Paper Citation Record · LEDGER

Consistency of augmentation graph and network approximability in contrastive learning

As of 11 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2502.04312.

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

pith.paper-citation-record.v1
2502.04312 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T22:55:54.884663Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

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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

33 of 33 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4ca5a6f2-3703-4675-b6f2-53cd39f60d2b · outbound

This paper cites Data augmentation: A comprehensive survey of modern ap- proaches.

Consistency of augmentation graph and network approximability in contrastive learning Data augmentation: A comprehensive survey of modern ap- proaches

Reference 1

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Observation 0fd67b7a-d514-40f3-a4bc-6f52447dac6a · outbound

This paper cites Text data augmentation for deep learning.

Consistency of augmentation graph and network approximability in contrastive learning Text data augmentation for deep learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.403064Z

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Observation af8feef8-ae6d-464c-9d70-4202ba49661b · outbound

This paper cites Nonparametric regression on low- dimensional manifolds using deep relu networks: Function approximation and statistical recovery.

Consistency of augmentation graph and network approximability in contrastive learning Nonparametric regression on low- dimensional manifolds using deep relu networks: Function approximation and statistical recovery

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.387861Z

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Observation e8795587-ac13-4305-9471-66499dce6ef7 · outbound

This paper cites A Theoretical Analysis of Contrastive Unsupervised Representation Learning.

Consistency of augmentation graph and network approximability in contrastive learning A Theoretical Analysis of Contrastive Unsupervised Representation Learning

Reference 4

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Observation 3567c434-d2b0-4a42-9530-9092fa132093 · outbound

This paper cites Contrastive estimation reveals topic poste- rior information to linear models.

Consistency of augmentation graph and network approximability in contrastive learning Contrastive estimation reveals topic poste- rior information to linear models

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.372144Z

Source-reported events for the cited work

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Observation 4ff37c78-7723-41c1-a205-1c374259f4b4 · outbound

This paper cites Contrastive learning, multi-view redun- dancy, and linear models.

Consistency of augmentation graph and network approximability in contrastive learning Contrastive learning, multi-view redun- dancy, and linear models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.356282Z

Source-reported events for the cited work

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Observation a8289e3c-b7c5-48e7-bd41-4a25ee3c993a · outbound

This paper cites Provable guarantees for self-supervised deep learning with spectral contrastive loss.

Consistency of augmentation graph and network approximability in contrastive learning Provable guarantees for self-supervised deep learning with spectral contrastive loss

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.339549Z

Source-reported events for the cited work

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Observation 28b86ce0-bbb8-49d2-b07a-3e273081cf14 · outbound

This paper cites Elliptic partial differential equations of second order, volume 224.

Consistency of augmentation graph and network approximability in contrastive learning Elliptic partial differential equations of second order, volume 224

Reference 8

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Observation 297ae7af-6ad0-4731-9085-d7526bf45f7f · outbound

This paper cites A graph discretization of the Laplace-Beltrami operator.

Consistency of augmentation graph and network approximability in contrastive learning A graph discretization of the Laplace-Beltrami operator

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.311646Z

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Observation b6754660-87a1-4ac7-92c8-de9ca343d065 · outbound

This paper cites Error Estimates for Spectral Convergence of the Graph Laplacian on Random Geometric Graphs Toward the Laplace–Beltrami Operator.

Consistency of augmentation graph and network approximability in contrastive learning Error Estimates for Spectral Convergence of the Graph Laplacian on Random Geometric Graphs Toward the Laplace–Beltrami Operator

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.296016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b4de73bb-f45a-41a1-af5b-8cd8068972d3 · outbound

This paper cites Improved spectral convergence rates for graph laplacians on ε-graphs and k-nn graphs.

Consistency of augmentation graph and network approximability in contrastive learning Improved spectral convergence rates for graph laplacians on ε-graphs and k-nn graphs

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.279521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2300ee1d-7fa5-4a14-9bac-3fbb0ee5ae24 · outbound

This paper cites Spectral convergence of graph laplacian and heat kernel reconstruction in l∞ from random samples.

Consistency of augmentation graph and network approximability in contrastive learning Spectral convergence of graph laplacian and heat kernel reconstruction in l∞ from random samples

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.263593Z

Source-reported events for the cited work

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Observation 184002d6-79bf-41d2-8781-889a996aeae7 · outbound

This paper cites Spectral convergence of diffusion maps: Improved error bounds and an alternative normalization.

Consistency of augmentation graph and network approximability in contrastive learning Spectral convergence of diffusion maps: Improved error bounds and an alternative normalization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.248148Z

Source-reported events for the cited work

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Observation 705ab865-2ea4-4bd1-8fbf-d0d6549f6ac4 · outbound

This paper cites Large sample spectral analysis of graph-based multi-manifold clustering.

Consistency of augmentation graph and network approximability in contrastive learning Large sample spectral analysis of graph-based multi-manifold clustering

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.232282Z

Source-reported events for the cited work

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Observation 88a2d9e7-b384-4c1d-8f52-2b608ea5111c · outbound

This paper cites Lipschitz regularity of graph laplacians on random data clouds.

Consistency of augmentation graph and network approximability in contrastive learning Lipschitz regularity of graph laplacians on random data clouds

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.216783Z

Source-reported events for the cited work

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Observation 3c116701-ad39-46e1-97de-d38518698165 · outbound

This paper cites Refined shared nearest neighbors graph for combining multiple data clusterings.

Consistency of augmentation graph and network approximability in contrastive learning Refined shared nearest neighbors graph for combining multiple data clusterings

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.200868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 249ede15-65fe-4925-a962-a221ca7265c7 · outbound

This paper cites Spectral Neural Networks: Approximation Theory and Optimization Landscape.

Consistency of augmentation graph and network approximability in contrastive learning Spectral Neural Networks: Approximation Theory and Optimization Landscape

Reference 17

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Source-reported events for the cited work

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Observation 2da3a56d-4b8c-478c-b2fe-94f9ddcc8dfa · outbound

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

Consistency of augmentation graph and network approximability in contrastive learning Small relu networks are powerful memorizers: a tight analysis of memorization capacity

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.181773Z

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Observation 7e199ea2-e727-4924-8524-fa61aef8cd1f · outbound

This paper cites Probability inequalities for sums of bounded random variables.

Consistency of augmentation graph and network approximability in contrastive learning Probability inequalities for sums of bounded random variables

Reference 19

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Observation 47db814f-e5c0-4fda-a5d8-d1782777465b · outbound

This paper cites Riemannian geometry, volume 6.

Consistency of augmentation graph and network approximability in contrastive learning Riemannian geometry, volume 6

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.148230Z

Source-reported events for the cited work

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Observation acdc36f3-5b86-4f51-8e3e-95279d5b0fd3 · outbound

This paper cites Sobolev spaces on Riemannian manifolds , volume 1635.

Consistency of augmentation graph and network approximability in contrastive learning Sobolev spaces on Riemannian manifolds , volume 1635

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.131856Z

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Observation 76aebb2b-30c2-41c2-95be-15067a3ca822 · outbound

This paper cites A tutorial on spectral clustering.

Consistency of augmentation graph and network approximability in contrastive learning A tutorial on spectral clustering

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.116482Z

Source-reported events for the cited work

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Observation b90fbf84-2836-4af6-ac2a-e83c92057513 · outbound

This paper cites The approximation of one matrix by another of lower rank.Psychometrika, 1(3):211–218, 1936.

Consistency of augmentation graph and network approximability in contrastive learning The approximation of one matrix by another of lower rank.Psychometrika, 1(3):211–218, 1936

Reference 23

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This paper cites Exploring simple siamese representation learning.

Consistency of augmentation graph and network approximability in contrastive learning Exploring simple siamese representation learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T22:55:54.837814Z

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Observation 7776be14-d5b3-48db-b86e-aaaa847877bc · outbound

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

Consistency of augmentation graph and network approximability in contrastive learning Learning multiple layers of features from tiny images

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T22:55:54.843377Z

Source-reported events for the cited work

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Observation 912f6844-2459-4574-943c-f609de36e696 · outbound

This paper cites Big self- supervised models are strong semi-supervised learners.

Consistency of augmentation graph and network approximability in contrastive learning Big self- supervised models are strong semi-supervised learners

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.070329Z

Source-reported events for the cited work

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Observation ae8306cf-ec83-4f14-9f81-9ade8851a821 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Consistency of augmentation graph and network approximability in contrastive learning A simple framework for contrastive learning of visual representations

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-08T22:55:54.855734Z

Source-reported events for the cited work

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Observation c6338f33-a16a-4cca-949c-9c0b790310ca · outbound

This paper cites Error estimates for spectral convergence of the graph laplacian on random geometric graphs toward the laplace–beltrami operator.

Consistency of augmentation graph and network approximability in contrastive learning Error estimates for spectral convergence of the graph laplacian on random geometric graphs toward the laplace–beltrami operator

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.042633Z

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Observation 9751e003-7066-473b-b680-b988926e9acf · outbound

This paper cites The game theoretic p-laplacian and semi-supervised learning with few labels.

Consistency of augmentation graph and network approximability in contrastive learning The game theoretic p-laplacian and semi-supervised learning with few labels

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.024836Z

Source-reported events for the cited work

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Observation 29e79fa3-bd78-481b-a45f-bc8d23448608 · outbound

This paper cites Large deviations for sums of partly dependent random variables.

Consistency of augmentation graph and network approximability in contrastive learning Large deviations for sums of partly dependent random variables

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:55.009138Z

Source-reported events for the cited work

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Observation 188dac6c-f1a8-48a5-aa68-4b43bbb518cd · outbound

This paper cites Differential topology, volume 370.

Consistency of augmentation graph and network approximability in contrastive learning Differential topology, volume 370

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:54.992906Z

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Observation 5226737f-016a-4a17-9864-589a6db089b1 · outbound

This paper cites Lectures on classical differential geometry.

Consistency of augmentation graph and network approximability in contrastive learning Lectures on classical differential geometry

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:54.977549Z

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Observation 58d466a3-bc67-49c4-8e87-fec5678b117d · outbound

This paper cites first, second, third, fourth, fifth, sixth.

Consistency of augmentation graph and network approximability in contrastive learning first, second, third, fourth, fifth, sixth

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:55:54.960662Z

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