Paper Citation Record · LEDGER

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks

As of 8 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2506.00918.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.00918 v1

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measured 43 of 43 reference resolution

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43 of 43 outbound references displayed

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Outbound references

Observation db6e3d29-2fc1-4c8a-aa66-609eea6a98e3 · outbound

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

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Deep ensembles work, but are they necessary? Advances in Neural Information Processing Systems, 35: 0 33646--33660, 2022

Reference 1

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This paper cites Deep evidential regression.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Deep evidential regression

Reference 2

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This paper cites Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks

Reference 3

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Observation 77572213-0e50-4b38-927b-02b4b4480268 · outbound

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

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks The need for uncertainty quantification in machine-assisted medical decision making

Reference 4

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Observation 59781e63-57ef-45c2-91c5-5a31f0e73961 · outbound

This paper cites Pitfalls of epistemic uncertainty quantification through loss minimisation.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Pitfalls of epistemic uncertainty quantification through loss minimisation

Reference 5

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Observation 750e4f78-a313-4280-82bd-ef0d3ea2de21 · outbound

This paper cites A Conceptual Introduction to Hamiltonian Monte Carlo.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks A Conceptual Introduction to Hamiltonian Monte Carlo

Reference 6

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This paper cites Weight uncertainty in neural network.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Weight uncertainty in neural network

Reference 7

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Observation ae4e5722-f0d4-4481-9cbe-1ae6bb6071c8 · outbound

This paper cites Plausible uncertainties for human pose regression.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Plausible uncertainties for human pose regression

Reference 8

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Observation c749340e-7b7f-4e0f-92e3-d49e64badba8 · outbound

This paper cites Heteroscedastic kernel ridge regression.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Heteroscedastic kernel ridge regression

Reference 9

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This paper cites Stochastic gradient hamiltonian monte carlo.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Stochastic gradient hamiltonian monte carlo

Reference 10

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Observation b4bff363-74f2-45b0-8842-08ab2d5b1a1c · outbound

This paper cites Repulsive deep ensembles are bayesian.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Repulsive deep ensembles are bayesian

Reference 11

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This paper cites Laplace redux-effortless bayesian deep learning.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Laplace redux-effortless bayesian deep learning

Reference 12

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This paper cites Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning

Reference 13

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This paper cites Aleatory or epistemic? does it matter? Structural safety, 31 0 (2): 0 105--112, 2009.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Aleatory or epistemic? does it matter? Structural safety, 31 0 (2): 0 105--112, 2009

Reference 14

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This paper cites Reliable training and estimation of variance networks.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Reliable training and estimation of variance networks

Reference 15

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This paper cites O zg \"u n C i c ek, Ahmed Abdulkadir, Yassine Marrakchi, Anton B \.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks O zg \"u n C i c ek, Ahmed Abdulkadir, Yassine Marrakchi, Anton B \

Reference 16

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Observation 71a098ab-9a9e-4b83-9796-2d5a3938608d · outbound

This paper cites Is MC Dropout Bayesian?.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Is MC Dropout Bayesian?

Reference 17

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This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 18

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Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Strictly proper scoring rules, prediction, and estimation

Reference 19

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This paper cites Probabilistic backpropagation for scalable learning of bayesian neural networks.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Probabilistic backpropagation for scalable learning of bayesian neural networks

Reference 20

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This paper cites Revisiting single image depth estimation: Toward higher resolution maps with accurate object boundaries.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Revisiting single image depth estimation: Toward higher resolution maps with accurate object boundaries

Reference 21

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Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks The apolloscape dataset for autonomous driving

Reference 22

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This paper cites o lkopf, Peter B \.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks o lkopf, Peter B \

Reference 23

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Observation c0c5ddd3-62b5-45e7-b474-817a7b2aa6db · outbound

This paper cites Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?

Reference 24

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This paper cites What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017

Reference 25

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Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Accurate uncertainties for deep learning using calibrated regression

Reference 26

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This paper cites DEUP: Direct Epistemic Uncertainty Prediction.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks DEUP: Direct Epistemic Uncertainty Prediction

Reference 27

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Observation db98f22c-a784-49dc-84c3-0a54d4960d2d · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 28

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Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Heteroscedastic gaussian process regression

Reference 29

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Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Dropout injection at test time for post hoc uncertainty quantification in neural networks

Reference 30

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Observation d3b2da9a-f118-4115-b9d2-434a8263efde · outbound

This paper cites A practical bayesian framework for backpropagation networks.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks A practical bayesian framework for backpropagation networks

Reference 31

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This paper cites A simple baseline for bayesian uncertainty in deep learning.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks A simple baseline for bayesian uncertainty in deep learning

Reference 32

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Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks The unreasonable effectiveness of deep evidential regression

Reference 33

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Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Mcmc using hamiltonian dynamics

Reference 34

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Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Estimating the mean and variance of the target probability distribution

Reference 35

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Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks A scalable laplace approximation for neural networks

Reference 36

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This paper cites Second-Order Uncertainty Quantification: Variance-Based Measures.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Second-Order Uncertainty Quantification: Variance-Based Measures

Reference 37

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This paper cites On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks

Reference 38

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This paper cites Evidential deep learning to quantify classification uncertainty.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Evidential deep learning to quantify classification uncertainty

Reference 39

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This paper cites Indoor segmentation and support inference from rgbd images.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Indoor segmentation and support inference from rgbd images

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:01:38.710168Z

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This paper cites Bayescap: Bayesian identity cap for calibrated uncertainty in frozen neural networks.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Bayescap: Bayesian identity cap for calibrated uncertainty in frozen neural networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:01:38.416567Z

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Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Unresolved cited work

Reference 42

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Observation 506a0995-b365-4bf5-a0b0-8b4b9a441f1b · outbound

This paper cites Doubly penalized likelihood estimator in heteroscedastic regression.

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks Doubly penalized likelihood estimator in heteroscedastic regression

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:01:38.051799Z

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