Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study | Paper Citation Record

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This paper cites Deep ensembles work, but are they necessary? Advances in Neural Information Processing Systems, 35: 0 33646--33660, 2022.

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Deep ensembles work, but are they necessary? Advances in Neural Information Processing Systems, 35: 0 33646--33660, 2022

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study A non-specificity measure for convex sets of probability distributions

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Disaggregated total uncertainty measure for credal sets

Reference 3

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Benchmarking Bayesian deep learning on diabetic retinopathy detection tasks

Reference 4

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This paper cites From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge.

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge

Reference 5

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Weight uncertainty in neural network

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Credal Bayesian deep learning

Reference 7

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This paper cites u gner, and Stephan G \.

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study u gner, and Stephan G \

Reference 8

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This paper cites Integral imprecise probability metrics.

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Integral imprecise probability metrics

Reference 9

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This paper cites Credal two-sample tests of epistemic uncertainty.

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Credal two-sample tests of epistemic uncertainty

Reference 10

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This paper cites Quantifying epistemic predictive uncertainty in conformal prediction.

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Quantifying epistemic predictive uncertainty in conformal prediction

Reference 11

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Bayesian networks with imprecise probabilities: Theory and application to classification

Reference 12

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This paper cites De Campos, Juan F.

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study De Campos, Juan F

Reference 13

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This paper cites Representing partial ignorance.

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Representing partial ignorance

Reference 14

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Masksembles for uncertainty estimation

Reference 15

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Dropout as a Bayesian approximation: Representing model uncertainty in deep learning

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Practical variational inference for neural networks

Reference 17

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This paper cites Transmission of information 1.

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Transmission of information 1

Reference 18

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Deep residual learning for image recognition

Reference 19

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study u hn and Eyke H \

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This paper cites Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods.

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods

Reference 21

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This paper cites Quantification of credal uncertainty in machine learning: A critical analysis and empirical comparison.

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Quantification of credal uncertainty in machine learning: A critical analysis and empirical comparison

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Unresolved cited work

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Hands-on Bayesian neural networks—a tutorial for deep learning users

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study A generalized deep learning framework for whole-slide image segmentation and analysis

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study CIFAR-10 (Canadian Institute For Advanced Research)

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Bayesian Hypernetworks

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Simple and scalable predictive uncertainty estimation using deep ensembles

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Packed ensembles for efficient uncertainty estimation

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study The enterprise of knowledge: An essay on knowledge, credal probability, and chance

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Position: Supervised classifiers answer the wrong questions for ood detection

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study o hr, Paul Hofman, Felix Mohr, and Eyke H \

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Predictive uncertainty estimation via prior networks

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study A unified evaluation framework for epistemic predictions

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Random-set neural networks

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Dropconnect is effective in modeling uncertainty of Bayesian deep networks

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Benchmarking uncertainty disentanglement: Specialized uncertainties for specialized tasks

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Deep deterministic uncertainty: A new simple baseline

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Second-order uncertainty quantification: A distance-based approach

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study o hr, Lisa Wimmer, Thomas Nagler, and Eyke H \

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Ensemble-based uncertainty quantification: Bayesian versus credal inference

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Uncertainty quantification for Bayesian optimization

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Credal deep ensembles for uncertainty quantification

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Credal ensemble distillation for uncertainty quantification, 2025 a

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Credal wrapper of model averaging for uncertainty estimation in classification

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study CreINNs : Credal-set interval neural networks for uncertainty estimation in classification tasks

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study A review of uncertainty representation and quantification in neural networks

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Learning Credal Ensembles via Distributionally Robust Optimization

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Navigating the waters of object detection: Evaluating the robustness of real-time object detection models for autonomous surface vehicles

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Enhancing the dependability of autonomous surface vehicles through robustness benchmarking of real-time object detection models

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Bayesian deep learning and a probabilistic perspective of generalization

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Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Learning from the wisdom of crowds by minimax entropy

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