Paper Citation Record · LEDGER

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees

As of 15 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2509.11054.

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pith.paper-citation-record.v1
2509.11054 v1

Coverage vector

measured 43 of 43 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-04T17:18:56.066975Z

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One-hop event checks from named stored sources.

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Source: cited_works

Reference resolution

43 of 43 outbound references displayed

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Outbound references

Observation c033a58e-9a91-48c0-a4c6-cd23f828e059 · outbound

This paper cites Emergence of invari- ance and disentanglement in deep representations.Journal of Machine Learning Research, 19(50):1–34, 2018.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Emergence of invari- ance and disentanglement in deep representations.Journal of Machine Learning Research, 19(50):1–34, 2018

Reference 1

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Observation c718ae55-f25c-4a0e-a8e4-0795a6c18ef4 · outbound

This paper cites The generalization ability of online algorithms for dependent data.IEEE Transactions on Information Theory, 59(1):573–587, 2012.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees The generalization ability of online algorithms for dependent data.IEEE Transactions on Information Theory, 59(1):573–587, 2012

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T17:18:50.753630Z

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Observation 6c7f5b1b-466a-42af-ada3-1060c9a6e047 · outbound

This paper cites An algorithm for calculating the capacity of arbitrary discrete memoryless channels.IEEE Transactions on Information Theory, 18(1):14–20, 1972.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees An algorithm for calculating the capacity of arbitrary discrete memoryless channels.IEEE Transactions on Information Theory, 18(1):14–20, 1972

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T17:18:50.855794Z

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Observation 13b3564e-8ad7-4fb9-9a16-8d7e32bc0160 · outbound

This paper cites Bartlett and Shahar Mendelson.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Bartlett and Shahar Mendelson

Reference 4

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Observation c09eaf6e-645a-4e6e-bc62-b16913873a13 · outbound

This paper cites Prentice-Hall, 1971.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Prentice-Hall, 1971

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T17:18:51.100205Z

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Observation 89478d8e-6266-45a6-b52b-b0652cde7515 · outbound

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Unresolved cited work

Reference 6

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Observation c2801fe3-e589-4134-b5ef-0e4335533878 · outbound

This paper cites Concentration Inequalities: A Nonasymptotic Theory of In- dependence.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Concentration Inequalities: A Nonasymptotic Theory of In- dependence

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T17:18:51.386173Z

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Observation ee574d96-91db-4608-8114-ba337df2682b · outbound

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

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees A simple framework for contrastive learning of visual representations

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T17:18:51.495630Z

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Observation f0967165-cc7c-4cd6-a7e2-8c7ca9ee346d · outbound

This paper cites Debiased contrastive learning.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Debiased contrastive learning

Reference 9

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Observation f635ab93-5def-478f-b870-f65a1a118b69 · outbound

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Unresolved cited work

Reference 10

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Observation 0270fd4d-fa07-432f-9dad-14fb6914fe7b · outbound

This paper cites Multiterminal source coding under logarithmic loss.IEEE Transactions on Infor- mation Theory, 60(1):740–761, 2014.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Multiterminal source coding under logarithmic loss.IEEE Transactions on Infor- mation Theory, 60(1):740–761, 2014

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T17:18:51.961691Z

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Cover and Joy A

Reference 12

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Observation 70d4d855-b007-4df9-a811-9b8e09962f48 · outbound

This paper cites Cambridge University Press, 2nd edition, 2011.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Cambridge University Press, 2nd edition, 2011

Reference 13

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This paper cites What to align in multimodal con- trastive learning? InInternational Conference on Learning Representations, 2025.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees What to align in multimodal con- trastive learning? InInternational Conference on Learning Representations, 2025

Reference 14

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Observation 6dad0285-4690-4e4a-a655-651c127f0c4f · outbound

This paper cites Rate-Distortion for Ranking with Incomplete Information.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Rate-Distortion for Ranking with Incomplete Information

Reference 15

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This paper cites Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc’Aurelio Ranzato, and Tomas Mikolov.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc’Aurelio Ranzato, and Tomas Mikolov

Reference 16

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Observation bb1d7481-334d-49c1-a94d-b972004eecac · outbound

This paper cites Gray.Vector Quantization and Signal Compression.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Gray.Vector Quantization and Signal Compression

Reference 17

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Observation 4367fc5e-bfc3-4d06-ae98-58f5b9c43e29 · outbound

This paper cites The information bottle- neck problem and its applications in machine learning.IEEE Journal on Selected Areas in Information Theory, 1(1):19– 38, 2020.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees The information bottle- neck problem and its applications in machine learning.IEEE Journal on Selected Areas in Information Theory, 1(1):19– 38, 2020

Reference 18

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Gray and David L

Reference 19

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Observation 2639bf60-bd90-4d24-9281-15172b925442 · outbound

This paper cites Dual alignment unsupervised domain adaptation for video–text retrieval.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Dual alignment unsupervised domain adaptation for video–text retrieval

Reference 20

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Observation 65c677b9-bb8d-4593-b609-f7646078cf6d · outbound

This paper cites Towards the generalization of contrastive self- supervised learning.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Towards the generalization of contrastive self- supervised learning

Reference 21

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Observation 138a6101-0d05-456e-824c-0bc9104d9d85 · outbound

This paper cites Cumulated gain- based evaluation of ir techniques.ACM Transactions on In- formation Systems, 20(4):422–446, 2002.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Cumulated gain- based evaluation of ir techniques.ACM Transactions on In- formation Systems, 20(4):422–446, 2002

Reference 22

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Observation 0f60d7bd-3257-4cc5-bc51-5fb93d32a14c · outbound

This paper cites Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision

Reference 23

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This paper cites Deep visual-semantic align- ments for generating image descriptions.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Deep visual-semantic align- ments for generating image descriptions

Reference 24

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Observation bf2fa6a7-b8a1-4d72-a0a3-e9e74dc72065 · outbound

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Generalization analysis for contrastive representation learning

Reference 25

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Observation c8039638-22ab-4dea-b551-116246473560 · outbound

This paper cites MIT Press, 2nd edition, 2018.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees MIT Press, 2nd edition, 2018

Reference 26

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Rep- resentation learning with contrastive predictive coding

Reference 27

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Observation 6e6118e5-6346-47d9-8f6f-7aab2b1e0a17 · outbound

This paper cites Estimation of entropy and mutual informa- tion.Neural Computation, 15(6):1191–1253, 2003.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Estimation of entropy and mutual informa- tion.Neural Computation, 15(6):1191–1253, 2003

Reference 28

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Quantization and the method ofk-means

Reference 29

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Learning transferable visual models from natural language supervision

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees A theoretical analy- sis of contrastive unsupervised representation learning

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Cambridge University Press, 2014

Reference 32

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Unresolved cited work

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This paper cites QUEST: Quadruple multimodal contrastive learning with constraints and self-penalization.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees QUEST: Quadruple multimodal contrastive learning with constraints and self-penalization

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This paper cites The information bottleneck method.37th Annual Allerton Con- ference on Communication, Control, and Computing, pages 368–377, 1999.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees The information bottleneck method.37th Annual Allerton Con- ference on Communication, Control, and Computing, pages 368–377, 1999

Reference 35

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Unresolved cited work

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Listwise approach to learning to rank: Theory and algorithm

Reference 37

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Observation 4d20c1a3-3b91-41f6-a864-2bffa5573826 · outbound

This paper cites Explaining and mitigating the modality gap in contrastive multimodal learning.arXiv preprint arXiv:2412.07909, 2024.

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Explaining and mitigating the modality gap in contrastive multimodal learning.arXiv preprint arXiv:2412.07909, 2024

Reference 38

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees Vector quantised contrastive learning

Reference 40

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees On the gen- eralization of multi-modal contrastive learning

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Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees The proof proceeds in four steps: S1

Reference 43

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