Paper Citation Record · LEDGER

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains

As of 20 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2505.14312.

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

pith.paper-citation-record.v1
2505.14312 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:39:27.501389Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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: arxiv_reference, observed 2026-06-30T07:04:21.380365Z

Reference resolution

50 of 50 outbound references displayed

verified exact1
verified fuzzy18
unresolved30
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 25079aeb-2bd9-48fd-a6da-fbdac7f95e2e · outbound

This paper cites Tabular data: Deep learning is not all you need.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Tabular data: Deep learning is not all you need

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:21.703996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf042313-a85c-475a-ada7-c31a9122a743 · outbound

This paper cites Deep neural networks and tabular data: A survey.IEEE transactions on neural networks and learning systems, 2022.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Deep neural networks and tabular data: A survey.IEEE transactions on neural networks and learning systems, 2022

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:21.782927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa43840d-9735-4a91-b734-ef517f757b52 · outbound

This paper cites Trust issues: Uncertainty estimation does not enable reliable ood detection on medical tabular data.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Trust issues: Uncertainty estimation does not enable reliable ood detection on medical tabular data

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:31.367808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0c3d85c9-2a97-4eef-8ace-9a1f6d6608f7 · outbound

This paper cites Deep learning and the electrocardiogram: review of the current state-of-the-art.EP Europace, 23(8):1179–1191, 2021.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Deep learning and the electrocardiogram: review of the current state-of-the-art.EP Europace, 23(8):1179–1191, 2021

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:31.221774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 55d14ab3-1467-47de-a941-9c6ff8053ecd · outbound

This paper cites Robust cognitive load detection from wrist-band sensors.Computers in Human Behavior Reports, 4:100116, 2021.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Robust cognitive load detection from wrist-band sensors.Computers in Human Behavior Reports, 4:100116, 2021

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:31.081889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 17715a81-9d4e-4cf0-963c-73190546f4b0 · outbound

This paper cites Sequential Deep Learning for Credit Risk Monitoring with Tabular Financial Data.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Sequential Deep Learning for Credit Risk Monitoring with Tabular Financial Data

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:22.133381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1136423f-7a5f-40ec-baf9-2c92e0192ecc · outbound

This paper cites DeepFM: A Factorization-Machine based Neural Network for CTR Prediction.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:22.241148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d13babd-41d6-4472-b345-12abf5e76674 · outbound

This paper cites Deep learning based recommender system: A survey and new perspectives.ACM computing surveys (CSUR), 52(1):1–38, 2019.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Deep learning based recommender system: A survey and new perspectives.ACM computing surveys (CSUR), 52(1):1–38, 2019

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:30.898940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f238f382-718b-4707-870e-5144271181a8 · outbound

This paper cites A survey of evolution in predictive models and impacting factors in customer churn.Advances in Data Science and Adaptive Analysis, 9(03):1750007, 2017.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains A survey of evolution in predictive models and impacting factors in customer churn.Advances in Data Science and Adaptive Analysis, 9(03):1750007, 2017

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:30.742819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2580c5ca-cb65-4071-bf54-04b0e06e7aa1 · outbound

This paper cites A customer churn prediction model based on xgboost and mlp.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains A customer churn prediction model based on xgboost and mlp

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:30.581608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8ba68e47-61cc-476c-a456-c8a055078798 · outbound

This paper cites Revisiting deep learning models for tabular data.Advances in Neural Information Processing Systems, 34:18932– 18943, 2021.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Revisiting deep learning models for tabular data.Advances in Neural Information Processing Systems, 34:18932– 18943, 2021

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:22.644062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc4be789-d193-4c6a-8fdf-b7ff41b9a2e7 · outbound

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

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:22.747651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e251cf74-fc5f-4072-84c7-0858954c098e · outbound

This paper cites Hyper- fast: Instant classification for tabular data.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Hyper- fast: Instant classification for tabular data

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:30.411425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1036d777-8eeb-41be-883a-4de054f5d93d · outbound

This paper cites Why Tabular Foundation Models Should Be a Research Priority.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Why Tabular Foundation Models Should Be a Research Priority

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:22.961853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61a9599b-8fb5-4db6-b6b5-6f53ae4153a0 · outbound

This paper cites Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:23.084528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb38703f-078d-4e55-acaa-0aa0e3cac8bf · outbound

This paper cites D2r2: Diffusion-based repre- sentation with random distance matching for tabular few-shot learning.Advances in Neural Information Processing Systems, 37:36890–36913, 2024.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains D2r2: Diffusion-based repre- sentation with random distance matching for tabular few-shot learning.Advances in Neural Information Processing Systems, 37:36890–36913, 2024

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:30.265683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 92f6e59f-bae2-4dfe-8ecb-b690323eac6f · outbound

This paper cites Arithmetic feature interaction is necessary for deep tabular learning.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Arithmetic feature interaction is necessary for deep tabular learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:30.099532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7fc19a41-9047-4fd9-b499-24d216ba9081 · outbound

This paper cites Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:23.669046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10f2f73c-1932-4b95-9ed5-1ecb35e95a76 · outbound

This paper cites Representation space augmentation for effective self-supervised learning on tabular data.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Representation space augmentation for effective self-supervised learning on tabular data

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:29.959703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9f55f56a-e363-4fcb-9b6f-d92bf7a2c0a8 · 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.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Why do tree-based models still outperform deep learning on typical tabular data?Advances in neural information processing systems, 35:507–520, 2022

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:24.049170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9688ae96-5e95-46d9-b420-5df4a332d5f1 · outbound

This paper cites When do neural nets outperform boosted trees on tabular data?Advances in Neural Information Processing Systems, 36, 2024.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains When do neural nets outperform boosted trees on tabular data?Advances in Neural Information Processing Systems, 36, 2024

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:24.259272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 818d7e91-40d9-4a1e-879b-ff809693a475 · outbound

This paper cites TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:24.417107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fa9ca92-da38-4136-95bd-4bdef3bc749c · outbound

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

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains A closer look at deep learning on tabular data.arXiv preprint arXiv:2407.00956, 2024

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:24.622409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e020b020-2a81-431a-b8f2-17e5c1dea109 · outbound

This paper cites Benchmarking distribution shift in tabular data with tableshift.Advances in Neural Information Processing Systems, 36, 2024.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Benchmarking distribution shift in tabular data with tableshift.Advances in Neural Information Processing Systems, 36, 2024

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:29.793253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 84168dce-1a93-4bb6-8935-8c615c8111f2 · outbound

This paper cites Towards heterogeneous long-tailed learning: Benchmarking, metrics, and toolbox.Advances in Neural Information Processing Systems, 37:73098–73123, 2024.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Towards heterogeneous long-tailed learning: Benchmarking, metrics, and toolbox.Advances in Neural Information Processing Systems, 37:73098–73123, 2024

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:29.616676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e5f38a45-c3e0-4f2a-89e5-a33064e3fa7d · outbound

This paper cites On embeddings for numerical features in tabular deep learning.Advances in Neural Information Processing Systems, 35:24991–25004, 2022.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains On embeddings for numerical features in tabular deep learning.Advances in Neural Information Processing Systems, 35:24991–25004, 2022

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:25.175500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3116fcf7-ac57-49fc-8e41-d37d51347a24 · outbound

This paper cites T2g-former: organizing tabular features into relation graphs promotes heterogeneous feature interaction.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains T2g-former: organizing tabular features into relation graphs promotes heterogeneous feature interaction

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:29.392091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b1d6e69e-3366-4182-9e87-1486c3f1b488 · outbound

This paper cites A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:39:27.800091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3bd762aa-b54c-46b3-8e65-df212e08b52a · outbound

This paper cites A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasets.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasets

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:25.352617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a80e8505-8013-46b2-b9fb-fbb429d484c8 · outbound

This paper cites TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:25.454631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8bec08a-753a-42ba-bc28-f7f79f154676 · outbound

This paper cites Towards quantifying the effect of datasets for benchmarking: A look at tabular machine learning, 2024.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Towards quantifying the effect of datasets for benchmarking: A look at tabular machine learning, 2024

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:29.177677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0cc4f27e-329c-41fb-b57f-c7c16455299f · outbound

This paper cites OpenML-Python: an extensible Python API for OpenML.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains OpenML-Python: an extensible Python API for OpenML

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:25.733245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 385ad7fa-8b6a-44bb-be41-789a431c7cf2 · outbound

This paper cites Pedregosa, G.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Pedregosa, G

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:25.826830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92c29a70-cb80-48f6-bd71-68e0c28b8638 · outbound

This paper cites The choice of scaling technique matters for classification performance.Applied Soft Computing, 133:109924, 2023.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains The choice of scaling technique matters for classification performance.Applied Soft Computing, 133:109924, 2023

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:28.959404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8d63be72-13ff-4db4-a451-3f4ac9bced81 · outbound

This paper cites Random forests.Machine learning, 45:5–32, 2001.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Random forests.Machine learning, 45:5–32, 2001

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:26.027681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 846b57fb-93d4-4cf9-94c3-4fe33d9d17a4 · outbound

This paper cites Xgboost: A scalable tree boosting system.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Xgboost: A scalable tree boosting system

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:26.131840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c1dd3c8-f951-4ec4-8c93-708e3002f68a · outbound

This paper cites Catboost: unbiased boosting with categorical features.Advances in neural information processing systems, 31, 2018.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Catboost: unbiased boosting with categorical features.Advances in neural information processing systems, 31, 2018

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:26.215361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dc5eeb8-bced-4d60-9ef3-87490b1f0907 · outbound

This paper cites Lightgbm: A highly efficient gradient boosting decision tree.Advances in neural information processing systems, 30, 2017.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Lightgbm: A highly efficient gradient boosting decision tree.Advances in neural information processing systems, 30, 2017

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:26.337262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14ed2db8-e471-468c-9f76-dd2c1d77a720 · outbound

This paper cites TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:26.439488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44a0dbe8-0733-4e09-a793-0417d9ecbc63 · outbound

This paper cites Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:26.512378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2c9ef15-af47-4570-81f5-3cbabc79b3db · outbound

This paper cites SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:26.596297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77e609b9-3474-44db-abe5-63d66421daea · outbound

This paper cites AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:26.672316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb53b119-d434-4608-a5b1-fac3e742ba4a · outbound

This paper cites Scaling tree-based automated machine learning to biomedical big data with a feature set selector.Bioinformatics, 36(1):250–256, 2020.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Scaling tree-based automated machine learning to biomedical big data with a feature set selector.Bioinformatics, 36(1):250–256, 2020

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:28.751367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dee4e4c5-c365-4ee9-9ec0-5ba2f55048d4 · outbound

This paper cites Algorithms for hyper- parameter optimization.Advances in neural information processing systems, 24, 2011.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Algorithms for hyper- parameter optimization.Advances in neural information processing systems, 24, 2011

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:26.837527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c6d2ba5-1ea9-4534-ab28-c4d0842e8dbc · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Optuna: A next-generation hyperparameter optimization framework

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:26.965513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcb1d361-7e6c-4395-b616-cbc24405b2f0 · outbound

This paper cites An inductive bias for tabular deep learning.Advances in Neural Information Processing Systems, 36, 2024.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains An inductive bias for tabular deep learning.Advances in Neural Information Processing Systems, 36, 2024

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:28.551830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b0a9c73c-63a3-4858-9b16-0e3682a7bb54 · outbound

This paper cites ExcelFormer: A neural network surpassing GBDTs on tabular data.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains ExcelFormer: A neural network surpassing GBDTs on tabular data

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:27.182549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38c765a9-2e52-466a-8ae5-ef2aea93cf46 · outbound

This paper cites Better by default: Strong pre-tuned mlps and boosted trees on tabular data.Advances in Neural Information Processing Systems, 37:26577–26658, 2024.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Better by default: Strong pre-tuned mlps and boosted trees on tabular data.Advances in Neural Information Processing Systems, 37:26577–26658, 2024

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:27.277046Z

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Observation 0e744a90-de0e-44dc-9f4d-508af5489123 · outbound

This paper cites Nonuniform fast fourier transforms using min-max interpolation.IEEE transactions on signal processing, 51(2):560–574, 2003.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Nonuniform fast fourier transforms using min-max interpolation.IEEE transactions on signal processing, 51(2):560–574, 2003

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:39:28.359129Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6b34129a-268a-48bd-a566-73a0c35a0dae · outbound

This paper cites Accurate predictions on small data with a tabular foundation model.Nature, 637(8045):319–326, 2025.

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains Accurate predictions on small data with a tabular foundation model.Nature, 637(8045):319–326, 2025

Reference 50

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T15:39:28.144280Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 59073487-33c5-40d7-bfe9-a349e4d243d2 · inbound

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

Beyond IID: How General Are Tabular Foundation Models, Really? MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains

Reference 95

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:04:21.382396Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.