et al.  Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks , 2018, ArXiv.

[448]  David Barber,et al.  A Scalable Laplace Approximation for Neural Networks , 2018, ICLR.

[449]  Kevin Smith,et al.  Bayesian Uncertainty Estimation for Batch Normalized Deep Networks , 2018, ICML.

[450]  Dustin Tran,et al.  Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches , 2018, ICLR.

[451]  Jasper Snoek,et al.  Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling , 2018, ICLR.

[452]  Jennifer G. Dy,et al.  Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning , 2018, KDD.

[453]  Dmitry P. Vetrov,et al.  Uncertainty Estimation via Stochastic Batch Normalization , 2018, ICLR.

[454]  Maria A. Zuluaga,et al.  Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning , 2017, IEEE Transactions on Medical Imaging.

[455]  Lawrence Carin,et al.  Learning Structural Weight Uncertainty for Sequential Decision-Making , 2017, AISTATS.

[456]  Jianhong Wang,et al.  Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning , 2017, NeurIPS.

[457]  Andrew M. Stuart,et al.  How Deep Are Deep Gaussian Processes? , 2017, J. Mach. Learn. Res..

[458]  Bernt Schiele,et al.  Long-Term On-board Prediction of People in Traffic Scenes Under Uncertainty , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[459]  Ron Meir,et al.  Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory , 2017, ICML.

[460]  Jaehoon Lee,et al.  Deep Neural Networks as Gaussian Processes , 2017, ICLR.

[461]  Richard E. Turner,et al.  Variational Continual Learning , 2017, ICLR.

[462]  Finale Doshi-Velez,et al.  Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning , 2017, ICML.

[463]  Ian Osband,et al.  The Uncertainty Bellman Equation and Exploration , 2017, ICML.

[464]  Kyungjae Lee,et al.  Uncertainty-Aware Learning from Demonstration Using Mixture Density Networks with Sampling-Free Variance Modeling , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[465]  Lei Zhang,et al.  Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[466]  Lena Maier-Hein,et al.  Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy , 2017, IEEE Transactions on Biomedical Engineering.

[467]  Xin Wang,et al.  IDK Cascades: Fast Deep Learning by Learning not to Overthink , 2017, UAI.

[468]  Roberto Cipolla,et al.  Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[469]  Ioannis Patras,et al.  Linear Maximum Margin Classifier for Learning from Uncertain Data , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[470]  A. G. Wilson,et al.  Fast Uncertainty Estimates and Bayesian Model Averaging of DNNs , 2018 .

[471]  S. Kramer,et al.  Semi-Supervised Bayesian Active Learning for Text Classification , 2018 .

[472]  Remus Pop Deep Ensemble Bayesian Active Learning , 2018 .

[473]  Eric T. Nalisnick Automatic Depth Determination for Bayesian ResNets , 2018 .

[474]  Hanna Tseran Natural Variational Continual Learning , 2018 .

[475]  Saeid Nahavandi,et al.  Neural Network-Based Uncertainty Quantification: A Survey of Methodologies and Applications , 2018, IEEE Access.

[476]  Sam Zimmerman,et al.  Prediction and Uncertainty Quantification of Daily Airport Flight Delays , 2018, PAPIs.

[477]  Siegfried Wahl,et al.  Leveraging uncertainty information from deep neural networks for disease detection , 2016, Scientific Reports.

[478]  Peter Henderson,et al.  Bayesian Policy Gradients via Alpha Divergence Dropout Inference , 2017, ArXiv.

[479]  Benjamin Woodward,et al.  Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection , 2017, ArXiv.

[480]  Garrison W. Cottrell,et al.  Deep-ESN: A Multiple Projection-encoding Hierarchical Reservoir Computing Framework , 2017, ArXiv.

[481]  Ben Glocker,et al.  Implicit Weight Uncertainty in Neural Networks. , 2017 .

[482]  Gabriel Kalweit,et al.  Uncertainty-driven Imagination for Continuous Deep Reinforcement Learning , 2017, CoRL.

[483]  Víctor M. Pérez-García,et al.  Towards Uncertainty-Assisted Brain Tumor Segmentation and Survival Prediction , 2017, BrainLes@MICCAI.

[484]  Carl E. Rasmussen,et al.  Convolutional Gaussian Processes , 2017, NIPS.

[485]  Xiao Yang,et al.  Uncertainty Quantification, Image Synthesis and Deformation Prediction for Image Registration , 2017 .

[486]  Zoubin Ghahramani,et al.  Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks , 2017, 1707.02476.

[487]  José Miguel Hernández-Lobato,et al.  Bayesian Semisupervised Learning with Deep Generative Models , 2017, 1706.09751.

[488]  Jinwoo Shin,et al.  Confident Multiple Choice Learning , 2017, ICML.

[489]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[490]  Vadim Sokolov,et al.  Deep Learning: A Bayesian Perspective , 2017, ArXiv.

[491]  Dennis Shasha,et al.  Crowdsourcing Thousands of Specialized Labels: A Bayesian Active Training Approach , 2017, IEEE Transactions on Multimedia.

[492]  Alex Kendall,et al.  Concrete Dropout , 2017, NIPS.

[493]  Benjamin Van Roy,et al.  Ensemble Sampling , 2017, NIPS.

[494]  Alireza Khorshidi,et al.  Addressing uncertainty in atomistic machine learning. , 2017, Physical chemistry chemical physics : PCCP.

[495]  Marc Peter Deisenroth,et al.  Doubly Stochastic Variational Inference for Deep Gaussian Processes , 2017, NIPS.

[496]  Antonio Criminisi,et al.  Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution , 2017, MICCAI.

[497]  Lawrence Carin,et al.  Learning Structured Weight Uncertainty in Bayesian Neural Networks , 2017, AISTATS.

[498]  Oriol Vinyals,et al.  Bayesian Recurrent Neural Networks , 2017, ArXiv.

[499]  Kun He,et al.  MIHash: Online Hashing with Mutual Information , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[500]  Yu Xue,et al.  Measures of uncertainty for neighborhood rough sets , 2017, Knowl. Based Syst..

[501]  Alex Kendall,et al.  What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.

[502]  Zoubin Ghahramani,et al.  Deep Bayesian Active Learning with Image Data , 2017, ICML.

[503]  Max Welling,et al.  Multiplicative Normalizing Flows for Variational Bayesian Neural Networks , 2017, ICML.

[504]  Chee Peng Lim,et al.  A Q-learning-based multi-agent system for data classification , 2017, Appl. Soft Comput..

[505]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[506]  Sergey Levine,et al.  Uncertainty-Aware Reinforcement Learning for Collision Avoidance , 2017, ArXiv.

[507]  Charles Blundell,et al.  Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.

[508]  Yash Goyal,et al.  Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering , 2016, International Journal of Computer Vision.

[509]  Bastian Leibe,et al.  Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[510]  Dustin Tran,et al.  Automatic Differentiation Variational Inference , 2016, J. Mach. Learn. Res..

[511]  Roberto Cipolla,et al.  Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding , 2015, BMVC.

[512]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[513]  Martin Wistuba,et al.  Harnessing Model Uncertainty for Detecting Adversarial Examples , 2017 .

[514]  Bjarne Foss,et al.  Echo State Networks for data-driven downhole pressure estimation in gas-lift oil wells , 2017, Neural Networks.

[515]  Aaron Klein,et al.  Bayesian Optimization with Robust Bayesian Neural Networks , 2016, NIPS.

[516]  Nikolaus Kriegeskorte,et al.  Robustly representing inferential uncertainty in deep neural networks through sampling , 2016, 1611.01639.

[517]  Michael Kampffmeyer,et al.  Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[518]  Zhe Gan,et al.  Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[519]  Jiasen Lu,et al.  Hierarchical Question-Image Co-Attention for Visual Question Answering , 2016, NIPS.

[520]  Trevor Campbell,et al.  Coresets for Scalable Bayesian Logistic Regression , 2016, NIPS.

[521]  Filip De Turck,et al.  VIME: Variational Information Maximizing Exploration , 2016, NIPS.

[522]  Nirmalya Roy,et al.  Active learning enabled activity recognition , 2016, 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[523]  Dit-Yan Yeung,et al.  Towards Bayesian Deep Learning: A Survey , 2016, ArXiv.

[524]  Max Welling,et al.  Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors , 2016, ICML.

[525]  Soumya Ghosh,et al.  Assumed Density Filtering Methods for Learning Bayesian Neural Networks , 2016, AAAI.

[526]  Jen-Tzung Chien,et al.  Bayesian Recurrent Neural Network for Language Modeling , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[527]  David M. Blei,et al.  Variational Inference: A Review for Statisticians , 2016, ArXiv.

[528]  Daniel Hernández-Lobato,et al.  Black-Box Alpha Divergence Minimization , 2015, ICML.

[529]  Roberto Cipolla,et al.  Modelling uncertainty in deep learning for camera relocalization , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[530]  Zoubin Ghahramani,et al.  Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.

[531]  Yee Whye Teh,et al.  Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics , 2014, J. Mach. Learn. Res..

[532]  R. Loftin Problems for Online Bayesian Inference in Neural Networks , 2016 .

[533]  Yarin Gal,et al.  Uncertainty in Deep Learning , 2016 .

[534]  Lawrence Carin,et al.  On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators , 2015, NIPS.

[535]  Hua Xu,et al.  A study of active learning methods for named entity recognition in clinical text , 2015, J. Biomed. Informatics.

[536]  Shie Mannor,et al.  Bayesian Reinforcement Learning: A Survey , 2015, Found. Trends Mach. Learn..

[537]  Vincze Veronika,et al.  Uncertainty Detection in Natural Language Texts , 2015 .

[538]  Vivek Rathod,et al.  Bayesian dark knowledge , 2015, NIPS.

[539]  Zoubin Ghahramani,et al.  Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference , 2015, ArXiv.

[540]  Julien Cornebise,et al.  Weight Uncertainty in Neural Networks , 2015, ArXiv.

[541]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[542]  Margaret Mitchell,et al.  VQA: Visual Question Answering , 2015, International Journal of Computer Vision.

[543]  Yann LeCun,et al.  The Loss Surfaces of Multilayer Networks , 2014, AISTATS.

[544]  Jonathan Tompson,et al.  Efficient object localization using Convolutional Networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[545]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[546]  Max Welling,et al.  Markov Chain Monte Carlo and Variational Inference: Bridging the Gap , 2014, ICML.

[547]  Masashi Sugiyama,et al.  Bayesian Dark Knowledge , 2015 .

[548]  Stephen J. Roberts,et al.  Bayesian Methods for Intelligent Task Assignment in Crowdsourcing Systems , 2015, Decision Making.

[549]  Ryan Babbush,et al.  Bayesian Sampling Using Stochastic Gradient Thermostats , 2014, NIPS.

[550]  Elisabetta Fersini,et al.  Sentiment analysis: Bayesian Ensemble Learning , 2014, Decis. Support Syst..

[551]  Ryan P. Adams,et al.  Avoiding pathologies in very deep networks , 2014, AISTATS.

[552]  Tianqi Chen,et al.  Stochastic Gradient Hamiltonian Monte Carlo , 2014, ICML.

[553]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[554]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[555]  Philip S. Yu,et al.  Active Learning: A Survey , 2014, Data Classification: Algorithms and Applications.

[556]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[557]  Neil D. Lawrence,et al.  Deep Gaussian Processes , 2012, AISTATS.

[558]  Yiyu Yao,et al.  An Outline of a Theory of Three-Way Decisions , 2012, RSCTC.

[559]  Zoubin Ghahramani,et al.  Bayesian Active Learning for Classification and Preference Learning , 2011, ArXiv.

[560]  Yee Whye Teh,et al.  Bayesian Learning via Stochastic Gradient Langevin Dynamics , 2011, ICML.

[561]  Grigorios Tsoumakas,et al.  An ensemble uncertainty aware measure for directed hill climbing ensemble pruning , 2010, Machine Learning.

[562]  A. Kiureghian,et al.  Aleatory or epistemic? Does it matter? , 2009 .

[563]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[564]  Ruslan Salakhutdinov,et al.  Bayesian probabilistic matrix factorization using Markov chain Monte Carlo , 2008, ICML '08.

[565]  Tom M. Mitchell,et al.  The Need for Biases in Learning Generalizations , 2007 .

[566]  Y. Ben-Haim Info-Gap Decision Theory: Decisions Under Severe Uncertainty , 2006 .

[567]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[568]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[569]  H. Barrett,et al.  Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[570]  Daphne Koller,et al.  Active learning: theory and applications , 2001 .

[571]  A. Rukhin Matrix Variate Distributions , 1999, The Multivariate Normal Distribution.

[572]  Huaiyu Zhu On Information and Sufficiency , 1997 .

[573]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[574]  S. Srihari Mixture Density Networks , 1994 .

[575]  A. Kennedy,et al.  Hybrid Monte Carlo , 1988 .

[576]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[577]  Hoover,et al.  Canonical dynamics: Equilibrium phase-space distributions. , 1985, Physical review. A, General physics.

[578]  S. Nosé A unified formulation of the constant temperature molecular dynamics methods , 1984 .

[579]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[580]  H. Robbins An Empirical Bayes Approach to Statistics , 1956 .