Paper Citation Record · LEDGER

On Generalization Bounds for Neural Networks with Low Rank Layers

As of 20 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2411.13733.

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

pith.paper-citation-record.v1
2411.13733 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:21:41.299847Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:58:26.820993Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T19:58:27.566398Z

Reference resolution

30 of 30 outbound references displayed

verified exact3
verified fuzzy18
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 40925500-9f18-4289-a91c-5f8acaa9dfa9 · outbound

This paper cites A chain rule for the expected suprema of g aussian processes.

On Generalization Bounds for Neural Networks with Low Rank Layers A chain rule for the expected suprema of g aussian processes

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.743125Z

Source-reported events for the cited work

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Observation ac1eb1e4-a758-4e93-b0b6-209c1c6b370a · outbound

This paper cites De ep residual learning for image recogni- tion.

On Generalization Bounds for Neural Networks with Low Rank Layers De ep residual learning for image recogni- tion

Reference 2

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Observation ffadbdb5-4a03-4333-a52c-8b5639343eeb · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

On Generalization Bounds for Neural Networks with Low Rank Layers Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 3

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Observation c1e5066e-a0bf-4116-b1b9-45639bed5a06 · outbound

This paper cites Attention is all you ne ed.

On Generalization Bounds for Neural Networks with Low Rank Layers Attention is all you ne ed

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.728961Z

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Observation e94767ac-fbc3-485b-b2c0-9e59d055813f · outbound

This paper cites Language models are few-shot learners.

On Generalization Bounds for Neural Networks with Low Rank Layers Language models are few-shot learners

Reference 5

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Observation f44aeecf-e3fe-45e0-96fb-f99f085b2458 · outbound

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On Generalization Bounds for Neural Networks with Low Rank Layers Unresolved cited work

Reference 6

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Observation 0ccab29e-76d3-4cda-9f96-516349ed421c · outbound

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On Generalization Bounds for Neural Networks with Low Rank Layers Unresolved cited work

Reference 7

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Source-reported events for the cited work

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Observation 1417261e-b5ee-468a-99e5-049f32f6b449 · outbound

This paper cites Benign overﬁtting in linear regression.

On Generalization Bounds for Neural Networks with Low Rank Layers Benign overﬁtting in linear regression

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.687041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 815f855a-993e-4649-8868-b347305a5ea0 · outbound

This paper cites Harmless interpola- tion of noisy data in regression.

On Generalization Bounds for Neural Networks with Low Rank Layers Harmless interpola- tion of noisy data in regression

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.672940Z

Source-reported events for the cited work

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Observation d13e9524-8e3a-4503-a2d0-843190e7c2fa · outbound

This paper cites No rm-based capacity control in neural net- works.

On Generalization Bounds for Neural Networks with Low Rank Layers No rm-based capacity control in neural net- works

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.661076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8df543e6-cab3-4d6a-971f-ad09f907a895 · outbound

This paper cites Advances in neural information processing systems , 30, 2017.

On Generalization Bounds for Neural Networks with Low Rank Layers Advances in neural information processing systems , 30, 2017

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.647416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 24b0931c-d152-4abe-9fd3-2f66b5e49fcd · outbound

This paper cites Com plexity control by gradient descent in deep networks.

On Generalization Bounds for Neural Networks with Low Rank Layers Com plexity control by gradient descent in deep networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.632526Z

Source-reported events for the cited work

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Observation 5fd179f4-4a4c-43e7-baba-d0915f84b962 · outbound

This paper cites Dynamics in deep classiﬁers trained with the square loss: Normalization, lo w rank, neural collapse, and generalization bounds.

On Generalization Bounds for Neural Networks with Low Rank Layers Dynamics in deep classiﬁers trained with the square loss: Normalization, lo w rank, neural collapse, and generalization bounds

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.618706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1c896c99-4d05-4ba3-83ed-83564ddb94d8 · outbound

This paper cites Siz e-independent sample complexity of neural networks.

On Generalization Bounds for Neural Networks with Low Rank Layers Siz e-independent sample complexity of neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.605050Z

Source-reported events for the cited work

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Observation 591a1dbd-c2e0-4cdc-b273-1251429714ab · outbound

This paper cites an unresolved cited work.

On Generalization Bounds for Neural Networks with Low Rank Layers Unresolved cited work

Reference 15

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Source-reported events for the cited work

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Observation 7219d340-2303-4817-a016-d38329641ca0 · outbound

This paper cites SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network.

On Generalization Bounds for Neural Networks with Low Rank Layers SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network

Reference 16

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Source-reported events for the cited work

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Observation 1124d261-552d-464b-8f99-3d9ba6f04f78 · outbound

This paper cites The low-rank simplicity bias in deep networks.

On Generalization Bounds for Neural Networks with Low Rank Layers The low-rank simplicity bias in deep networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.579757Z

Source-reported events for the cited work

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Observation d6ed6c37-df5a-4183-998c-e051e497f966 · outbound

This paper cites Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, and Nathan Srebro.

On Generalization Bounds for Neural Networks with Low Rank Layers Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, and Nathan Srebro

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.567209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cceef7bc-b387-4e24-83bb-c5b783ebe708 · outbound

This paper cites Directional convergence and alignment in deep learning.

On Generalization Bounds for Neural Networks with Low Rank Layers Directional convergence and alignment in deep learning

Reference 19

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Source-reported events for the cited work

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Observation 6c71b90d-3700-400a-9bc8-add109e35a35 · outbound

This paper cites Gradient Descent Happens in a Tiny Subspace.

On Generalization Bounds for Neural Networks with Low Rank Layers Gradient Descent Happens in a Tiny Subspace

Reference 20

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Source-reported events for the cited work

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Observation 2a4b13dd-7c70-4473-bd3e-b307f173436d · outbound

This paper cites Implicit regul arization towards rank minimiza- tion in relu networks.

On Generalization Bounds for Neural Networks with Low Rank Layers Implicit regul arization towards rank minimiza- tion in relu networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.552330Z

Source-reported events for the cited work

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Observation 1101686d-5238-49a7-91ee-cc2ee0e19ac4 · outbound

This paper cites Prevalence of neural collapse during the terminal phase of deep learning training.

On Generalization Bounds for Neural Networks with Low Rank Layers Prevalence of neural collapse during the terminal phase of deep learning training

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.538039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bbe58aeb-48f5-411e-8583-a8a4605a60c4 · outbound

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On Generalization Bounds for Neural Networks with Low Rank Layers Unresolved cited work

Reference 23

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Source-reported events for the cited work

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Observation 45d04225-8511-419d-a412-59af28acb6ea · outbound

This paper cites Rademacher and g aussian complexities: Risk bounds and structural results.

On Generalization Bounds for Neural Networks with Low Rank Layers Rademacher and g aussian complexities: Risk bounds and structural results

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.494744Z

Source-reported events for the cited work

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Observation a8975d62-e0c6-46db-b0a7-bbc7d5b2e29a · outbound

This paper cites A Chain Rule for the Expected Suprema of Bernoulli Processes.

On Generalization Bounds for Neural Networks with Low Rank Layers A Chain Rule for the Expected Suprema of Bernoulli Processes

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-12T16:21:41.372813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a517638a-8bb3-4b87-a163-183f6c00cfc0 · outbound

This paper cites Understanding machine learning: From theory to algorithms.

On Generalization Bounds for Neural Networks with Low Rank Layers Understanding machine learning: From theory to algorithms

Reference 26

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Source-reported events for the cited work

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Observation 6e19e95d-b805-444d-a32d-fa4fb76abaec · outbound

This paper cites Foundations of machine learning.

On Generalization Bounds for Neural Networks with Low Rank Layers Foundations of machine learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.470143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8c6b9639-1832-4eb4-a32e-34be37e6b198 · outbound

This paper cites A vector-contraction inequality for r ademacher complexities.

On Generalization Bounds for Neural Networks with Low Rank Layers A vector-contraction inequality for r ademacher complexities

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.456732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d17b94e9-c1bc-4084-9d2d-56e810dd5863 · outbound

This paper cites A pac-bayesian approach to spectrally- normalized margin bounds for neural networks.

On Generalization Bounds for Neural Networks with Low Rank Layers A pac-bayesian approach to spectrally- normalized margin bounds for neural networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:21:41.442240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6e252b24-b702-4cd9-bd50-4e872b0c9876 · outbound

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On Generalization Bounds for Neural Networks with Low Rank Layers Unresolved cited work

Reference 2023

Resolution: unresolved; raw_fallback, observed 2026-08-12T16:21:41.507634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 32e4a70b-92df-4506-b716-770f59b22ddf · inbound

Parameter Symmetry Potentially Unifies Deep Learning Theory cites this paper.

Parameter Symmetry Potentially Unifies Deep Learning Theory On Generalization Bounds for Neural Networks with Low Rank Layers

Reference 56

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.