Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning | Paper Citation Record

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This paper cites The need for uncertainty quantification in machine-assisted medical decision making.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning The need for uncertainty quantification in machine-assisted medical decision making

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This paper cites Pitfalls of epistemic uncer- tainty quantification through loss minimisation.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Pitfalls of epistemic uncer- tainty quantification through loss minimisation

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning On second-order scoring rules for epistemic uncertainty quantification

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning A general framework for updating belief distributions

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Weight uncertainty in neural network

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This paper cites Density-regression: Efficient and distance-aware deepregressorforuncertaintyestimationunderdistributionshifts.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Density-regression: Efficient and distance-aware deepregressorforuncertaintyestimationunderdistributionshifts

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This paper cites Posterior network: un- certainty estimation without OOD samples via density-based pseudo-counts.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Posterior network: un- certainty estimation without OOD samples via density-based pseudo-counts

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This paper cites R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning

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This paper cites R-edl: Relaxing nonessential settings of evidential deep learning.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning R-edl: Relaxing nonessential settings of evidential deep learning

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This paper cites Deep Learning for Classical Japanese Literature.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Deep Learning for Classical Japanese Literature

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This paper cites Uncertainty Estimation by Fisher Information-based Evidential Deep Learning.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Uncertainty Estimation by Fisher Information-based Evidential Deep Learning

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Uncertainty estimation by fisher information-based evidential deep learning

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This paper cites The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web].

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]

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This paper cites nflows: nor- 16DIP–Evidential Deep Learning malizing flows in PyTorch.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning nflows: nor- 16DIP–Evidential Deep Learning malizing flows in PyTorch

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This paper cites Self-distribution distillation: efficient un- certainty estimation.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Self-distribution distillation: efficient un- certainty estimation

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This paper cites Dropout as a bayesian approximation: Repre- sentingmodeluncertaintyindeeplearning.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Dropout as a bayesian approximation: Repre- sentingmodeluncertaintyindeeplearning

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This paper cites Bayesian uncertainty quantification for machine-learned models in physics.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Bayesian uncertainty quantification for machine-learned models in physics

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This paper cites A comprehensive survey on evi- dential deep learning and its applications.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning A comprehensive survey on evi- dential deep learning and its applications

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This paper cites Conformal prediction with conditionalguarantees.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Conformal prediction with conditionalguarantees

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This paper cites On calibration of modern neural networks.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning On calibration of modern neural networks

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This paper cites Discriminant Analysis by Gaussian Mix- tures.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Discriminant Analysis by Gaussian Mix- tures

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This paper cites Bayesian Evidential Deep Learning with PAC Regularization.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Bayesian Evidential Deep Learning with PAC Regularization

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Deep Residual Learning for Image Recognition

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning A survey on uncertainty quantification methods for deep learning

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Probabilistic backpropagation for scalable learning of bayesian neural networks

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Distilling the Knowledge in a Neural Network

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This paper cites The effects of communicating uncertainty around statistics, on public trust.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning The effects of communicating uncertainty around statistics, on public trust

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Learning Multiple Layers of Features from Tiny Images

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Learning multiple layers of features from tiny images.(2009)

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Human-level con- cept learning through probabilistic program induction

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Simple and scal- able predictive uncertainty estimation using deep ensembles

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Gradient-based learning applied to document recognition

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning As Confidence Aligns: Understanding the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Predictive uncertainty estimation via prior networks

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Reverse kl-divergence training of prior networks: Improved uncertainty and adversarial robustness

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Ensemble Distribution Distillation

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Amortized Variational Inference: When and Why?

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Benchmarking uncertainty dis- entanglement: Specialized uncertainties for specialized tasks

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Deep Deterministic Uncertainty: A Simple Baseline

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Reading Digits in Natural Images with Unsupervised Feature Learning

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Masked Autoregressive Flow for Density Estimation

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This paper cites (2019).PyTorch: an imperative style, high-performance deep learning library.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning (2019).PyTorch: an imperative style, high-performance deep learning library

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning The effects of communicating scientific uncertainty on trust and decision making in a public health context

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Beyond deep ensembles: A large-scale evaluation of bayesian deep learning under distribution shift

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Uncertainty-aware deep classifiers using generative models

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Evidential deep learning to quantify classification uncertainty

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Are uncertainty quantification capabilities of evidential deep learning a mirage?

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Information robust dirichlet networks for predictive un- certainty estimation

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Pseudo-likelihoods for Bayesian inference

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Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Diversity-enhanced probabilistic ensemble for un- certainty estimation

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This paper cites Uncertainty estimation by density aware evidential deep learning.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Uncertainty estimation by density aware evidential deep learning

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This paper cites Quantifying Classification Uncertainty using Regularized Evidential Neural Networks.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning Quantifying Classification Uncertainty using Regularized Evidential Neural Networks

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This paper cites [47] for a comprehensive overview of existing limitations.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning [47] for a comprehensive overview of existing limitations

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This paper cites The flow consists 30DIP–Evidential Deep Learning Table 4: LeNet-5 implementation details for MNIST.

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning The flow consists 30DIP–Evidential Deep Learning Table 4: LeNet-5 implementation details for MNIST

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