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Paper Citation Record · LEDGER

Similarity search generalisation in contrastive learning with InfoNCE loss

As of 10 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2607.09405.

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

pith.paper-citation-record.v1
2607.09405 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T03:15:48.800246Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

25 of 25 outbound references displayed

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Outbound references

Observation 7f9dcaf7-0100-4f5b-a8bc-c416e5554a85 · outbound

This paper cites Spectrally-normalized margin bounds for neural networks.

Similarity search generalisation in contrastive learning with InfoNCE loss Spectrally-normalized margin bounds for neural networks

Reference 1

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:04b144de712bc3a2ff95a880d575dbe349081a32625e1458252f5b0cec3cfac0

Observation 08a5a9c6-4e29-4e88-b92d-cbfcf50d40bd · outbound

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

Similarity search generalisation in contrastive learning with InfoNCE loss A simple framework for contrastive learning of visual representations

Reference 2

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:c08d5d2eade989ae9d97ad6521e292137acb4e8f7b2ae8a678063c858902e109

Observation baebb9f3-f25d-42fe-a5aa-b868131961e0 · outbound

This paper cites Generalization bounds with logarithmic negative-sample dependence for adversarial contrastive learning.

Similarity search generalisation in contrastive learning with InfoNCE loss Generalization bounds with logarithmic negative-sample dependence for adversarial contrastive learning

Reference 3

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:5a05282aa8f4a1910bfc6e8ac269ec8aea2cc11db912c8557e0dd420f799a11e

Observation 34d1f346-3e3f-4581-bbb6-a79025c59421 · outbound

This paper cites Size-independent sample complexity of neural networks.

Similarity search generalisation in contrastive learning with InfoNCE loss Size-independent sample complexity of neural networks

Reference 4

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:a0df18011fb4a7b77ac37a891386577b799a66d3d6f6f84238deb388d900cb3e

Observation f6807394-9590-42d9-8747-e89f111649c6 · outbound

This paper cites A rescaling-invariant lipschitz bound based on path-metrics for modern relu network parameterizations.

Similarity search generalisation in contrastive learning with InfoNCE loss A rescaling-invariant lipschitz bound based on path-metrics for modern relu network parameterizations

Reference 5

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:9990cb51fbeccef69087d0a1edb54197ab2999bd84a08cc260666715da1181e9

Observation ca501fa9-1cb9-4fc4-961c-be6b5804e1c1 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

Similarity search generalisation in contrastive learning with InfoNCE loss Momentum contrast for unsupervised visual representation learning

Reference 6

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:98edfd6dc99cfa20663527fc30415ad574aa21cf240bd023889f21a1f58e197a

Observation 30f7c4ac-a3b1-45b1-94a3-0240613e7f2b · outbound

This paper cites Data-efficient image recognition with contrastive predictive coding.

Similarity search generalisation in contrastive learning with InfoNCE loss Data-efficient image recognition with contrastive predictive coding

Reference 7

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:568d7e98db979f3b1da6156403ca89558f133a252bdad09fbf28df3370a1722c

Observation 6f064df0-20c4-4747-8e8e-643ee9ae6d17 · outbound

This paper cites Generalization analysis for supervised contrastive representation learning under non-iid settings.

Similarity search generalisation in contrastive learning with InfoNCE loss Generalization analysis for supervised contrastive representation learning under non-iid settings

Reference 8

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:8ae8b7c6b20b449dcf571b555ec4b1478b59a6e7e788a1592b4dbc99d9c687a7

Observation 101609fb-e712-4e8f-ad60-c3b720520ed9 · outbound

This paper cites Supervised contrastive learning.

Similarity search generalisation in contrastive learning with InfoNCE loss Supervised contrastive learning

Reference 9

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:ca4caa8d565cb30ad68cb9ae9230ba2c4355f46ff2f68501d72e78baabcb2b41

Observation e1853d89-f913-4323-b89a-6c4fce5bf52c · outbound

This paper cites Data-dependent generalization bounds for multi-class classification.

Similarity search generalisation in contrastive learning with InfoNCE loss Data-dependent generalization bounds for multi-class classification

Reference 10

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:2a683cf66ac6803156d3caf54015a7229d67482f0e1b8ff95ac6d692c2050df8

Observation 0c2529cd-e2b9-4ede-88d7-98e3048ae22d · outbound

This paper cites Generalization analysis for contrastive representation learning.

Similarity search generalisation in contrastive learning with InfoNCE loss Generalization analysis for contrastive representation learning

Reference 11

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:6a2c64b9228b28aecac6c4f6d761971f34ed5f8000b43411d2ff3c5e8da6da6b

Observation 10805c23-5ea9-410a-ab97-706700902335 · outbound

This paper cites A vector-contraction inequality for rademacher complexities.

Similarity search generalisation in contrastive learning with InfoNCE loss A vector-contraction inequality for rademacher complexities

Reference 12

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Observation 4d98f86f-6f21-4587-888a-ff6d15186bca · outbound

This paper cites Foundations of Machine Learning.

Similarity search generalisation in contrastive learning with InfoNCE loss Foundations of Machine Learning

Reference 13

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:47c18567941b7f41e839d54bbf9f1d6be4f7cb8fd2bd90e74fe9e34696fa19fd

Observation 1bd4d9ba-e001-43b2-a305-5650440233c3 · outbound

This paper cites Norm-based capacity control in neural networks.

Similarity search generalisation in contrastive learning with InfoNCE loss Norm-based capacity control in neural networks

Reference 14

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:ff144b5df98d211e104ff0c662e495546baebbe36d3dde637865a2452f819470

Observation 514eba9a-08bf-451d-b214-ccaa267318ef · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Similarity search generalisation in contrastive learning with InfoNCE loss Learning Transferable Visual Models From Natural Language Supervision

Reference 15

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:32b87e198854521380d408588f293b298338d683aee2bc9b7489edcf01e130f4

Observation 5eb53e52-8b2c-41ec-b487-d1900dc61c21 · outbound

This paper cites A theoretical analysis of contrastive unsupervised representation learning.

Similarity search generalisation in contrastive learning with InfoNCE loss A theoretical analysis of contrastive unsupervised representation learning

Reference 16

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:763c65747b2b1059f69abe30bc50cacb45873b33b52cde2c1346e54e8afcd4ac

Observation 9333f937-2133-41b3-9f4c-e813817b2002 · outbound

This paper cites Contrastive multiview coding.

Similarity search generalisation in contrastive learning with InfoNCE loss Contrastive multiview coding

Reference 17

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:aa2e6d6c70ca3e1ab18d01496027c4c0d1df96ae4ca56e1f3e68f226c41cc8c2

Observation 3787c0e6-1c61-413f-808c-b77cfffcf586 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Similarity search generalisation in contrastive learning with InfoNCE loss Representation Learning with Contrastive Predictive Coding

Reference 18

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:d9000eec23051d12031d1564d91c5041070f6c1d324cb754d6e2b0c70020b027

Observation 151f408d-8a49-4537-95fa-ef92ce810a4b · outbound

This paper cites High-dimensional statistics: A non-asymptotic viewpoint, volume 48.

Similarity search generalisation in contrastive learning with InfoNCE loss High-dimensional statistics: A non-asymptotic viewpoint, volume 48

Reference 19

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:c617755e20fe5983b8ed7b641abb9583525a3c73afd45441d289af5f4a636611

Observation bce8601d-43e5-46cb-977a-a999fa5f32b5 · outbound

This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere.

Similarity search generalisation in contrastive learning with InfoNCE loss Understanding contrastive representation learning through alignment and uniformity on the hypersphere

Reference 20

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:a3bf3ccb49b14a5a12e908b75378046a0767cb7642511f5d5cf25842c1f527e6

Observation ce2d0208-fe70-4965-a93d-623f65edf165 · outbound

This paper cites Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere.

Similarity search generalisation in contrastive learning with InfoNCE loss Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

Reference 21

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:c2bf0715bd0a4e7cf46ab9d22278e0c29207edca7b43f8560ee782b67814d888

Observation 09635cb9-d737-4fbc-b7f9-c989f3af52fb · outbound

This paper cites Unsupervised feature learning via non-parametric instance discrimination.

Similarity search generalisation in contrastive learning with InfoNCE loss Unsupervised feature learning via non-parametric instance discrimination

Reference 22

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:57e53f1bb9ef1072ec6e26fa2079192152fd49f02277e57b9f04945a619ebaa4

Observation ce2bb7b1-c0e5-41ad-8a3d-9cdb5cdb654b · outbound

This paper cites Tuning large neural networks via zero-shot hyperparameter transfer.

Similarity search generalisation in contrastive learning with InfoNCE loss Tuning large neural networks via zero-shot hyperparameter transfer

Reference 23

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:244a9503f2f4736a3b503b8d21d6da727d2786c296dcbd1516576c12984969b7

Observation 203b5d78-18a7-41ac-9170-ea08cd35a7f7 · outbound

This paper cites Decoupled contrastive learning.

Similarity search generalisation in contrastive learning with InfoNCE loss Decoupled contrastive learning

Reference 24

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source=arxiv_source observed=2026-07-13T03:15:48.800246Z digest=sha256:9283731b59faec3e236f99329a8f241ab57bf0bbd989f816c58fad389c94363c

Observation 6150db59-94bb-4903-b967-13935d3647e6 · outbound

This paper cites Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, and Wieland Brendel.

Similarity search generalisation in contrastive learning with InfoNCE loss Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, and Wieland Brendel

Reference 25

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