Paper Citation Record · LEDGER

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning

As of 10 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2512.02076.

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

pith.paper-citation-record.v1
2512.02076 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T19:21:25.144911Z

measured 29 of 29 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.

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

29 of 29 outbound references displayed

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unresolved29
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External citation measurements

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Outbound references

Observation 7e9817ad-9ceb-493f-ad01-5a3242d25d0a · outbound

This paper cites Geometric means in a novel vector space structure on symmetric positive-definite matrices.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Geometric means in a novel vector space structure on symmetric positive-definite matrices

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.052247Z

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Observation 6aa73ff5-0046-4202-8189-8e6495624109 · outbound

This paper cites Prediction by supervised principal components.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Prediction by supervised principal components

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.056535Z

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Observation df082aa0-f995-489e-b8a3-5b602b3b6303 · outbound

This paper cites Multimodal machine learning: A survey and taxonomy.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Multimodal machine learning: A survey and taxonomy

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.059933Z

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Observation e78220b4-e8bf-46d2-9cf8-98cb7ab0385f · outbound

This paper cites Geometry of the space of phylogenetic trees.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Geometry of the space of phylogenetic trees

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.063789Z

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Observation 4fb3bc11-641a-4367-b65c-8bf3ae3c75ed · outbound

This paper cites Randomprojectionindimensionality reduction: Applications to image and text data, in: Proceedings of the 7thACMSIGKDDInternationalConferenceonKnowledgeDiscovery and Data Mining, ACM.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Randomprojectionindimensionality reduction: Applications to image and text data, in: Proceedings of the 7thACMSIGKDDInternationalConferenceonKnowledgeDiscovery and Data Mining, ACM

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.067671Z

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Observation c22e1bbc-ab0a-4ae7-af1d-5e93fa5de753 · outbound

This paper cites Tools for fast metric data search in structural methods for image classification.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Tools for fast metric data search in structural methods for image classification

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.070985Z

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Observation 6a1ef21b-bae3-42ca-bdb9-3193aa0d69ea · outbound

This paper cites Computational topology for data analysis.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Computational topology for data analysis

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.074938Z

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Observation 7c868acd-208c-4db8-af06-4f78a4563d9a · outbound

This paper cites Non-euclideanstatistics for covariance matrices, with applications to diffusion tensor imaging.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Non-euclideanstatistics for covariance matrices, with applications to diffusion tensor imaging

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.078006Z

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Observation 043c264a-e3fb-4441-aac4-97e85dfe925a · outbound

This paper cites Modelingtime-varyingrandomobjects anddynamicnetworks.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Modelingtime-varyingrandomobjects anddynamicnetworks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.081274Z

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Observation 75390166-ec9b-4e25-aac3-2ff8249a0da4 · outbound

This paper cites Regression for non-euclidean data using distance matrices.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Regression for non-euclidean data using distance matrices

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.084421Z

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Observation 9a790cc6-0412-40fc-a1f0-9594c77fc9c7 · outbound

This paper cites Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.087611Z

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Observation f8bdd923-85b4-4925-9fa5-744b6e419e26 · outbound

This paper cites Robust nonparametric regression with metric-space valued output.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Robust nonparametric regression with metric-space valued output

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.090745Z

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Observation 0b2e69a9-1c6c-4e5b-ae01-28aaa3f551aa · outbound

This paper cites beta-vae: Learning basic visual concepts with a constrained variational framework, in: International Conference on Learning Representations (ICLR).

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning beta-vae: Learning basic visual concepts with a constrained variational framework, in: International Conference on Learning Representations (ICLR)

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.094095Z

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Observation 5bbb0515-d771-4991-afb4-5e932ca5aad8 · outbound

This paper cites Areviewonevaluationmetricsfor data classification evaluations.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Areviewonevaluationmetricsfor data classification evaluations

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.097235Z

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Observation b27dcc01-0de1-42be-a8a6-efa493482efc · outbound

This paper cites an unresolved cited work.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Unresolved cited work

Reference 15

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Observation 15b79ec3-91cb-4cae-9212-a7583d854462 · outbound

This paper cites Observable Covariance and Principal Observable Analysis for Data on Metric Spaces.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Observable Covariance and Principal Observable Analysis for Data on Metric Spaces

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.106917Z

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Observation 408ccde4-087c-4587-8b70-0ee8f9b52d4f · outbound

This paper cites Scaffold: Stochastic controlled averaging for federated learning, in: Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Scaffold: Stochastic controlled averaging for federated learning, in: Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.110362Z

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Observation 0ed5c128-ae65-4da8-8c79-39e364f0054d · outbound

This paper cites Overcom- ing catastrophic forgetting in neural networks.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Overcom- ing catastrophic forgetting in neural networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.113493Z

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Observation 22f08ee3-7511-4b10-9f26-9f4ed16670cb · outbound

This paper cites Federated optimization in heterogeneous networks, in: Proceedings of the 2nd Conference on Machine Learning and Systems (MLSys).

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Federated optimization in heterogeneous networks, in: Proceedings of the 2nd Conference on Machine Learning and Systems (MLSys)

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.116731Z

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Observation 549ea2ff-c525-4c8f-a0ce-8b286eaee5f6 · outbound

This paper cites Eclipse: Efficient long-range video retrieval using sight and sound, in: European Conference on Computer Vision, Springer.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Eclipse: Efficient long-range video retrieval using sight and sound, in: European Conference on Computer Vision, Springer

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.120019Z

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Observation c84765c7-e980-48e5-900e-92040f47ad8f · outbound

This paper cites Object oriented data analysis.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Object oriented data analysis

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.122858Z

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Observation 3348e3e9-dab9-41d9-a0d6-d6123b42c7a1 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data, in: Proceedings of the 20th International ConferenceonArtificialIntelligenceandStatistics(AISTATS),PMLR.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Communication-efficient learning of deep networks from decentralized data, in: Proceedings of the 20th International ConferenceonArtificialIntelligenceandStatistics(AISTATS),PMLR

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.125873Z

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Observation 526d4b15-de5d-4270-9991-b626bc3f4605 · outbound

This paper cites Functionalanalysis:anintroductiontometricspaces, Hilbert spaces, and Banach algebras.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Functionalanalysis:anintroductiontometricspaces, Hilbert spaces, and Banach algebras

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.128661Z

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Observation 5e58fbc7-02f5-4454-9b34-cff5d9e95cf9 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Representation Learning with Contrastive Predictive Coding

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.131978Z

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Observation abdb9329-eca6-4206-9748-524a10c85450 · outbound

This paper cites Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.135464Z

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Observation 53808e1c-c30d-49fe-8608-37b99482ad2d · outbound

This paper cites Aneffective multimodal image fusion method using mri and pet for alzheimer’s disease diagnosis.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Aneffective multimodal image fusion method using mri and pet for alzheimer’s disease diagnosis

Reference 26

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Observation 43e6acf3-481b-47bb-b2c5-e71a3315af43 · outbound

This paper cites Remaining useful life prediction of iiot-enabled complex industrial systems with hybrid fusionofmultipleinformationsources.IEEEInternetofThingsJournal 8, 9045–9058.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Remaining useful life prediction of iiot-enabled complex industrial systems with hybrid fusionofmultipleinformationsources.IEEEInternetofThingsJournal 8, 9045–9058

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.141672Z

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Observation b5d76b52-e22c-4404-861d-2555f2a006da · outbound

This paper cites Local polynomial regression for symmetric positive definite matrices.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Local polynomial regression for symmetric positive definite matrices

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.144911Z

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Observation 147de170-0a1f-479b-a34d-a21e9c1ee0bb · outbound

This paper cites Foundations and Trends in Machine Learning 14, 1–210.

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Foundations and Trends in Machine Learning 14, 1–210

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:25.103853Z

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Pith citing papers

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