Paper Citation Record · LEDGER

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values

As of 18 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2512.19602.

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

pith.paper-citation-record.v1
2512.19602 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T14:45:18.813055Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T06:59:14.626274Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-06-30T07:04:21.534397Z

Reference resolution

35 of 35 outbound references displayed

verified exact0
verified fuzzy0
unresolved35
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bfab448b-4a72-4af3-943f-91e61195da73 · outbound

This paper cites Foundation models defining a new era in vision: a survey and outlook.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Foundation models defining a new era in vision: a survey and outlook

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.709535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8674c7a4-9378-4638-be0d-a13594718c06 · outbound

This paper cites Automated car- diovascular magnetic resonance image analysis with fully convolutional networks.Journal of cardiovascular magnetic resonance, 20(1):65, 2018.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Automated car- diovascular magnetic resonance image analysis with fully convolutional networks.Journal of cardiovascular magnetic resonance, 20(1):65, 2018

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.713592Z

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Observation 900ac5ee-81ce-48fe-8dca-7f0f1294b6a3 · outbound

This paper cites Predicting stroke outcome: a case for multimodal deep learn- ing methods with tabular and ct perfusion data.Artificial Intelligence in Medicine, 147:102719, 2024.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Predicting stroke outcome: a case for multimodal deep learn- ing methods with tabular and ct perfusion data.Artificial Intelligence in Medicine, 147:102719, 2024

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.717059Z

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Unavailable: canonical work link unavailable.

Observation 2d7937a3-3b39-4a59-bd40-1244972ddb4d · outbound

This paper cites Multi- centre, multi-vendor and multi-disease cardiac segmentation: the m&ms challenge.IEEE Transactions on Medical Imaging, 40(12):3543–3554, 2021.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Multi- centre, multi-vendor and multi-disease cardiac segmentation: the m&ms challenge.IEEE Transactions on Medical Imaging, 40(12):3543–3554, 2021

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.720229Z

Source-reported events for the cited work

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Observation 99009de1-ee74-4650-b26a-fe11eac77420 · outbound

This paper cites Xgboost: A scalable tree boosting system.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Xgboost: A scalable tree boosting system

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.723725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68419ab1-40c2-48a9-b673-d5e46c7d12ca · outbound

This paper cites Tip: Tabular-image pre- training for multimodal classification with incomplete data.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Tip: Tabular-image pre- training for multimodal classification with incomplete data

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.726910Z

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Unavailable: canonical work link unavailable.

Observation 7f70cb0a-89ea-40e1-bbcd-8d6aff52b018 · outbound

This paper cites Stil: Semi-supervised tabular-image learning for comprehensive task-relevant information exploration in multimodal classifi- cation.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Stil: Semi-supervised tabular-image learning for comprehensive task-relevant information exploration in multimodal classifi- cation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.730092Z

Source-reported events for the cited work

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Observation 777af6eb-f1e6-4d40-a53a-6d512028945a · outbound

This paper cites Prediction of pathological complete re- sponse to neoadjuvant chemotherapy in breast cancer using deep learning with integrative imaging, molecular and demo- graphic data.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Prediction of pathological complete re- sponse to neoadjuvant chemotherapy in breast cancer using deep learning with integrative imaging, molecular and demo- graphic data

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.732845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb8ddad3-8766-4aeb-9ec3-52781c73ed5b · outbound

This paper cites Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.Medical Image Analysis, 102: 103503, 2025.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.Medical Image Analysis, 102: 103503, 2025

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.738743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b0c508f-cb91-4331-87cf-915280a17d67 · outbound

This paper cites Time and the patient–physician relationship.Journal of gen- eral internal medicine, 14(Suppl 1):S34, 1999.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Time and the patient–physician relationship.Journal of gen- eral internal medicine, 14(Suppl 1):S34, 1999

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.741562Z

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Unavailable: canonical work link unavailable.

Observation 6e2c9752-1a1c-4f24-99e0-3d1db3655c65 · outbound

This paper cites an unresolved cited work.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.744409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f458b991-46d7-4c00-a5eb-cd71314148eb · outbound

This paper cites Why do tree-based models still outperform deep learning on typical tabular data?Advances in neural information processing systems, 35:507–520, 2022.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Why do tree-based models still outperform deep learning on typical tabular data?Advances in neural information processing systems, 35:507–520, 2022

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.747643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74308899-9b20-4665-8444-e05967d5cf52 · outbound

This paper cites Best of both worlds: Multimodal contrastive learning with tabular and imaging data.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Best of both worlds: Multimodal contrastive learning with tabular and imaging data

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.750513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2dd8fc48-3a4f-4401-837c-ba0654dae379 · outbound

This paper cites Can spatiotemporal 3d cnns retrace the history of 2d cnns and im- agenet? InProceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 6546–6555, 2018.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Can spatiotemporal 3d cnns retrace the history of 2d cnns and im- agenet? InProceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 6546–6555, 2018

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.753503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97dc18f6-626b-4085-8c2b-d254a6777704 · outbound

This paper cites Tables Guide Vision: Learning to See the Heart through Tabular Data.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Tables Guide Vision: Learning to See the Heart through Tabular Data

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.756176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6cb2bc8-e203-419f-9623-9a69fa157909 · outbound

This paper cites Deep residual learning for image recognition.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Deep residual learning for image recognition

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.759560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a66ae2de-5de0-4f68-ab39-2ea72eb82c59 · outbound

This paper cites TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.762662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ba0a0af-365d-4c49-a688-7e87d74cfb22 · outbound

This paper cites Dvm-car: A large-scale automotive dataset for visual marketing research and applications, 2023.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Dvm-car: A large-scale automotive dataset for visual marketing research and applications, 2023

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.765635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49ebc3e9-9c6d-4724-9a2d-91af5863b71d · outbound

This paper cites Tabular insights, visual impacts: transferring expertise from tables to images.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Tabular insights, visual impacts: transferring expertise from tables to images

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.768350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2314dd8a-fbf9-4352-a56f-abd27e69907b · outbound

This paper cites Table Foundation Models: on knowledge pre-training for tabular learning.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Table Foundation Models: on knowledge pre-training for tabular learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.771139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1490aec-121f-4a5c-8dc9-a4736c365b0e · outbound

This paper cites Matryoshka representation learning.Advances in Neural Information Processing Systems, 35:30233–30249, 2022.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Matryoshka representation learning.Advances in Neural Information Processing Systems, 35:30233–30249, 2022

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.774258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a952cae-d0e4-4d65-93a8-57ddbb0b3396 · outbound

This paper cites SimMLM: A Simple Framework for Multi-modal Learning with Missing Modality.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values SimMLM: A Simple Framework for Multi-modal Learning with Missing Modality

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.776911Z

Source-reported events for the cited work

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Observation 292ba139-b839-4822-8af4-98738103b52c · outbound

This paper cites Classification and regres- sion by randomforest.R news, 2(3):18–22, 2002.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Classification and regres- sion by randomforest.R news, 2(3):18–22, 2002

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.779736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e24e2c49-210b-4108-b1db-a63b5925b7a0 · outbound

This paper cites TabICL: A Tabular Foundation Model for In-Context Learning on Large Data.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values TabICL: A Tabular Foundation Model for In-Context Learning on Large Data

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.782771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d80995c8-5cbb-449b-9a5f-5fcb2d62c7f5 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Learning transferable visual models from natural language supervi- sion

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.785810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e829653-60c3-4532-9aca-3bc9f7767cdc · outbound

This paper cites A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to alzheimer’s disease.Neuroimage, 189:276–287, 2019.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to alzheimer’s disease.Neuroimage, 189:276–287, 2019

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.788605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be8b3850-39c1-4a0f-a3d3-5bb46a3500d4 · outbound

This paper cites Uk biobank: an open access resource for identifying the causes of a wide range of com- plex diseases of middle and old age.PLoS medicine, 12(3): e1001779, 2015.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Uk biobank: an open access resource for identifying the causes of a wide range of com- plex diseases of middle and old age.PLoS medicine, 12(3): e1001779, 2015

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.792024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f5b7f00-73a6-492a-a448-698da5d24086 · outbound

This paper cites Long-term cancer survival prediction using multimodal deep learning.Scientific Reports, 11(1):13505, 2021.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Long-term cancer survival prediction using multimodal deep learning.Scientific Reports, 11(1):13505, 2021

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.794928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15121c18-2f0a-4b75-9d4a-7bcba6f937ca · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.797817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a935e74-6a33-4dad-b7ff-9d17661416e3 · outbound

This paper cites Boosting Multimodal Learning via Disentangled Gradient Learning.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Boosting Multimodal Learning via Disentangled Gradient Learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.800781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7ad50e1-67db-4009-bce0-ab0f769e8f10 · outbound

This paper cites Daft: A universal module to interweave tabular data and 3d images in cnns.NeuroImage, 260:119505, 2022.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Daft: A universal module to interweave tabular data and 3d images in cnns.NeuroImage, 260:119505, 2022

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.803887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5d512a0-1d0d-4ba6-b670-1350f3d768d9 · outbound

This paper cites A closer look at deep learning methods on tabular datasets.arXiv preprint arXiv:2407.00956, 2024.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values A closer look at deep learning methods on tabular datasets.arXiv preprint arXiv:2407.00956, 2024

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.806761Z

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Observation b25cc000-d803-44ca-bf7a-c6212556fd2f · outbound

This paper cites A Survey of Large Language Models.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values A Survey of Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.809812Z

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Unavailable: canonical work link unavailable.

Observation 67a671e5-4563-4e0a-ae08-5693fa023bf7 · outbound

This paper cites Multi-transsp: Multimodal transformer for survival prediction of nasopharyngeal carcinoma patients.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values Multi-transsp: Multimodal transformer for survival prediction of nasopharyngeal carcinoma patients

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.813055Z

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Unavailable: canonical work link unavailable.

Observation 42783bed-2442-4247-9b15-0b30803e604f · outbound

This paper cites 2, 5, 6, 8, 1.

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values 2, 5, 6, 8, 1

Reference 252

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:18.735776Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 3deef451-4f67-42fb-a367-3584f6454cb1 · inbound

Beyond IID: How General Are Tabular Foundation Models, Really? cites this paper.

Beyond IID: How General Are Tabular Foundation Models, Really? No Data? No Problem: Robust Vision-Tabular Learning with Missing Values

Reference 148

Resolution: verified exact; local_arxiv, observed 2026-06-30T07:04:21.535881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.