Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning | Paper Citation Record

Observation c4469811-5a6d-4ffd-8571-8809bbd59811 · outbound

This paper cites Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:36:09.713419Z

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Observation db5e885e-b2a7-4e31-8eb2-4adf4cd42d59 · outbound

This paper cites Missing Value Imputation on Multidimensional Time Series.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Missing Value Imputation on Multidimensional Time Series

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:36:09.661647Z

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Observation f5906bd9-77a5-4dfb-81a5-6de4cc45eed0 · outbound

This paper cites TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:36:09.707554Z

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Observation 0a4c68a7-1e11-4789-80c3-31eb5c098683 · outbound

This paper cites TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:36:09.697941Z

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This paper cites Mul- timodal federated learning: A survey.Sensors, 23(15):6986.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Mul- timodal federated learning: A survey.Sensors, 23(15):6986

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.436284Z

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Observation fc28eb6c-ef1e-49eb-bfb8-6ef1310fbd96 · outbound

This paper cites Feddat: An approach for foundation model fine- tuning in multi-modal heterogeneous federated learning.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Feddat: An approach for foundation model fine- tuning in multi-modal heterogeneous federated learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.429758Z

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Observation 2b971266-bca7-485e-b17b-ee1eb9629f93 · outbound

This paper cites Fedmsplit: Correlation- adaptive federated multi-task learning across multimodal split networks.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Fedmsplit: Correlation- adaptive federated multi-task learning across multimodal split networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.477740Z

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Observation 31b2deb7-3a8f-4935-8201-da8a03dc1810 · outbound

This paper cites Probabilistic conformal distilla- tion for enhancing missing modality robustness.Advances in Neural Information Processing Systems, 37:36218–36242.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Probabilistic conformal distilla- tion for enhancing missing modality robustness.Advances in Neural Information Processing Systems, 37:36218–36242

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.402784Z

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Observation 5e274c98-9fce-46d2-a621-5067d9b3d993 · outbound

This paper cites Dam: Towards a foundation model for time se- ries forecasting.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Dam: Towards a foundation model for time se- ries forecasting

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.448773Z

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Observation e87a94a6-5e86-4637-ad43-0dc70f8d0a94 · outbound

This paper cites A decoder-only foundation model for time-series forecasting.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning A decoder-only foundation model for time-series forecasting

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.395983Z

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Observation 4d67c6e1-a160-45b5-9bb5-41e6f41c0a14 · outbound

This paper cites LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:36:09.672455Z

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Observation 2bff4b16-efa6-4f7e-92ed-a116d2a285a3 · outbound

This paper cites Gp-vae: Deep probabilistic time series impu- tation.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Gp-vae: Deep probabilistic time series impu- tation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.392106Z

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Observation 6259f46f-07de-4fbc-848d-99712e3f11ca · outbound

This paper cites MOMENT: A Family of Open Time-series Foundation Models.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning MOMENT: A Family of Open Time-series Foundation Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:36:09.655524Z

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Observation 96e315b7-48e4-4cee-8b0d-fc5b69c26fc3 · outbound

This paper cites Fusemoe: Mixture-of-experts transformers for flexi- modal fusion.Advances in Neural Information Processing Systems, 37:67850–67900.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Fusemoe: Mixture-of-experts transformers for flexi- modal fusion.Advances in Neural Information Processing Systems, 37:67850–67900

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.474244Z

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Observation 8d213379-1b92-491d-9227-871abeaf31f0 · outbound

This paper cites Denoising diffu- sion probabilistic models.Advances in neural information processing systems, 33:6840–6851.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Denoising diffu- sion probabilistic models.Advances in neural information processing systems, 33:6840–6851

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.470710Z

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Observation 121b45b9-d4b5-49e4-9a94-14130e1b7d16 · outbound

This paper cites Multimodal federated learning: Concept, methods, applications and future directions.Information Fusion, 112:102576.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Multimodal federated learning: Concept, methods, applications and future directions.Information Fusion, 112:102576

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.439561Z

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Observation 6d9d4e01-13b8-49c9-9ce1-8e2c812bdfb7 · outbound

This paper cites MIMIC-IV.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning MIMIC-IV

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.399217Z

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Observation e402e8dc-0fba-4b63-bb93-8a664e5c6c54 · outbound

This paper cites Sleep-edf database expanded (version 1.0.0).

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Sleep-edf database expanded (version 1.0.0)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.442882Z

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Observation f8b75ade-93e7-4ebf-b2c6-aeebc3b24962 · outbound

This paper cites Cyin: Cyclic informative latent space for bridging complete and incomplete multimodal learning.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Cyin: Cyclic informative latent space for bridging complete and incomplete multimodal learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.388520Z

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Observation 2a25ec38-0d88-40e2-ba3d-2009aeaf3e16 · outbound

This paper cites Timer: Generative Pre-trained Transformers Are Large Time Series Models.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:36:09.690612Z

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Observation 9c9270e9-2dbd-427f-bc27-2be0ba5de809 · outbound

This paper cites Mul- tivariate time series imputation with generative adversarial networks.Advances in neural information processing systems, 31.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Mul- tivariate time series imputation with generative adversarial networks.Advances in neural information processing systems, 31

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.459020Z

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Observation 5f5a8683-2663-467c-9178-2ce32de11049 · outbound

This paper cites Nguyen, Trong Nghia Hoang, Thanh Trung Huynh, Quoc Viet Hung Nguyen, and Phi Le Nguyen.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Nguyen, Trong Nghia Hoang, Thanh Trung Huynh, Quoc Viet Hung Nguyen, and Phi Le Nguyen

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.416905Z

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Observation 1a6b582f-d91a-4d54-96d1-190f54de8538 · outbound

This paper cites Fedmac: Tackling partial-modality missing in federated learn- ing with cross-modal aggregation and contrastive regulariza- tion.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Fedmac: Tackling partial-modality missing in federated learn- ing with cross-modal aggregation and contrastive regulariza- tion

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.455709Z

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Observation 49945ec3-0558-4050-86ec-d53a590b25f3 · outbound

This paper cites Fedmm: Feder- ated multi-modal learning with modality heterogeneity in computational pathology.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Fedmm: Feder- ated multi-modal learning with modality heterogeneity in computational pathology

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.406165Z

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Observation b57fdd43-86e2-4615-b894-354f62c80cb9 · outbound

This paper cites A contrastive learning and graph-based approach for missing modalities in multimodal federated learning.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning A contrastive learning and graph-based approach for missing modalities in multimodal federated learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.384103Z

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Observation c1c47557-cf4c-4973-aca5-e64ebcad8a0e · outbound

This paper cites Federated prompt-tuning with heterogeneous and incomplete multimodal client data.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Federated prompt-tuning with heterogeneous and incomplete multimodal client data

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.483549Z

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Observation 9f45ae73-b525-4a40-986f-628b8e7627ac · outbound

This paper cites Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:36:09.681862Z

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Observation a503122e-a97b-4005-aea6-7e33a2346e2e · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-11T20:36:09.643240Z

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Observation 5451b363-931e-4ef9-b7ea-f906efd4535f · outbound

This paper cites Csdi: Conditional score-based diffusion models for probabilistic time series imputation.Advances in neural in- formation processing systems, 34:24804–24816.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Csdi: Conditional score-based diffusion models for probabilistic time series imputation.Advances in neural in- formation processing systems, 34:24804–24816

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.409944Z

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Observation 7b25993c-f975-4f60-bc8d-19ad6737fbb9 · outbound

This paper cites PTB-XL: A large publicly available elec- trocardiography dataset.Scientific Data.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning PTB-XL: A large publicly available elec- trocardiography dataset.Scientific Data

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.432773Z

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Observation 5b77d142-847c-43e3-8d61-3db9c64935d9 · outbound

This paper cites Deep Learning for Multivariate Time Series Imputation: A Survey.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Deep Learning for Multivariate Time Series Imputation: A Survey

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:36:09.648681Z

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Observation ba228601-692d-4fe7-b056-dea9c6f4b696 · outbound

This paper cites Distribution- consistent modal recovering for incomplete multimodal learn- ing.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Distribution- consistent modal recovering for incomplete multimodal learn- ing

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.480715Z

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Observation bc5eb00e-b87f-4a7d-8aeb-db9e60241156 · outbound

This paper cites TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

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Resolution: verified exact; arxiv_id, observed 2026-05-16T19:09:17.286568Z

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Observation 503329cd-1b19-46db-9260-926929442148 · outbound

This paper cites A unified framework for multi-modal federated learning.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning A unified framework for multi-modal federated learning

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.452087Z

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Observation 334d6f94-f81b-438a-ad34-4b9c6896a8ba · outbound

This paper cites Promptcast: A new prompt- based learning paradigm for time series forecasting.IEEE Transactions on Knowledge and Data Engineering, 36(11): 6851–6864.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Promptcast: A new prompt- based learning paradigm for time series forecasting.IEEE Transactions on Knowledge and Data Engineering, 36(11): 6851–6864

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.380203Z

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Observation 87017466-eb5c-458f-986e-8866bff216dc · outbound

This paper cites Frequency-aware generative models for multivariate time se- ries imputation.Advances in Neural Information Processing Systems, 37:52595–52623.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Frequency-aware generative models for multivariate time se- ries imputation.Advances in Neural Information Processing Systems, 37:52595–52623

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.419942Z

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Observation 48863e58-078a-44e9-9549-f6b01ff7e541 · outbound

This paper cites Estimating missing data in temporal data streams using multi- directional recurrent neural networks.IEEE Transactions on Biomedical Engineering, 66(5):1477–1490.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Estimating missing data in temporal data streams using multi- directional recurrent neural networks.IEEE Transactions on Biomedical Engineering, 66(5):1477–1490

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.423223Z

Source-reported events for the cited work

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Observation d9307656-0ece-4ab7-acc2-5a8424ae0e5f · outbound

This paper cites Multimodal Federated Learning via Contrastive Representation Ensemble.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Multimodal Federated Learning via Contrastive Representation Ensemble

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Resolution: verified exact; arxiv_id, observed 2026-05-11T20:36:09.732659Z

Source-reported events for the cited work

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Observation b29f24ac-0102-4869-bcaa-db5ccdd038c8 · outbound

This paper cites Robust multimodal federated learning for incomplete modalities.Computer Communications, 214:234–243.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Robust multimodal federated learning for incomplete modalities.Computer Communications, 214:234–243

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.486158Z

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Observation b2e2a36b-bc88-481f-8293-afbecec6ec49 · outbound

This paper cites Flex-moe: Modeling arbitrary modality combination via the flexible mixture-of-experts.Advances in Neural Information Processing Systems, 37:98782–98805.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Flex-moe: Modeling arbitrary modality combination via the flexible mixture-of-experts.Advances in Neural Information Processing Systems, 37:98782–98805

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.463811Z

Source-reported events for the cited work

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Observation a47ebe09-7279-4c9d-a5d7-8e9f4535ec73 · outbound

This paper cites an unresolved cited work.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-05-26T16:57:39.426438Z

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Observation aa56d4c7-8ee7-4896-8a56-3267867b5dfe · outbound

This paper cites Mul- timodal federated learning on iot data.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Mul- timodal federated learning on iot data

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.467092Z

Source-reported events for the cited work

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Observation b98f1dee-6fa9-4b8b-9c6b-218d0d8a7cfb · outbound

This paper cites Missing data imputation via conditional generator and cor- relation learning for multimodal brain tumor segmentation.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Missing data imputation via conditional generator and cor- relation learning for multimodal brain tumor segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T16:57:39.413765Z

Source-reported events for the cited work

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