暂无分享,去创建一个
Li Liu | Xiaochun Cao | Abbas Khosravi | Saeid Nahavandi | Moloud Abdar | U Rajendra Acharya | Mohammad Ghavamzadeh | Dana Rezazadegan | Vladimir Makarenkov | Farhad Pourpanah | Sadiq Hussain | Paul Fieguth | M. Ghavamzadeh | P. Fieguth | Xiaochun Cao | S. Nahavandi | U. Acharya | A. Khosravi | V. Makarenkov | M. Abdar | Farhad Pourpanah | Sadiq Hussain | D. Rezazadegan | Li Liu | Moloud Abdar | Dana Rezazadegan
[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 .