Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:23:06.727778Z
Paper Citation Record · LEDGER
TabFlex: Scaling Tabular Learning to Millions with Linear Attention
As of 21 August 2026, this Paper Citation Record lists 80 of 80 outbound references and 4 inbound Pith citation observations for arXiv:2506.05584.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-27T13:45:07.339338Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T20:50:11.133540Z
Reference resolution
80 of 80 outbound references displayed
- verified exact2
- verified fuzzy37
- unresolved41
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b817498e-ed5b-4230-b8d1-5c9292dd378e · outbound
This paper cites write newline.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention write newline
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.065322Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 93a05e08-61dd-42b9-94ca-697b20e0802e · outbound
This paper cites GPT-4 Technical Report.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention GPT-4 Technical Report
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.072004Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1e4aa804-8985-4516-892d-a5e1a8b7e3a7 · outbound
This paper cites an unresolved cited work.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T10:23:08.257739Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation f68e4c6b-1de3-43c8-a48d-70ed842240ef · outbound
This paper cites MambaTab: A Plug-and-Play Model for Learning Tabular Data.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention MambaTab: A Plug-and-Play Model for Learning Tabular Data
Reference 4
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T10:23:07.195570Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 041d3fc8-7e9e-4ea7-9497-c9723b7607dd · outbound
This paper cites Transformers learn to implement preconditioned gradient descent for in-context learning.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Transformers learn to implement preconditioned gradient descent for in-context learning
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:08.238148Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4a449f67-c86a-4573-a991-8ade9b985dda · outbound
This paper cites an unresolved cited work.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Unresolved cited work
Reference 6
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T10:23:08.216689Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8a0c7c64-b24e-4ee6-9274-d7260ba49a49 · outbound
This paper cites Loan approval prediction based on machine learning approach.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Loan approval prediction based on machine learning approach
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:08.190370Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 433aeaf1-88fa-44c7-9911-48d489504671 · outbound
This paper cites Qwen Technical Report.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Qwen Technical Report
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.107328Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1fdd5875-b8e2-421c-833c-1ff9aa89b60f · outbound
This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.112340Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1a2cb109-d55e-4a1e-b53b-1775439a0057 · outbound
This paper cites Transformers as statisticians: Provable in-context learning with in-context algorithm selection.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Transformers as statisticians: Provable in-context learning with in-context algorithm selection
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:08.165284Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation ab51883f-04e6-499a-831c-4be4e517617a · outbound
This paper cites Longformer: The Long-Document Transformer.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Longformer: The Long-Document Transformer
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.122150Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a493816b-54b2-4d86-9808-6e3121e67d7c · outbound
This paper cites OpenML Benchmarking Suites.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention OpenML Benchmarking Suites
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.127733Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ae5d8beb-3267-4318-b79e-c85702ca208b · outbound
This paper cites M., Gir \'o -i Nieto, X., and Ioannidis, A.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention M., Gir \'o -i Nieto, X., and Ioannidis, A
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:08.141288Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation fcd03941-5e71-49f1-b641-f5ce6e636746 · outbound
This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:08.121938Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 573f1dfe-0055-476a-b5e2-e20ebb091fc4 · outbound
This paper cites Z., and Wu, J.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Z., and Wu, J
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:08.103775Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation aef7be51-d6fb-46d8-9600-6ce377f78233 · outbound
This paper cites and Guestrin, C.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention and Guestrin, C
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.148357Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 30b8fd06-a223-4ac6-9356-e0072305eadc · outbound
This paper cites Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.153832Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 63bda6d5-3641-48e6-959d-ff85f01e386c · outbound
This paper cites Qwen2-Audio Technical Report.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Qwen2-Audio Technical Report
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.159190Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5d724641-74ca-4238-bb5d-156e31e2e8d0 · outbound
This paper cites Support-vector networks.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Support-vector networks
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:08.071545Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 6faca99f-281f-4e34-bd49-43135fcf1318 · outbound
This paper cites and Hart, P.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention and Hart, P
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.170745Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9d6b89d4-0766-4767-9090-a8b0e8ea6410 · outbound
This paper cites an unresolved cited work.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Unresolved cited work
Reference 21
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T10:23:08.040098Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8e377e70-ca0d-4d95-aee9-6aa10c4f124b · outbound
This paper cites Flashattention-2: Faster attention with better parallelism and work partitioning.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Flashattention-2: Faster attention with better parallelism and work partitioning
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:08.024159Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4c865d6d-a105-411e-a84e-e7e54ab9973e · outbound
This paper cites and Gu, A.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention and Gu, A
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:08.003932Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 2e4838aa-ce49-4277-840a-a2ffba4ce608 · outbound
This paper cites Y., Ermon, S., Rudra, A., and Re, C.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Y., Ermon, S., Rudra, A., and Re, C
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.982718Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a52cff8b-3911-47bd-b4bc-935e47a66946 · outbound
This paper cites CausalLM is not optimal for in-context learning.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention CausalLM is not optimal for in-context learning
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.962940Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d9efcc46-14b8-4aef-a98c-80730f00f605 · outbound
This paper cites LIFT : Language-interfaced fine-tuning for non-language machine learning tasks.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention LIFT : Language-interfaced fine-tuning for non-language machine learning tasks
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.943462Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 01e7eb41-3150-42b5-a12c-068602dcd713 · outbound
This paper cites The Llama 3 Herd of Models.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention The Llama 3 Herd of Models
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.209784Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 481dbd0d-7620-47ff-b17d-08a0e1e323ba · outbound
This paper cites TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.215110Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8693a9bf-9664-41d0-a789-7eab717df857 · outbound
This paper cites Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.221142Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e1dbfe7f-8fe7-4618-b242-e79ff5c00e32 · outbound
This paper cites an unresolved cited work.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Unresolved cited work
Reference 30
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T10:23:07.926864Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation e54fbd43-4e49-4442-be52-11539beb207c · outbound
This paper cites Gemini: A Family of Highly Capable Multimodal Models.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Gemini: A Family of Highly Capable Multimodal Models
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.234238Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 51a8d04a-da06-4925-a6d1-77bc7f94aec8 · outbound
This paper cites Improving Input-label Mapping with Demonstration Replay for In-context Learning.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Improving Input-label Mapping with Demonstration Replay for In-context Learning
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.239553Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4591fffa-7787-46dd-a501-9e754b3f0a60 · outbound
This paper cites Revisiting deep learning models for tabular data.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Revisiting deep learning models for tabular data
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.906082Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 7c66920f-8104-4eb9-9e9a-6c80e6d241d9 · outbound
This paper cites TabR : Tabular deep learning meets nearest neighbors.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention TabR : Tabular deep learning meets nearest neighbors
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.888655Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation e3a52d4d-5962-424b-981b-eccd87aa0abb · outbound
This paper cites Why do tree-based models still outperform deep learning on typical tabular data? Advances in neural information processing systems, 35: 0 507--520, 2022.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Why do tree-based models still outperform deep learning on typical tabular data? Advances in neural information processing systems, 35: 0 507--520, 2022
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.868025Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 9a095128-6dd6-423a-a88b-58e0c279ca7a · outbound
This paper cites and Dao, T.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention and Dao, T
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.261731Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 13ae815f-7b58-48dd-becf-8e41f09e42bf · outbound
This paper cites K., Dao, T., Rudra, A., and Re, C.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention K., Dao, T., Rudra, A., and Re, C
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.823972Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 59a1cbd2-8ca7-4121-b7a6-76ebbc5d8c87 · outbound
This paper cites Efficiently modeling long sequences with structured state spaces.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Efficiently modeling long sequences with structured state spaces
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.805233Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 6e5bfa1c-ff3e-4358-9876-30ece75396a3 · outbound
This paper cites DeepFM: A Factorization-Machine based Neural Network for CTR Prediction.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.279107Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6d1d62b0-7a8b-463f-be04-b71f9b1cc222 · outbound
This paper cites J., Oktay, D., Lin, Z., Verkuil, R., Tran, V.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention J., Oktay, D., Lin, Z., Verkuil, R., Tran, V
Reference 40
- Resolution
- verified exact
- doi, observed 2026-08-07T10:23:06.789446Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 73aabe10-6bcb-4450-902d-a5942e550aab · outbound
This paper cites Deep residual learning for image recognition.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Deep residual learning for image recognition
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.291394Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3834e135-0a79-479a-ac87-6559466e6ae2 · outbound
This paper cites Tab PFN : A transformer that solves small tabular classification problems in a second.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Tab PFN : A transformer that solves small tabular classification problems in a second
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.759799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation fbb51f5b-5186-4c38-b7ad-f799a42d73af · outbound
This paper cites u ller, S., Purucker, L., Krishnakumar, A., K \.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention u ller, S., Purucker, L., Krishnakumar, A., K \
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.301650Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3626d02e-e574-4811-9a6f-aba428c28004 · outbound
This paper cites TabTransformer: Tabular Data Modeling Using Contextual Embeddings.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention TabTransformer: Tabular Data Modeling Using Contextual Embeddings
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.307742Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c317637a-e02f-4351-a171-ea8f38611ef2 · outbound
This paper cites E., Pollard, T.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention E., Pollard, T
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.717638Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 71da2975-5741-445d-b1ec-594e65bfc18f · outbound
This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Transformers are rnns: Fast autoregressive transformers with linear attention
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.317742Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 23c3617f-2e99-4782-807b-bd53d01b16e9 · outbound
This paper cites Lightgbm: A highly efficient gradient boosting decision tree.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Lightgbm: A highly efficient gradient boosting decision tree
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.323253Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cad481ce-5fda-475d-85f7-fba4d6e71426 · outbound
This paper cites Learning multiple layers of features from tiny images.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Learning multiple layers of features from tiny images
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.328834Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cb288fe6-622b-42b2-a4ef-d4393c09d118 · outbound
This paper cites MNIST handwritten digit database.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention MNIST handwritten digit database
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.652484Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 16053309-22c6-4f14-b5b8-418e80100c11 · outbound
This paper cites Classification and regression by randomforest.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Classification and regression by randomforest
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.633523Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation b16341c9-dd12-4ad8-a4e1-5005572042db · outbound
This paper cites In-Context Data Distillation with TabPFN.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention In-Context Data Distillation with TabPFN
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.343773Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c5c54713-870f-46cc-9f9c-0bb1dd3a1f1f · outbound
This paper cites and Ratajczak, W.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention and Ratajczak, W
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.615090Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation eb6b8719-75fa-4fa9-917f-adabdc5a6e6b · outbound
This paper cites C., Khandagale, S., Valverde, J., C, V.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention C., Khandagale, S., Valverde, J., C, V
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.586683Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 3fba9fa4-bd7a-4fb7-b7d4-feb4bb05d28c · outbound
This paper cites L., Gu, A., Fernando, A., Gulcehre, C., Pascanu, R., and De, S.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention L., Gu, A., Fernando, A., Gulcehre, C., Pascanu, R., and De, S
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.565353Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 6da4a17e-4df7-4f17-b988-e057036203bc · outbound
This paper cites G., Albalak, A., Arcadinho, S., Biderman, S., Cao, H., Cheng, X., Chung, M.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention G., Albalak, A., Arcadinho, S., Biderman, S., Cao, H., Cheng, X., Chung, M
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.545810Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 75f94ff4-29fc-4434-9c04-7870b162e21d · outbound
This paper cites Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.369435Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf15c6c0-3e7d-412c-89d7-af7c192e0bea · outbound
This paper cites V., and Gulin, A.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention V., and Gulin, A
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.528435Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation ee9494b4-b132-41c6-9d83-3a0018722efc · outbound
This paper cites The devil in linear transformer.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention The devil in linear transformer
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.511941Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 2ce90432-6bb4-4522-854e-fa0edcc7db1c · outbound
This paper cites an unresolved cited work.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Unresolved cited work
Reference 59
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T10:23:07.495025Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 451a2a0a-a8b2-4054-bf72-9f58786f088a · outbound
This paper cites L., Ma, J., and Fergus, R.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention L., Ma, J., and Fergus, R
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.390203Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 40fdd05b-516d-4edc-a06d-332ca6f5d0a5 · outbound
This paper cites E., Hinton, G.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention E., Hinton, G
Reference 61
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.395312Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 43b0cab2-2df8-4ae5-b648-d5c474b9be60 · outbound
This paper cites FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
Reference 62
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.400087Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8258b155-2756-43ea-882c-914cdcad3fa8 · outbound
This paper cites SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
Reference 63
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.405863Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation abc6012f-7f4f-46b4-bcf4-74a9abdde51a · outbound
This paper cites Retentive Network: A Successor to Transformer for Large Language Models.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Retentive Network: A Successor to Transformer for Large Language Models
Reference 64
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.410758Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 92a994ef-569a-40aa-8bfd-6d27c2e28a0c · outbound
This paper cites Mambular: A Sequential Model for Tabular Deep Learning.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Mambular: A Sequential Model for Tabular Deep Learning
Reference 65
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.415765Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 112d6863-f566-4844-9f6b-ae5b7f216029 · outbound
This paper cites an unresolved cited work.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Unresolved cited work
Reference 66
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T10:23:07.465497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation e2affd65-1df2-4f9a-b157-cf39c9268ed7 · outbound
This paper cites N., Bischl, B., and Torgo, L.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention N., Bischl, B., and Torgo, L
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.445740Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 14db07af-519a-40e5-8bb5-99ce5109c05b · outbound
This paper cites N., Kaiser, ., and Polosukhin, I.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention N., Kaiser, ., and Polosukhin, I
Reference 68
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.432802Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d75804d2-7baf-4656-8433-59e25f645e1f · outbound
This paper cites an unresolved cited work.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Unresolved cited work
Reference 69
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T10:23:07.415238Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 625d2afb-eb06-4c81-a4f5-14a1ff045cb6 · outbound
This paper cites Eegformer: A transformer--based brain activity classification method using eeg signal.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Eegformer: A transformer--based brain activity classification method using eeg signal
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.399415Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 79e79d2e-ed20-414b-b1c9-fa718283599e · outbound
This paper cites Fashion-MNIST : a novel image dataset for benchmarking machine learning algorithms.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Fashion-MNIST : a novel image dataset for benchmarking machine learning algorithms
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.381834Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 0f25105e-9902-4133-bb44-3535ad9565f1 · outbound
This paper cites Y.-C., Li, W., Gilani, A., Goan, H.-S., and Liu, H.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Y.-C., Li, W., Gilani, A., Goan, H.-S., and Liu, H
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.366521Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c7cddad3-3e38-40ac-88fc-29525964ec0d · outbound
This paper cites Q., Cirik, F.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Q., Cirik, F
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.349787Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a92b5bf7-a65b-4f12-b9ca-b4fb6aa0751d · outbound
This paper cites Feature selection using stochastic gates.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Feature selection using stochastic gates
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.330645Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 73eec504-33ec-4a0b-bee6-cd1dec16467a · outbound
This paper cites Gated linear attention transformers with hardware-efficient training.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Gated linear attention transformers with hardware-efficient training
Reference 75
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.313966Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8df86a36-8313-4643-bf26-2f9619731622 · outbound
This paper cites Vime: Extending the success of self-and semi-supervised learning to tabular domain.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Vime: Extending the success of self-and semi-supervised learning to tabular domain
Reference 76
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.297874Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation b4bd4ff5-8722-439a-b694-2d4805ecb7c0 · outbound
This paper cites an unresolved cited work.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Unresolved cited work
Reference 77
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T10:23:07.282228Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4259621e-f1c1-4681-95da-02c55bf42fd3 · outbound
This paper cites The hedgehog & the porcupine: Expressive linear attentions with softmax mimicry.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention The hedgehog & the porcupine: Expressive linear attentions with softmax mimicry
Reference 78
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.266971Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 851882f0-af1c-41ae-baef-e3a12d939cf7 · outbound
This paper cites Deep learning based recommender system: A survey and new perspectives.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention Deep learning based recommender system: A survey and new perspectives
Reference 79
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:23:06.651633Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fad94aba-f9ae-4341-8b24-58d3251da413 · outbound
This paper cites XTab : Cross-table pretraining for tabular transformers.
TabFlex: Scaling Tabular Learning to Millions with Linear Attention XTab : Cross-table pretraining for tabular transformers
Reference 80
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:23:07.239628Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Pith citing papers
Observation 34393b24-82ea-43c3-b4be-f9f5cc24ab05 · inbound
When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach cites this paper.
When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach TabFlex: Scaling Tabular Learning to Millions with Linear Attention
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T05:43:05.602844Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 60aab242-93a3-43d6-857b-e35848b61a19 · inbound
CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching cites this paper.
CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching TabFlex: Scaling Tabular Learning to Millions with Linear Attention
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T04:37:37.352182Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation e66aabdf-dcf3-414c-86a0-bb567e76f823 · inbound
Are Tabular Foundation Models Robust to Realistic Query Distribution Shifts in Microbiome Data? cites this paper.
Are Tabular Foundation Models Robust to Realistic Query Distribution Shifts in Microbiome Data? TabFlex: Scaling Tabular Learning to Millions with Linear Attention
Reference 24
- Resolution
- malformed identifier
- arxiv_id, observed 2026-07-04T16:39:57.987181Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 6828f47b-8727-4487-a6c9-c15f765c202a · inbound
Privacy Vulnerabilities of Attention Layers in Tabular Foundation Models and Protection of High-Risk Queries cites this paper.
Privacy Vulnerabilities of Attention Layers in Tabular Foundation Models and Protection of High-Risk Queries TabFlex: Scaling Tabular Learning to Millions with Linear Attention
Reference 15
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-04T20:50:11.135822Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.