Confidence Calibration of Deep Learning Systems | Paper Citation Record

Observation 8cdbcfdd-aeb0-4bce-aa8f-4066c286ce8b · outbound

This paper cites Conformal prediction: A gentle introduction.

Confidence Calibration of Deep Learning Systems Conformal prediction: A gentle introduction

Reference 1

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This paper cites Uncertainty sets for image classifiers using conformal prediction.

Confidence Calibration of Deep Learning Systems Uncertainty sets for image classifiers using conformal prediction

Reference 2

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This paper cites Private Prediction Sets.

Confidence Calibration of Deep Learning Systems Private Prediction Sets

Reference 3

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This paper cites Learning with privacy at scale, 2017.

Confidence Calibration of Deep Learning Systems Learning with privacy at scale, 2017

Reference 4

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This paper cites Private learning and sanitization: Pure vs.

Confidence Calibration of Deep Learning Systems Private learning and sanitization: Pure vs

Reference 5

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Observation a79dffea-a79d-410d-bd04-1f3ffa8ee948 · outbound

This paper cites Training deep neural-networks based on unreli- able labels.

Confidence Calibration of Deep Learning Systems Training deep neural-networks based on unreli- able labels

Reference 6

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This paper cites Verification of forecasts expressed in terms of probability.Monthly Weather Review, 78(1):1–3, 1950.

Confidence Calibration of Deep Learning Systems Verification of forecasts expressed in terms of probability.Monthly Weather Review, 78(1):1–3, 1950

Reference 7

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This paper cites AnomMAN: Detect Anomaly on Multi-view Attributed Networks.

Confidence Calibration of Deep Learning Systems AnomMAN: Detect Anomaly on Multi-view Attributed Networks

Reference 8

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Observation b75caa72-faf9-4386-a8ee-7eb477d37f51 · outbound

This paper cites Noise against noise: stochastic label noise helps combat inherent label noise.

Confidence Calibration of Deep Learning Systems Noise against noise: stochastic label noise helps combat inherent label noise

Reference 9

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Observation 26cc1e37-59ef-440c-8085-419705859bf2 · outbound

This paper cites Instance-dependent label-noise learning with manifold-regularized transition matrix estimation.

Confidence Calibration of Deep Learning Systems Instance-dependent label-noise learning with manifold-regularized transition matrix estimation

Reference 10

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Observation 1c033d79-f0ed-4e84-84b3-da61acaafd39 · outbound

This paper cites Learning with instance-dependent label noise: A sample sieve approach.

Confidence Calibration of Deep Learning Systems Learning with instance-dependent label noise: A sample sieve approach

Reference 11

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Observation 2465f15e-5bfd-4c75-9c4d-d876ddf2f147 · outbound

This paper cites Differential privacy in the shuffle model: A survey of separations, 2022.

Confidence Calibration of Deep Learning Systems Differential privacy in the shuffle model: A survey of separations, 2022

Reference 12

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Observation 5d4c7da8-cc9d-435b-a996-bd29d759b644 · outbound

This paper cites Split conformal prediction under data contamination.

Confidence Calibration of Deep Learning Systems Split conformal prediction under data contamination

Reference 13

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Observation a6d94616-50ac-4bd9-ab89-0ad1b0fd2d56 · outbound

This paper cites Towards discriminability and diversity: Batch nuclear-norm maximization under label insuﬀicient situa- tions.

Confidence Calibration of Deep Learning Systems Towards discriminability and diversity: Batch nuclear-norm maximization under label insuﬀicient situa- tions

Reference 14

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Observation 78d0afca-da10-4135-86ef-7ecbc2e10e26 · outbound

This paper cites Imagenet: A large- scale hierarchical image database.

Confidence Calibration of Deep Learning Systems Imagenet: A large- scale hierarchical image database

Reference 15

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Observation 077222a3-c620-40d9-9289-a4d9a13e92c8 · outbound

This paper cites Are labels always necessary for classifier accuracy evaluation? In Proc.

Confidence Calibration of Deep Learning Systems Are labels always necessary for classifier accuracy evaluation? In Proc

Reference 16

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Observation 08c0701d-40fd-4264-b3b0-d603ae8cb66f · outbound

This paper cites Training a neural network based on unreliable human annotation of medical images.

Confidence Calibration of Deep Learning Systems Training a neural network based on unreliable human annotation of medical images

Reference 17

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This paper cites Dowson and B.

Confidence Calibration of Deep Learning Systems Dowson and B

Reference 18

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Observation cc205d19-6032-40a6-9f4d-8f80766d11e8 · outbound

This paper cites Local privacy and statistical minimax rates.

Confidence Calibration of Deep Learning Systems Local privacy and statistical minimax rates

Reference 19

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Observation 1ef2f4e9-e98a-4054-a3c4-c59cefab937f · outbound

This paper cites Differential privacy.

Confidence Calibration of Deep Learning Systems Differential privacy

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T00:21:56.867270Z

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Observation 07b52af0-1b32-4283-86af-abbe6defc2f3 · outbound

This paper cites Label Noise Robustness of Conformal Prediction.

Confidence Calibration of Deep Learning Systems Label Noise Robustness of Conformal Prediction

Reference 21

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Observation efeaeecb-1d15-472a-8dc2-448be803ea43 · outbound

This paper cites Rappor: Randomized aggregatable privacy-preserving ordinal response.

Confidence Calibration of Deep Learning Systems Rappor: Randomized aggregatable privacy-preserving ordinal response

Reference 22

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This paper cites an unresolved cited work.

Confidence Calibration of Deep Learning Systems Unresolved cited work

Reference 23

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Observation 83c90485-39b4-4c85-9901-9c54344b7a9f · outbound

This paper cites The limits of distribution-free conditional predictive inference.Information and Inference: A Journal of the IMA, 10(2):455–482, 2021.

Confidence Calibration of Deep Learning Systems The limits of distribution-free conditional predictive inference.Information and Inference: A Journal of the IMA, 10(2):455–482, 2021

Reference 24

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Observation 4b885a0a-ccf3-49b8-8526-9a7a0034ad39 · outbound

This paper cites Calibration of medical imaging classification systems with weight scaling.

Confidence Calibration of Deep Learning Systems Calibration of medical imaging classification systems with weight scaling

Reference 25

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Observation 114bb003-8890-45b7-9389-0caf59bc10f8 · outbound

This paper cites Locally private mean estimation: Z-test and tight confidence intervals, 2019.

Confidence Calibration of Deep Learning Systems Locally private mean estimation: Z-test and tight confidence intervals, 2019

Reference 26

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Observation e4bd1b57-d51b-4266-bfb4-7e9c71b82356 · outbound

This paper cites Domain-adversarial training of neural networks.

Confidence Calibration of Deep Learning Systems Domain-adversarial training of neural networks

Reference 27

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Observation aa15c0d5-4a6e-426b-a33c-dfb2ac318e21 · outbound

This paper cites Leveraging unlabeled data to predict out-of- distribution performance.

Confidence Calibration of Deep Learning Systems Leveraging unlabeled data to predict out-of- distribution performance

Reference 28

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Observation 9d2af7c9-96ea-430b-9cd3-9709394a94a0 · outbound

This paper cites Deep learning with label differential privacy.Advances in Neural Information Processing Systems (NeurIPs), 34:27131–27145, 2021.

Confidence Calibration of Deep Learning Systems Deep learning with label differential privacy.Advances in Neural Information Processing Systems (NeurIPs), 34:27131–27145, 2021

Reference 29

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Confidence Calibration of Deep Learning Systems Unresolved cited work

Reference 30

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Observation 6563ee15-062b-4e0c-ae16-41078b0f5479 · outbound

This paper cites Training deep neural-networks using a noise adap- tation layer.

Confidence Calibration of Deep Learning Systems Training deep neural-networks using a noise adap- tation layer

Reference 31

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This paper cites Pre- dicting with confidence on unseen distributions.

Confidence Calibration of Deep Learning Systems Pre- dicting with confidence on unseen distributions

Reference 32

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This paper cites On calibration of modern neural networks.

Confidence Calibration of Deep Learning Systems On calibration of modern neural networks

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This paper cites Deep self-learning from noisy labels.

Confidence Calibration of Deep Learning Systems Deep self-learning from noisy labels

Reference 34

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Observation a4fd0e05-6491-49b2-95e8-9249100f2404 · outbound

This paper cites Deep residual learning for image recognition.

Confidence Calibration of Deep Learning Systems Deep residual learning for image recognition

Reference 35

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Observation 06be17e7-9e68-4170-b0c6-c7a6296b4559 · outbound

This paper cites Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem.

Confidence Calibration of Deep Learning Systems Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem

Reference 36

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This paper cites Using trusted data to train deep networks on labels corrupted by severe noise.

Confidence Calibration of Deep Learning Systems Using trusted data to train deep networks on labels corrupted by severe noise

Reference 37

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Observation 5d337606-8931-4421-b9a6-1417e6289030 · outbound

This paper cites Simple and effective regularization methods for training on noisily labeled data with generalization guarantee.

Confidence Calibration of Deep Learning Systems Simple and effective regularization methods for training on noisily labeled data with generalization guarantee

Reference 38

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Observation 06978f0f-194d-4fce-bf8b-987d410c8d8b · outbound

This paper cites Densely con- nected convolutional networks.

Confidence Calibration of Deep Learning Systems Densely con- nected convolutional networks

Reference 39

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This paper cites O2u-net: Asimplenoisylabeldetection approachfordeepneuralnetworks.

Confidence Calibration of Deep Learning Systems O2u-net: Asimplenoisylabeldetection approachfordeepneuralnetworks

Reference 40

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Observation 47abc4dd-8002-47d4-bd2e-63096a8cd80c · outbound

This paper cites Uncertainty-aware learning against label noise on imbalanced datasets.

Confidence Calibration of Deep Learning Systems Uncertainty-aware learning against label noise on imbalanced datasets

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Observation 0e749943-0d39-4ddc-a8fe-e9ee1f089428 · outbound

This paper cites Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison.

Confidence Calibration of Deep Learning Systems Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison

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Observation a85a1763-db89-41df-98c3-461a63b13ec8 · outbound

This paper cites Delving into sample loss curve to embrace noisy and imbalanced data.

Confidence Calibration of Deep Learning Systems Delving into sample loss curve to embrace noisy and imbalanced data

Reference 43

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This paper cites Minimum class confusion for ver- satile domain adaptation.

Confidence Calibration of Deep Learning Systems Minimum class confusion for ver- satile domain adaptation

Reference 44

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Observation 99862c7e-7289-4396-8898-ba0a8990c726 · outbound

This paper cites Discrete distribution estimation under local privacy.

Confidence Calibration of Deep Learning Systems Discrete distribution estimation under local privacy

Reference 45

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Observation ceef08ae-235b-4df8-ad84-01664a31c3b2 · outbound

This paper cites What can we learn privately?SIAM Journal on Computing , 40(3):793–826, 2011.

Confidence Calibration of Deep Learning Systems What can we learn privately?SIAM Journal on Computing , 40(3):793–826, 2011

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Observation c6c741a9-55f1-4de4-a499-226766d93265 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Confidence Calibration of Deep Learning Systems Adam: A Method for Stochastic Optimization

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Observation 62ba38f7-540f-4e73-88dc-c0d50a475ee5 · outbound

This paper cites Learning multiple layers of features from tiny images.

Confidence Calibration of Deep Learning Systems Learning multiple layers of features from tiny images

Reference 48

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Observation a8bb66c5-d3d0-4bdb-aa1c-67053688f8b4 · outbound

This paper cites Simple and scalable pre- dictive uncertainty estimation using deep ensembles.

Confidence Calibration of Deep Learning Systems Simple and scalable pre- dictive uncertainty estimation using deep ensembles

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Observation df84e6e9-546a-478f-8630-8a4819b4e0dd · outbound

This paper cites Coupled-view deep classifier learning from multiple noisy annotators.

Confidence Calibration of Deep Learning Systems Coupled-view deep classifier learning from multiple noisy annotators

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.953113Z

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Observation 4abcec2f-5523-4d89-8563-0ff9e6f72368 · outbound

This paper cites Trustable co-label learning from multiple noisy annotators.IEEE Transactions on Multimedia , 25:1045–1057, 2021.

Confidence Calibration of Deep Learning Systems Trustable co-label learning from multiple noisy annotators.IEEE Transactions on Multimedia , 25:1045–1057, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.935734Z

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Observation 5efeca0c-3787-480d-a88d-51081aa7726c · outbound

This paper cites Provably end-to-end label-noise learning without anchor points.

Confidence Calibration of Deep Learning Systems Provably end-to-end label-noise learning without anchor points

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.915713Z

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Observation 29cce41a-ab31-456d-b65b-d0b22f5a2c90 · outbound

This paper cites Domain adaptation with auxiliary target domain- oriented classifier.

Confidence Calibration of Deep Learning Systems Domain adaptation with auxiliary target domain- oriented classifier

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.897885Z

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Observation d46f2699-13a2-4696-8f34-5fb22c06d565 · outbound

This paper cites A holistic view of label noise transition matrix in deep learning and beyond.

Confidence Calibration of Deep Learning Systems A holistic view of label noise transition matrix in deep learning and beyond

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.872899Z

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Observation 42f2a3c2-c3de-41dd-9153-cdb5c4e6437e · outbound

This paper cites Classification with noisy labels by importance reweighting.

Confidence Calibration of Deep Learning Systems Classification with noisy labels by importance reweighting

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.849626Z

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Observation e8f8d84b-a28b-42f3-9ee0-b2b7ece868dc · outbound

This paper cites Conditional adversarial domain adaptation.

Confidence Calibration of Deep Learning Systems Conditional adversarial domain adaptation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.829800Z

Source-reported events for the cited work

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Observation 88a7c63c-a34f-4c9a-b77d-64a016e2b9dc · outbound

This paper cites Improving trustworthiness of AI disease severity rating in medical imaging with ordinal conformal prediction sets.

Confidence Calibration of Deep Learning Systems Improving trustworthiness of AI disease severity rating in medical imaging with ordinal conformal prediction sets

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.812677Z

Source-reported events for the cited work

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Observation 2d5e2459-b4d6-41ee-b050-18fa20d659e3 · outbound

This paper cites Fair conformal predictors for applications in medical imaging.

Confidence Calibration of Deep Learning Systems Fair conformal predictors for applications in medical imaging

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.792812Z

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Observation 5e76b7a0-5d16-4408-aa98-02b5025467ae · outbound

This paper cites Label-noise learning with intrinsically long-tailed data.arXiv e-prints, pages arXiv–2208, 2022.

Confidence Calibration of Deep Learning Systems Label-noise learning with intrinsically long-tailed data.arXiv e-prints, pages arXiv–2208, 2022

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Observation dd562fb1-447f-465e-9f3a-870a162ef804 · outbound

This paper cites The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality.The Annals of Probability, pages 1269–1283, 1990.

Confidence Calibration of Deep Learning Systems The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality.The Annals of Probability, pages 1269–1283, 1990

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Observation 89098c72-8e08-40cb-b5e0-d4f6d4f7a9f3 · outbound

This paper cites Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization.

Confidence Calibration of Deep Learning Systems Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.750734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c93014b4-f8a4-465e-a6b9-5b89cd8fd0ac · outbound

This paper cites Revisiting the calibration of modern neural net- works.

Confidence Calibration of Deep Learning Systems Revisiting the calibration of modern neural net- works

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.733804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e55f47ef-97f0-44cf-a9e4-b3a4a6659c35 · outbound

This paper cites Calibrating deep neural networks using focal loss.Advances in Neural Information Processing Systems (NeurIPs), 33:15288–15299, 2020.

Confidence Calibration of Deep Learning Systems Calibrating deep neural networks using focal loss.Advances in Neural Information Processing Systems (NeurIPs), 33:15288–15299, 2020

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.718544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9ed33c88-dcd4-46a4-9ffa-6a8556c53c6b · outbound

This paper cites When Does Label Smoothing Help?.

Confidence Calibration of Deep Learning Systems When Does Label Smoothing Help?

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T00:21:57.106643Z

Source-reported events for the cited work

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Observation b2ee5c37-0783-4919-9c9a-15d0cc69259e · outbound

This paper cites Obtaining well calibrated probabilities using bayesian binning.

Confidence Calibration of Deep Learning Systems Obtaining well calibrated probabilities using bayesian binning

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-16T00:21:57.112238Z

Source-reported events for the cited work

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Observation 77cbb9e6-634f-40e7-b5bb-1d4e90fa95ff · outbound

This paper cites Posterior calibration and exploratory analysis for natural language processing models.

Confidence Calibration of Deep Learning Systems Posterior calibration and exploratory analysis for natural language processing models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T00:21:57.117726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9953bf5-438c-4c40-ab5b-34dbcf16b913 · outbound

This paper cites Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction.Nature Communications, 13(1):7761, 2022.

Confidence Calibration of Deep Learning Systems Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction.Nature Communications, 13(1):7761, 2022

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.691534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 64808eae-729a-4791-93fc-38caee6ca979 · outbound

This paper cites Unsupervised Calibration under Covariate Shift.

Confidence Calibration of Deep Learning Systems Unsupervised Calibration under Covariate Shift

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-16T00:21:57.130342Z

Source-reported events for the cited work

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Observation 1bda673c-411d-4f69-95b8-0cb74ebcf0a2 · outbound

This paper cites Calibrated prediction with covariate shift via unsupervised domain adaptation.

Confidence Calibration of Deep Learning Systems Calibrated prediction with covariate shift via unsupervised domain adaptation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.674750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5d6d6c6e-7261-47a9-8286-a4350a21d0ca · outbound

This paper cites Making deep neural networks robust to label noise: A loss correction approach.

Confidence Calibration of Deep Learning Systems Making deep neural networks robust to label noise: A loss correction approach

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.656464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6830d369-df86-4ac8-81ab-80cfb3891ceb · outbound

This paper cites Moment matching for multi-source domain adaptation.

Confidence Calibration of Deep Learning Systems Moment matching for multi-source domain adaptation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.638529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 45b98941-1f2d-467f-9cda-07afd771d67b · outbound

This paper cites VisDA: The Visual Domain Adaptation Challenge.

Confidence Calibration of Deep Learning Systems VisDA: The Visual Domain Adaptation Challenge

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-16T00:21:57.157931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 125ab980-dc21-432d-b79d-657b702d72c8 · outbound

This paper cites Privacy-preserving confor- mal prediction under local differential privacy.

Confidence Calibration of Deep Learning Systems Privacy-preserving confor- mal prediction under local differential privacy

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.623468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation edf92e43-4de2-4f5f-a203-705f064440c0 · outbound

This paper cites Confidence calibration of a medical imaging classification system that is robust to label noise.

Confidence Calibration of Deep Learning Systems Confidence calibration of a medical imaging classification system that is robust to label noise

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.608920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation da6cfc48-b992-44b8-bc17-98badf259110 · outbound

This paper cites Calibration of network confidence for unsupervised domain adaptation using estimated accuracy.

Confidence Calibration of Deep Learning Systems Calibration of network confidence for unsupervised domain adaptation using estimated accuracy

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.592376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 30087cd9-91bb-4f85-ac61-bdfaaa8b9c32 · outbound

This paper cites A conformal prediction score that is robust to label noise.

Confidence Calibration of Deep Learning Systems A conformal prediction score that is robust to label noise

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.577586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 027648a6-e8b0-4437-8876-b965bbda4a2d · outbound

This paper cites A joint training and confidence calibration procedure that is robust to label noise.

Confidence Calibration of Deep Learning Systems A joint training and confidence calibration procedure that is robust to label noise

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.557457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 61de6896-52d5-4f96-a08a-24432a7b5a09 · outbound

This paper cites Conformal Prediction of Classifiers with Many Classes based on Noisy Labels.

Confidence Calibration of Deep Learning Systems Conformal Prediction of Classifiers with Many Classes based on Noisy Labels

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-16T00:21:57.191524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 766c1f85-a7e8-46a1-b55e-abc97597af47 · outbound

This paper cites Conformal prediction of classifiers with many classes based on noisy labels.

Confidence Calibration of Deep Learning Systems Conformal prediction of classifiers with many classes based on noisy labels

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.535089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 34e1f52a-73ec-4357-bcee-96922365834b · outbound

This paper cites Probabilistic outputs for support vector machines and comparisons to regu- larized likelihood methods.

Confidence Calibration of Deep Learning Systems Probabilistic outputs for support vector machines and comparisons to regu- larized likelihood methods

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.516018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b341e812-8840-4a2b-95f3-8f0f0e7b598b · outbound

This paper cites Learning to reweight examples for robust deep learning.

Confidence Calibration of Deep Learning Systems Learning to reweight examples for robust deep learning

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.496821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7954ae42-16c0-41cb-96b2-c3a51504e165 · outbound

This paper cites Classification with valid and adaptive coverage.

Confidence Calibration of Deep Learning Systems Classification with valid and adaptive coverage

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.476342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d72de517-d060-45f3-bd8d-ff161375ebed · outbound

This paper cites Post training uncertainty calibration of deep networks for medical image segmentation.

Confidence Calibration of Deep Learning Systems Post training uncertainty calibration of deep networks for medical image segmentation

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.458784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7bfda177-4449-4a56-9c5e-a7d3a72c548f · outbound

This paper cites Adapting visual category models to new domains.

Confidence Calibration of Deep Learning Systems Adapting visual category models to new domains

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.440327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 05a93f30-4d91-4875-9e04-a952bf8c3c5a · outbound

This paper cites Improved pre- dictive uncertainty using corruption-based calibration.Stat, 1050:7, 2021.

Confidence Calibration of Deep Learning Systems Improved pre- dictive uncertainty using corruption-based calibration.Stat, 1050:7, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.422230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a21cf5a3-a0fe-4445-859e-538e7cacff04 · outbound

This paper cites Adaptive conformal classification with noisy labels.

Confidence Calibration of Deep Learning Systems Adaptive conformal classification with noisy labels

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-16T00:21:57.242428Z

Source-reported events for the cited work

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Observation 36192aa7-da50-4e22-a07e-9f0c76ccdda5 · outbound

This paper cites Meta- weight-net: Learning an explicit mapping for sample weighting.

Confidence Calibration of Deep Learning Systems Meta- weight-net: Learning an explicit mapping for sample weighting

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.404351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7a31a58b-d98e-45ed-a25c-f58b59dac709 · outbound

This paper cites Learning from noisy labels with deep neural networks: A survey.IEEE transactions on neural networks and learning systems, 34(11):8135–8153, 2022.

Confidence Calibration of Deep Learning Systems Learning from noisy labels with deep neural networks: A survey.IEEE transactions on neural networks and learning systems, 34(11):8135–8153, 2022

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-16T00:21:57.255094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc8fa04e-87e9-4c75-a1fa-cfa059be29ed · outbound

This paper cites Joint optimization framework for learning with noisy labels.

Confidence Calibration of Deep Learning Systems Joint optimization framework for learning with noisy labels

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.373540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cbe96b34-9073-4870-93aa-933a06366cae · outbound

This paper cites Post-hoc uncertainty calibration for domain drift scenarios.

Confidence Calibration of Deep Learning Systems Post-hoc uncertainty calibration for domain drift scenarios

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.352586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 19be5904-a966-4be9-978f-1b069616f650 · outbound

This paper cites The HAM10000 dataset, a large collec- tion of multi-source dermatoscopic images of common pigmented skin lesions.Scientific data, 5(1):1–9, 2018.

Confidence Calibration of Deep Learning Systems The HAM10000 dataset, a large collec- tion of multi-source dermatoscopic images of common pigmented skin lesions.Scientific data, 5(1):1–9, 2018

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.336449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 03cd96dd-e0cb-4d92-a6f6-d0384ab9a629 · outbound

This paper cites Deep hashing network for unsupervised domain adaptation.

Confidence Calibration of Deep Learning Systems Deep hashing network for unsupervised domain adaptation

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.320529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 69c7a617-e39d-48d3-b103-1fdbda634c4e · outbound

This paper cites Springer, 2005.

Confidence Calibration of Deep Learning Systems Springer, 2005

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-16T00:21:57.284097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc590cf8-4822-45df-b73b-3ce556d1f580 · outbound

This paper cites Graph structure estimation neural networks.

Confidence Calibration of Deep Learning Systems Graph structure estimation neural networks

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.293717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9be0591e-58ba-4d0b-8663-46a5da22ee1c · outbound

This paper cites Locally differentially private protocols for frequency estimation.

Confidence Calibration of Deep Learning Systems Locally differentially private protocols for frequency estimation

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-16T00:21:57.293561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25fa4197-fe79-403f-9191-1dbdf045e69f · outbound

This paper cites Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases.

Confidence Calibration of Deep Learning Systems Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.264419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f54f210c-8272-487f-b86e-4b1e1574aa6f · outbound

This paper cites Transferable calibra- tion with lower bias and variance in domain adaptation.

Confidence Calibration of Deep Learning Systems Transferable calibra- tion with lower bias and variance in domain adaptation

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.245391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 14f78754-230a-4133-afd5-afac0d977d0a · outbound

This paper cites Randomized response: A survey technique for eliminating evasive answer bias.

Confidence Calibration of Deep Learning Systems Randomized response: A survey technique for eliminating evasive answer bias

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:21:58.225701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 013785d3-129f-4b7e-a202-b6abcb644ee4 · outbound

This paper cites Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations.

Confidence Calibration of Deep Learning Systems Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-16T00:21:57.313395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc326788-e456-4597-af2a-936c89eec70e · outbound

This paper cites Robust Long-Tailed Learning under Label Noise.

Confidence Calibration of Deep Learning Systems Robust Long-Tailed Learning under Label Noise

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-16T00:21:57.318142Z

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