Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T19:21:25.144911Z
Paper Citation Record · LEDGER
As of 8 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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T19:21:25.144911Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
29 of 29 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 7e9817ad-9ceb-493f-ad01-5a3242d25d0a · outbound
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
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Observation 6aa73ff5-0046-4202-8189-8e6495624109 · outbound
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Prediction by supervised principal components
Reference 2
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Observation df082aa0-f995-489e-b8a3-5b602b3b6303 · outbound
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Multimodal machine learning: A survey and taxonomy
Reference 3
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Observation e78220b4-e8bf-46d2-9cf8-98cb7ab0385f · outbound
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Geometry of the space of phylogenetic trees
Reference 4
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Observation 4fb3bc11-641a-4367-b65c-8bf3ae3c75ed · outbound
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
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Observation c22e1bbc-ab0a-4ae7-af1d-5e93fa5de753 · outbound
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
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Observation 6a1ef21b-bae3-42ca-bdb9-3193aa0d69ea · outbound
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Computational topology for data analysis
Reference 7
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Observation 7c868acd-208c-4db8-af06-4f78a4563d9a · outbound
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
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Observation 043c264a-e3fb-4441-aac4-97e85dfe925a · outbound
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Modelingtime-varyingrandomobjects anddynamicnetworks
Reference 9
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Observation 75390166-ec9b-4e25-aac3-2ff8249a0da4 · outbound
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Regression for non-euclidean data using distance matrices
Reference 10
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Observation 9a790cc6-0412-40fc-a1f0-9594c77fc9c7 · outbound
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
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Observation f8bdd923-85b4-4925-9fa5-744b6e419e26 · outbound
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Robust nonparametric regression with metric-space valued output
Reference 12
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Observation 0b2e69a9-1c6c-4e5b-ae01-28aaa3f551aa · outbound
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
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Observation 5bbb0515-d771-4991-afb4-5e932ca5aad8 · outbound
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Areviewonevaluationmetricsfor data classification evaluations
Reference 14
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Observation b27dcc01-0de1-42be-a8a6-efa493482efc · outbound
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
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
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Observation 408ccde4-087c-4587-8b70-0ee8f9b52d4f · outbound
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
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Observation 0ed5c128-ae65-4da8-8c79-39e364f0054d · outbound
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Overcom- ing catastrophic forgetting in neural networks
Reference 18
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Observation 22f08ee3-7511-4b10-9f26-9f4ed16670cb · outbound
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
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Observation 549ea2ff-c525-4c8f-a0ce-8b286eaee5f6 · outbound
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
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Observation c84765c7-e980-48e5-900e-92040f47ad8f · outbound
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Object oriented data analysis
Reference 21
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Observation 3348e3e9-dab9-41d9-a0d6-d6123b42c7a1 · outbound
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
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Observation 526d4b15-de5d-4270-9991-b626bc3f4605 · outbound
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Functionalanalysis:anintroductiontometricspaces, Hilbert spaces, and Banach algebras
Reference 23
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Observation 5e58fbc7-02f5-4454-9b34-cff5d9e95cf9 · outbound
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Representation Learning with Contrastive Predictive Coding
Reference 24
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Observation abdb9329-eca6-4206-9748-524a10c85450 · outbound
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
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Observation 53808e1c-c30d-49fe-8608-37b99482ad2d · outbound
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
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
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Observation b5d76b52-e22c-4404-861d-2555f2a006da · outbound
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Local polynomial regression for symmetric positive definite matrices
Reference 28
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Observation 147de170-0a1f-479b-a34d-a21e9c1ee0bb · outbound
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning Foundations and Trends in Machine Learning 14, 1–210
Reference 2021
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No inbound Pith citation observations are available.