Paper Citation Record · LEDGER

Learned Random Label Predictions as a Neural Network Complexity Metric

As of 12 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2411.19640.

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

pith.paper-citation-record.v1
2411.19640 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T06:02:31.932789Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

26 of 26 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4bd8fe82-b57b-4e2c-b62d-3aa7d96c608f · outbound

This paper cites write newline.

Learned Random Label Predictions as a Neural Network Complexity Metric write newline

Reference 1

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Source-reported events for the cited work

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Observation 9912836f-339d-4b17-a203-673eac38e606 · outbound

This paper cites Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings.

Learned Random Label Predictions as a Neural Network Complexity Metric Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings

Reference 2

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Source-reported events for the cited work

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Observation cf515396-cffd-4c59-bba5-d9f896429041 · outbound

This paper cites Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, and Simon Lacoste-Julien.

Learned Random Label Predictions as a Neural Network Complexity Metric Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, and Simon Lacoste-Julien

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.334732Z

Source-reported events for the cited work

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Observation 54192c5b-5533-433a-b45c-4cd3e7055b61 · outbound

This paper cites The secret sharer: evaluating and testing unintended memorization in neural networks.

Learned Random Label Predictions as a Neural Network Complexity Metric The secret sharer: evaluating and testing unintended memorization in neural networks

Reference 4

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Source-reported events for the cited work

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Observation bb668f13-9b89-4311-afc1-a8679cc9a22c · outbound

This paper cites Extracting training data from large language models.

Learned Random Label Predictions as a Neural Network Complexity Metric Extracting training data from large language models

Reference 5

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Source-reported events for the cited work

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Observation 399775f0-4c60-4ce5-9084-f39817b50ea3 · outbound

This paper cites Neural networks learning and memorization with (almost) no over-parameterization.

Learned Random Label Predictions as a Neural Network Complexity Metric Neural networks learning and memorization with (almost) no over-parameterization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.289272Z

Source-reported events for the cited work

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Observation ccaef573-a7f1-4b67-9b40-d524438f0245 · outbound

This paper cites What neural networks memorize and why: Discovering the long tail via influence estimation.

Learned Random Label Predictions as a Neural Network Complexity Metric What neural networks memorize and why: Discovering the long tail via influence estimation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.274518Z

Source-reported events for the cited work

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Observation dfe619bd-180d-4fc9-ac78-0b9201dc7048 · outbound

This paper cites Entropy and mutual information in models of deep neural networks.

Learned Random Label Predictions as a Neural Network Complexity Metric Entropy and mutual information in models of deep neural networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.258597Z

Source-reported events for the cited work

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Observation 6eea0144-d0da-4d0a-8c77-c89b3020fef9 · outbound

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

Learned Random Label Predictions as a Neural Network Complexity Metric Learning multiple layers of features from tiny images

Reference 9

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Source-reported events for the cited work

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Observation 13796d16-dfb6-481b-8d25-24bbf6be40fa · outbound

This paper cites A simple weight decay can improve generalization.

Learned Random Label Predictions as a Neural Network Complexity Metric A simple weight decay can improve generalization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.231793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2a1143aa-fdc4-4b47-8b1d-148d99a9a57f · outbound

This paper cites A survey on bias and fairness in machine learning.

Learned Random Label Predictions as a Neural Network Complexity Metric A survey on bias and fairness in machine learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.214727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a1d961aa-d44e-4a4e-9408-67495def1bd7 · outbound

This paper cites Foundations of machine learning.

Learned Random Label Predictions as a Neural Network Complexity Metric Foundations of machine learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.199624Z

Source-reported events for the cited work

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Observation a07d1e76-8377-43fa-9ba0-8e07b0bb1cbb · outbound

This paper cites When does label smoothing help? In Advances in Neural Information Processing Systems , volume 32, 2019.

Learned Random Label Predictions as a Neural Network Complexity Metric When does label smoothing help? In Advances in Neural Information Processing Systems , volume 32, 2019

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.183461Z

Source-reported events for the cited work

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Observation 2ba60888-f363-4a33-a992-a6ef2205f96a · outbound

This paper cites Deep double descent: Where bigger models and more data hurt.

Learned Random Label Predictions as a Neural Network Complexity Metric Deep double descent: Where bigger models and more data hurt

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.167640Z

Source-reported events for the cited work

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Observation cf210664-b111-42f3-9de8-87b562615fd9 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition.

Learned Random Label Predictions as a Neural Network Complexity Metric Very deep convolutional networks for large-scale image recognition

Reference 15

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Observation 11d55968-16cf-4c2b-abd4-f1efe4b542a5 · outbound

This paper cites Dropout: A simple way to prevent neural networks from overfitting.

Learned Random Label Predictions as a Neural Network Complexity Metric Dropout: A simple way to prevent neural networks from overfitting

Reference 16

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Source-reported events for the cited work

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Observation 01a3971c-1157-458c-bd32-22eae262c86f · outbound

This paper cites Rethinking the inception architecture for computer vision.

Learned Random Label Predictions as a Neural Network Complexity Metric Rethinking the inception architecture for computer vision

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.128056Z

Source-reported events for the cited work

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Observation 97bf20bd-9ccd-4243-a48e-2f45ebe34ca7 · outbound

This paper cites Image fairness in deep learning: problems, models, and challenges.

Learned Random Label Predictions as a Neural Network Complexity Metric Image fairness in deep learning: problems, models, and challenges

Reference 18

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Source-reported events for the cited work

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Observation 01d29188-77f3-4a8d-846c-fdd182136a30 · outbound

This paper cites Memorization without overfitting: Analyzing the training dynamics of large language models.

Learned Random Label Predictions as a Neural Network Complexity Metric Memorization without overfitting: Analyzing the training dynamics of large language models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.099791Z

Source-reported events for the cited work

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Observation 3109987d-1557-447a-8a5e-5bc242c39007 · outbound

This paper cites Towards Fairness in Visual Recognition : Effective Strategies for Bias Mitigation.

Learned Random Label Predictions as a Neural Network Complexity Metric Towards Fairness in Visual Recognition : Effective Strategies for Bias Mitigation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.084765Z

Source-reported events for the cited work

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Observation 3bb0efb6-8bb6-47b0-bc7c-af0e3ace20db · outbound

This paper cites Small relu networks are powerful memorizers: a tight analysis of memorization capacity.

Learned Random Label Predictions as a Neural Network Complexity Metric Small relu networks are powerful memorizers: a tight analysis of memorization capacity

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.069780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 045ccded-d1ef-4a08-8df2-f2ea8adb2be6 · outbound

This paper cites Wide residual networks.

Learned Random Label Predictions as a Neural Network Complexity Metric Wide residual networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.054720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6e32e109-8f75-43ab-9e8a-ab7db3a2ee09 · outbound

This paper cites Mitigating Unwanted Biases with Adversarial Learning.

Learned Random Label Predictions as a Neural Network Complexity Metric Mitigating Unwanted Biases with Adversarial Learning

Reference 23

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Source-reported events for the cited work

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Observation a1f1496b-fba1-4842-ba48-727579185d4f · outbound

This paper cites Understanding deep learning requires rethinking generalization.

Learned Random Label Predictions as a Neural Network Complexity Metric Understanding deep learning requires rethinking generalization

Reference 24

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Source-reported events for the cited work

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Observation e5dc8786-c501-49b0-9eaf-2c52848f16a7 · outbound

This paper cites Mozer, and Yoram Singer.

Learned Random Label Predictions as a Neural Network Complexity Metric Mozer, and Yoram Singer

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.038793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 66b2c2fc-b15a-4a68-81fa-59775885ead8 · outbound

This paper cites Understanding deep learning (still) requires rethinking generalization.

Learned Random Label Predictions as a Neural Network Complexity Metric Understanding deep learning (still) requires rethinking generalization

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:02:32.024286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.