Paper Citation Record · LEDGER

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation

As of 14 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:2412.07169.

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

pith.paper-citation-record.v1
2412.07169 v4

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Pith citing papers itemized under the disclosed page cap.

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A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T11:26:03.159725Z

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation 899d722b-151e-4ac7-80fc-90eb7754fa90 · outbound

This paper cites Information dropout: Learning optimal representations through noisy computa- tion.IEEE transactions on pattern analysis and machine intelligence, 40(12):2897–2905, 2018.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Information dropout: Learning optimal representations through noisy computa- tion.IEEE transactions on pattern analysis and machine intelligence, 40(12):2897–2905, 2018

Reference 1

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Observation 69177c54-b898-461f-b3ae-388bfee9ada3 · outbound

This paper cites The medical segmentation decathlon.Nature communications, 13(1):4128, 2022.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation The medical segmentation decathlon.Nature communications, 13(1):4128, 2022

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:32.540096Z

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Observation 421d21d3-ad54-48e9-a94f-27d08e703cac · outbound

This paper cites Adaptive dropout for training deep neural networks.Advances in neural information pro- cessing systems, 26, 2013.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Adaptive dropout for training deep neural networks.Advances in neural information pro- cessing systems, 26, 2013

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:32.455580Z

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Observation e99bc180-c72b-4178-b18b-524ceeabd3c5 · outbound

This paper cites The need for uncertainty quantification in machine- assisted medical decision making.Nature Machine Intelli- gence, 1(1):20–23, 2019.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation The need for uncertainty quantification in machine- assisted medical decision making.Nature Machine Intelli- gence, 1(1):20–23, 2019

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:32.440056Z

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Observation 158b29c9-8098-4a12-8a11-34fec6220292 · outbound

This paper cites MINE: Mutual Information Neural Estimation.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation MINE: Mutual Information Neural Estimation

Reference 5

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Observation 96b7cf7e-380d-4321-a88b-c11cf8bc65ce · outbound

This paper cites Weight uncertainty in neural network.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Weight uncertainty in neural network

Reference 6

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Observation 46763820-6f99-4b62-b8c6-ccfe2f52b33a · outbound

This paper cites Adaptive Estimators Show Information Compression in Deep Neural Networks.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Adaptive Estimators Show Information Compression in Deep Neural Networks

Reference 7

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Observation fe157901-6837-44c0-81ec-cc79886148b3 · outbound

This paper cites John Wiley & Sons, 1999.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation John Wiley & Sons, 1999

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:32.415915Z

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Observation 06bc36d6-1ae0-4243-b028-8a111aa76ba7 · outbound

This paper cites MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions

Reference 9

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Observation 7801702d-414c-457f-8b63-61fb006fe92e · outbound

This paper cites Masksembles for uncertainty estimation.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Masksembles for uncertainty estimation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:32.274112Z

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Observation 7c6b1472-e23a-4f63-a22b-682596a393ed · outbound

This paper cites Mc layer normalization for calibrated uncertainty in deep learn- ing.Transactions on Machine Learning Research, 2024.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Mc layer normalization for calibrated uncertainty in deep learn- ing.Transactions on Machine Learning Research, 2024

Reference 11

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Observation 1b4e4f83-e8f8-4c40-9e85-4425e8656249 · outbound

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

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 12

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Observation 899fbaf8-89bc-4e1a-a30e-094dadb5c2b9 · outbound

This paper cites Concrete dropout.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Concrete dropout

Reference 13

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Observation 75fddebe-35db-48e5-acc1-e594bb5c29da · outbound

This paper cites Uncertainty in deep learning.phd thesis,.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Uncertainty in deep learning.phd thesis,

Reference 14

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Observation 30d06853-d850-4094-9417-ce158ad02305 · outbound

This paper cites Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers

Reference 15

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Observation 39c2cc54-1259-42b1-bdd3-8fbe58efff6b · outbound

This paper cites On calibration of modern neural networks.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation On calibration of modern neural networks

Reference 16

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Observation 3b5e2dec-569a-4123-95c7-814c7a5923dc · outbound

This paper cites Deep residual learning for image recognition.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Deep residual learning for image recognition

Reference 17

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Observation 73588f86-220b-4c7a-a9e4-b70a1931adfd · outbound

This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 18

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Observation b958ac11-0af4-4a17-b557-72ae011f9269 · outbound

This paper cites Imagenet object localization challenge.Kaggle.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Imagenet object localization challenge.Kaggle

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:31.885708Z

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Observation ec814c79-f0a9-41b3-b5f6-891a2eb6ba0b · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation.Nature methods, 18(2):203–211, 2021.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation.Nature methods, 18(2):203–211, 2021

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:31.865735Z

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Observation 734a97f0-482e-4c96-b86c-c9a81348f1c3 · outbound

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

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation What uncertainties do we need in bayesian deep learning for computer vision?Advances in neural information processing systems, 30, 2017

Reference 21

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Observation 6614401f-71c8-418f-9dad-6e8aacb34483 · outbound

This paper cites Varia- tional dropout and the local reparameterization trick.Ad- vances in neural information processing systems, 28, 2015.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Varia- tional dropout and the local reparameterization trick.Ad- vances in neural information processing systems, 28, 2015

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:31.840390Z

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Observation acbb01b3-3dea-48c9-883b-1d30f5f6b8a0 · outbound

This paper cites Improving model calibration with accuracy versus uncertainty optimization.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Improving model calibration with accuracy versus uncertainty optimization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:31.717626Z

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Observation 6b84cd5a-e4b0-4729-932d-2a1a96468ecc · outbound

This paper cites Simple and scalable predictive uncertainty estima- tion using deep ensembles.Advances in neural information processing systems, 30, 2017.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Simple and scalable predictive uncertainty estima- tion using deep ensembles.Advances in neural information processing systems, 30, 2017

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:31.625545Z

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Observation 9dda301a-96b7-4b6c-a36e-fdac7b24ab30 · outbound

This paper cites Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference

Reference 25

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Observation df8ee1ca-cd0b-40bf-aa6e-631f92f782f8 · outbound

This paper cites Dropout injection at test time for post hoc uncertainty quantifica- tion in neural networks.Information Sciences, 645:119356,.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Dropout injection at test time for post hoc uncertainty quantifica- tion in neural networks.Information Sciences, 645:119356,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:31.612363Z

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Observation 13e6c5c5-0897-4649-81a3-58a10dbaa133 · outbound

This paper cites Boundary-aware uncertainty suppression for semi-supervised medical image segmenta- tion.IEEE Transactions on Artificial Intelligence, 5(8): 4074–4086, 2024.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Boundary-aware uncertainty suppression for semi-supervised medical image segmenta- tion.IEEE Transactions on Artificial Intelligence, 5(8): 4074–4086, 2024

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:31.545831Z

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Observation ba16efba-3323-401c-b04b-1f522c356d2b · outbound

This paper cites Confidence calibration and predictive uncertainty estimation for deep medical im- age segmentation.IEEE transactions on medical imaging, 39(12):3868–3878, 2020.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Confidence calibration and predictive uncertainty estimation for deep medical im- age segmentation.IEEE transactions on medical imaging, 39(12):3868–3878, 2020

Reference 28

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Observation 68876716-dd0f-4757-b8a0-4e045c03a59c · outbound

This paper cites Training-free uncertainty estimation for dense re- gression: Sensitivity as a surrogate.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Training-free uncertainty estimation for dense re- gression: Sensitivity as a surrogate

Reference 29

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This paper cites Dropconnect is effective in modeling uncertainty of bayesian deep networks.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Dropconnect is effective in modeling uncertainty of bayesian deep networks

Reference 30

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This paper cites Evaluating Bayesian Deep Learning Methods for Semantic Segmentation.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Evaluating Bayesian Deep Learning Methods for Semantic Segmentation

Reference 31

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This paper cites Accuracy-rejection curves (arcs) for com- paring classification methods with a reject option.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Accuracy-rejection curves (arcs) for com- paring classification methods with a reject option

Reference 32

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This paper cites Risk versus uncertainty in deep learning: Bayes, bootstrap and the dangers of dropout.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Risk versus uncertainty in deep learning: Bayes, bootstrap and the dangers of dropout

Reference 33

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Pytorch: An imperative style, high-performance deep learning library

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This paper cites An- nealed dropout training of deep networks.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation An- nealed dropout training of deep networks

Reference 35

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This paper cites Information Flow in Deep Neural Networks.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Information Flow in Deep Neural Networks

Reference 36

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This paper cites Opening the Black Box of Deep Neural Networks via Information.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Opening the Black Box of Deep Neural Networks via Information

Reference 37

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This paper cites Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1):1929–1958, 2014.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1):1929–1958, 2014

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:30.854665Z

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This paper cites Bayesian uncertainty estimation for batch normalized deep networks.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Bayesian uncertainty estimation for batch normalized deep networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:30.838215Z

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This paper cites Regularization of neural networks using drop- connect.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Regularization of neural networks using drop- connect

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:30.738220Z

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Observation 9f10e1fb-dcfe-49fe-8ef4-2b3a9154996a · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004

Reference 41

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Observation 10dd2c4d-3341-4d29-a31d-8284285923e6 · outbound

This paper cites Medm- nist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification.Scientific Data, 10(1):41,.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Medm- nist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification.Scientific Data, 10(1):41,

Reference 42

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This paper cites Boundary un- certainty aware network for automated polyp segmentation.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Boundary un- certainty aware network for automated polyp segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:30.568050Z

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Observation f6bc9dee-0190-470d-adbd-291e9013a161 · outbound

This paper cites Reproducibility Statement The Rate-In algorithm code and implementation examples are available in theGitHub repository 1.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Reproducibility Statement The Rate-In algorithm code and implementation examples are available in theGitHub repository 1

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:30.471812Z

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Observation f6ae8a0c-759e-44ce-b240-6fe481fecba4 · outbound

This paper cites Synthetic Data Figures 8 and 9 evaluate Rate-In dropout’s performance.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Synthetic Data Figures 8 and 9 evaluate Rate-In dropout’s performance

Reference 46

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T19:08:30.442674Z

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Observation ab5faa3e-3494-4930-865b-1548cd82ce3e · outbound

This paper cites This perspective shift allows us to examine how dropout affects network representations in task-specific contexts.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation This perspective shift allows us to examine how dropout affects network representations in task-specific contexts

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:30.388182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c993d19f-b1c1-416e-a669-ca4d211e7dff · outbound

This paper cites Figure 5.

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Figure 5

Reference 123

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:30.457094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 12c7e482-65f4-4f0f-b506-c3e3cddd69db · inbound

Monte Carlo Stochastic Depth for Uncertainty Estimation in Deep Learning cites this paper.

Monte Carlo Stochastic Depth for Uncertainty Estimation in Deep Learning Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:26:03.169344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.