Paper Citation Record · LEDGER

Sample Margin-Aware Recalibration of Temperature Scaling

As of 9 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2506.23492.

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

pith.paper-citation-record.v1
2506.23492 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:47:00.323351Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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verified fuzzy27
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d7a2c9ea-e071-4f39-93b4-a18f2f9860cb · outbound

This paper cites On calibration of modern neural networks.

Sample Margin-Aware Recalibration of Temperature Scaling On calibration of modern neural networks

Reference 1

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Observation 32753ecf-21b7-471c-8f76-e057fc938eb0 · outbound

This paper cites Obtaining well calibrated probabilities using bayesian binning.

Sample Margin-Aware Recalibration of Temperature Scaling Obtaining well calibrated probabilities using bayesian binning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:04.641814Z

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Observation e134ed4e-fc49-427c-a039-dbf27662db17 · outbound

This paper cites Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers.

Sample Margin-Aware Recalibration of Temperature Scaling Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:04.445575Z

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Observation 7ba046f9-76b2-4377-a598-3e6435f6451f · outbound

This paper cites Network calibration using differentiable classification performance metrics.

Sample Margin-Aware Recalibration of Temperature Scaling Network calibration using differentiable classification performance metrics

Reference 4

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Observation df70c6c6-574e-422b-88bc-929d790b1940 · outbound

This paper cites Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration.

Sample Margin-Aware Recalibration of Temperature Scaling Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration

Reference 5

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Observation 3bcd2487-dea4-4945-97bb-4454f69c9fa9 · outbound

This paper cites Parameterized temperature scaling for boosting the expressive power in post-hoc uncertainty calibration.

Sample Margin-Aware Recalibration of Temperature Scaling Parameterized temperature scaling for boosting the expressive power in post-hoc uncertainty calibration

Reference 6

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Observation e93c143a-d998-461a-b327-20d1a9fbaa89 · outbound

This paper cites Trainable calibration measures for neural networks from kernel mean embeddings.

Sample Margin-Aware Recalibration of Temperature Scaling Trainable calibration measures for neural networks from kernel mean embeddings

Reference 7

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Observation 3e7d8576-b40c-4755-bc7f-a25a904cd3ea · outbound

This paper cites Soft calibration objectives for neural networks.

Sample Margin-Aware Recalibration of Temperature Scaling Soft calibration objectives for neural networks

Reference 8

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This paper cites Obtaining well calibrated probabilities using bayesian binning.

Sample Margin-Aware Recalibration of Temperature Scaling Obtaining well calibrated probabilities using bayesian binning

Reference 9

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Observation 594e8df5-9652-4df3-a1f6-621087348276 · outbound

This paper cites Calibration of neural networks using splines.

Sample Margin-Aware Recalibration of Temperature Scaling Calibration of neural networks using splines

Reference 10

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Observation 58ca27ec-1d10-41b3-8b05-5d3d0ed751cb · outbound

This paper cites UnMASKed: Quantifying Gender Biases in Masked Language Models through Linguistically Informed Job Market Prompts.

Sample Margin-Aware Recalibration of Temperature Scaling UnMASKed: Quantifying Gender Biases in Masked Language Models through Linguistically Informed Job Market Prompts

Reference 11

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Observation a8e98ea7-d8df-47da-92c5-d6f1021b13a2 · outbound

This paper cites Proximity-informed calibration for deep neural networks.

Sample Margin-Aware Recalibration of Temperature Scaling Proximity-informed calibration for deep neural networks

Reference 12

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This paper cites Multicalibration: Calibration for the (computationally-identifiable) masses.

Sample Margin-Aware Recalibration of Temperature Scaling Multicalibration: Calibration for the (computationally-identifiable) masses

Reference 13

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Source-reported events for the cited work

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Observation c6c4efd1-ad2e-4460-a5b9-a1d1fadfe4d8 · outbound

This paper cites Verification of forecasts expressed in terms of probability.

Sample Margin-Aware Recalibration of Temperature Scaling Verification of forecasts expressed in terms of probability

Reference 14

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Observation 8e40f62d-d41a-468c-9a2c-0e790a8c11a1 · outbound

This paper cites Re- thinking the inception architecture for computer vision.

Sample Margin-Aware Recalibration of Temperature Scaling Re- thinking the inception architecture for computer vision

Reference 15

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Observation 4efd638e-5564-4abe-a6a7-af5f500e8d0d · outbound

This paper cites Calibrating deep neural networks using focal loss.

Sample Margin-Aware Recalibration of Temperature Scaling Calibrating deep neural networks using focal loss

Reference 16

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Observation 662dd26b-3bbd-48f8-a13a-b4138a35325e · outbound

This paper cites Dual focal loss for calibration.

Sample Margin-Aware Recalibration of Temperature Scaling Dual focal loss for calibration

Reference 17

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Observation d5287093-8e5f-497b-bdc9-9f547f2c454e · outbound

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

Sample Margin-Aware Recalibration of Temperature Scaling Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 18

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

Sample Margin-Aware Recalibration of Temperature Scaling Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 19

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This paper cites Approaching the limit of accuracy: Residual uncertainty via test-time data augmentation.

Sample Margin-Aware Recalibration of Temperature Scaling Approaching the limit of accuracy: Residual uncertainty via test-time data augmentation

Reference 20

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Observation e2acd416-b4cb-49f8-9107-0fa6f110e236 · outbound

This paper cites Yong-Jin Han.

Sample Margin-Aware Recalibration of Temperature Scaling Yong-Jin Han

Reference 21

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Observation 8fa75c68-7761-4f4a-bea9-dac25e715cf7 · outbound

This paper cites Intra order-preserving functions for calibration of multi-class neural networks.

Sample Margin-Aware Recalibration of Temperature Scaling Intra order-preserving functions for calibration of multi-class neural networks

Reference 22

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This paper cites Optimizing Calibration by Gaining Aware of Prediction Correctness.

Sample Margin-Aware Recalibration of Temperature Scaling Optimizing Calibration by Gaining Aware of Prediction Correctness

Reference 23

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

Sample Margin-Aware Recalibration of Temperature Scaling Learning multiple layers of features from tiny images

Reference 24

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Sample Margin-Aware Recalibration of Temperature Scaling ImageNet: A large-scale hierarchical image database

Reference 25

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

Sample Margin-Aware Recalibration of Temperature Scaling Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 26

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This paper cites Large-scale long-tailed recognition in an open world.

Sample Margin-Aware Recalibration of Temperature Scaling Large-scale long-tailed recognition in an open world

Reference 27

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This paper cites Learning to recognize sketches: The ImageNet-Sketch benchmark.

Sample Margin-Aware Recalibration of Temperature Scaling Learning to recognize sketches: The ImageNet-Sketch benchmark

Reference 28

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Observation 77bf3193-1023-46b6-9bea-a403a48875fb · outbound

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Sample Margin-Aware Recalibration of Temperature Scaling Deep residual learning for image recognition

Reference 29

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Sample Margin-Aware Recalibration of Temperature Scaling Wide residual networks

Reference 30

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Sample Margin-Aware Recalibration of Temperature Scaling Densely connected convolutional networks

Reference 31

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This paper cites HUJI-KU at MRP~2020: Two Transition-based Neural Parsers.

Sample Margin-Aware Recalibration of Temperature Scaling HUJI-KU at MRP~2020: Two Transition-based Neural Parsers

Reference 32

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

Sample Margin-Aware Recalibration of Temperature Scaling Pytorch: An imperative style, high-performance deep learning library

Reference 33

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Observation 0734ac8e-85f7-481b-81c3-bfcda2cf75db · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Sample Margin-Aware Recalibration of Temperature Scaling Swin transformer: Hierarchical vision transformer using shifted windows

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:01.446511Z

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Observation ddfb8eb2-ba2f-456a-9b6f-bbfb11c441ec · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Sample Margin-Aware Recalibration of Temperature Scaling An image is worth 16x16 words: Transformers for image recognition at scale

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:01.217443Z

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Observation 440ffa61-88fb-40b0-899d-51b127844baf · outbound

This paper cites Network calibration by class-based temperature scaling.

Sample Margin-Aware Recalibration of Temperature Scaling Network calibration by class-based temperature scaling

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:01.047604Z

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Observation 4fcc487c-e750-4fe1-8039-62267e97d665 · outbound

This paper cites the increased dimensionality introduces substantial noise for precise temperature parameterization,.

Sample Margin-Aware Recalibration of Temperature Scaling the increased dimensionality introduces substantial noise for precise temperature parameterization,

Reference 37

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T21:47:00.765823Z

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Pith citing papers

No inbound Pith citation observations are available.