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REVIEW 5 major objections 6 minor 81 references

iStructTab: Structured Feature Sequencing for Multimodal Learning of Image and Tabular Data

T0 review · 5 major / 6 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read The paper claims that ordering image-derived and tabular features before fusion is itself a learnable lever, and that a graph-based descriptor sequencing plus an order-aware transformer outperforms strong multimodal baselines across six…

desk verdict Useful empirical evidence that feature ordering matters in image-tabular transformers, but the CPP framing is unsupported and the CheXpert gap undercuts the 'best overall' claim. read the letter →

arxiv 2608.04348 v1 pith:GXEZAT7D submitted 2026-08-05 cs.CV cs.AIcs.LGstat.ML

Al Zadid Sultan Bin Habib , Md Younus Ahamed , Prashnna Gyawali , Gianfranco Doretto , Donald A. Adjeroh This is my paper · ORCID
classification cs.CVcs.AIcs.LGstat.ML
keywords multimodallearningfeaturesequencingColumnPermutationProblemgraphconvolutionimage-tabularfusiontransformerorderinglabelnoiserobustness
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that the order in which tabular and image-derived features are fed to a fusion transformer is not a nuisance but a learnable lever, and that choosing that order algorithmically improves prediction and robustness. It proposes iStructTab, which first computes per-feature statistical descriptors, refines them through a similarity graph, and sorts features by the norms of the refined descriptors to produce a permutation $\pi_{\mathrm{GEDS}}$. An order-aware transformer with memory tokens and a sequencing loss then consumes the features in that order. Across six image-plus-table benchmarks the resulting model reports the best average rank and the lowest average regret among sixteen methods, and it retains accuracy under noisy labels. If right, the paper establishes that structured feature sequencing can serve as a broadly effective component of multimodal fusion, not a niche preprocessing step.

What carries the argument

The mechanism is GEDS plus OEMT. GEDS computes, for each fused feature column, a descriptor $\mathbf{d}_j = [\mu_j, \sigma^2_j]^\top$, row-normalizes the descriptor matrix $\mathbf{D}$, builds a cosine-similarity graph with self-connections, symmetrically normalizes it to $\hat{\mathbf{A}}$, and applies one graph convolution $\mathbf{D}' = \sigma(\hat{\mathbf{A}}\mathbf{D}\mathbf{W})$. Each feature then receives a score $s_j = \|\mathbf{D}'_{j:}\|_2$, and the permutation is $\pi_{\mathrm{GEDS}} = \operatorname{argsort}(s_1, \ldots, s_m)$ in ascending order. OEMT projects the reordered features into tokens, uses a learnable order-aware pooling matrix to compress $m$ tokens into $k \ll m$ summary tokens, prepends $M$ learnable memory tokens, and processes the resulting sequence with a Linformer encoder that has linear attention. The classification head pools the memory tokens, and the total loss adds a sequencing term $\lambda_{\mathrm{FS}}\|\mathbf{s}' - \boldsymbol{\beta}\|_2^2$ that penalizes deviation from a linear importance ranking $\boldsymbol{\beta}$ defined by $\pi_{\mathrm{GEDS}}$.

What would settle it

A reader could falsify the central claim by computing $D(\pi_{\mathrm{GEDS}})$ and $D(\pi_{\mathrm{random}})$, $D(\pi_{\mathrm{Fisher}})$, and $D(\pi_{\mathrm{corr}})$ on the same fused features of any benchmark; if ascending-norm ordering does not systematically yield lower $D$ than those alternatives, the CPP grounding collapses. Separately, an ablation that feeds the same pipeline the reversed or shuffled $\pi_{\mathrm{GEDS}}$ and finds no accuracy drop would falsify the claim that the sequencing itself carries the gain.

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Extended reading notes

Core claim

The central claim is that multimodal fusion of images and tabular data can be formulated as a Column Permutation Problem (CPP), in which one seeks a permutation $\pi$ of fused features that minimizes a dispersion cost $D(\pi)=\sum_{i<j} w_{ij}|\pi(i)-\pi(j)|$, and that a cheap graph-based heuristic produces such a permutation. The heuristic, GEDS, builds a two-dimensional descriptor per feature (mean and variance), normalizes the descriptors, forms a cosine-similarity graph, applies one graph convolution, and sorts features by the ascending L2 norms of the refined descriptors. The resulting order is enforced by the OEMT backbone through order-aware pooling, learnable memory tokens, and a sequencing loss that pushes predicted order scores toward a linear rank target. The paper's evidence is comparative: across six benchmarks, iStructTab attains the best average rank (1.50 ± 0.76) and lowest average regret (2.21 ± 4.59), beats prior methods under label noise, and its ablations show that removing feature sequencing collapses accuracy on DVM from 99.29% to roughly 81–83%.

Load-bearing premise

The load-bearing premise is that sorting fused features by the ascending L2 norm of graph-convolved mean/variance descriptors genuinely approximates minimizing the dispersion objective $D(\pi)$; the paper does not measure $D(\pi)$ or compare this order with cheaper alternatives.

Editorial extensions

If this is right

  • The six-dataset average rank of 1.50 ± 0.76 and average regret of 2.21 ± 4.59 imply that GEDS plus OEMT is consistently competitive across medical and consumer image-tabular benchmarks, not just on one favorable dataset.
  • Ablations on DVM show that removing feature sequencing drops accuracy to roughly 81–83% from 99.29%, which implies the ordering mechanism is a primary driver of the method's accuracy in that setting, not a small correction.
  • Under 60% label noise on HAM10000 the model reaches 76.42% versus 70.1% for the best prior method, implying that the sequencing-based representation degrades more gracefully under corrupted supervision.
  • Because GEDS costs $O(Nm + m^2)$ on fused features and OEMT uses linear attention, the sequence-enforcement pipeline adds modest overhead, implying it can be attached to existing multimodal encoders without a compute cliff.
  • Successful explicit feature ordering suggests that permutation-aware designs, whether by averaging over orders or learning an order, should be part of the comparison suite for future multimodal methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, the sequencing mechanism is not tied to image-plus-table input: any concatenated embedding whose columns are exchangeable could be run through GEDS, and a cheap test would be applying iStructTab-style ordering to text-plus-audio or multi-table fusion.
  • The paper does not establish that $\pi_{\mathrm{GEDS}}$ actually minimizes $D(\pi)$; an editorial next experiment is replacing the heuristic with direct optimization of $D(\pi)$, for instance by differentiable sorting, and checking whether the gains grow, shrink, or vanish.
  • The robustness under label noise may come partly from the sequencing regularization term rather than from the order itself; ablating $\lambda_{\mathrm{FS}}$ at high noise levels would separate those effects, but the paper does not report that ablation.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper introduces iStructTab, a multimodal architecture for image-tabular classification. It proposes Graph-Enhanced Descriptor Sequencing (GEDS), which computes per-feature mean/variance descriptors, refines them via a graph convolution, and sorts the resulting L2 norms to produce a feature permutation πGEDS. This permutation is then used to reorder the fused feature vector before an Order-Aware Efficient Transformer with Memory Augmentation (OEMT) processes it. A sequencing loss aligns the model's predicted order scores with a linearly decreasing target β derived from πGEDS. The method is evaluated on six image-tabular benchmarks, with additional experiments on label noise, efficiency, calibration, and ablation. The paper reports the best average rank and lowest average regret among sixteen baselines, and a within-model ablation shows that removing feature sequencing degrades DVM accuracy substantially (96.85% to 80.80% untuned).

Significance. If the central claim is established, the paper would make a useful contribution by showing that explicit feature ordering, grounded in a combinatorial permutation objective, can improve multimodal fusion. The paper ships code and includes a broad empirical evaluation, which is commendable. The within-model ablation provides credible evidence that some form of feature sequencing matters for the proposed architecture. However, the specific grounding of GEDS in the Column Permutation Problem is not supported: the algorithm's score is never shown to relate to the dispersion objective D(π), and the reported gains are not isolated from simpler ordering heuristics. The significance of the contribution is therefore conditional on closing this gap.

major comments (5)
  1. [Section 3.1, Algorithm 1 steps 7–9, Eqs. (10)–(11)] The GEDS ordering score is never connected to the CPP objective D(π) in Eq. (1). The paper neither defines the weights w_ij operationally nor evaluates D(π) for πGEDS versus alternative orderings, so the abstract's claim that GEDS reduces inter-feature dispersion is unsupported. Please add a direct measurement of D(π) for πGEDS, random, original, variance-based, Fisher, and correlation-based orderings, and report both the dispersion values and the resulting classification accuracy.
  2. [Section 3.1, Eqs. (9)–(11) and Eq. (20)] The graph-convolution weight W in Eq. (9) cannot receive a gradient through the discrete argsort in Eq. (11), and the score vector s is not used in the total loss L_total of Eq. (20). Consequently, the graph refinement is effectively an untrained fixed transformation, contradicting the claim that GEDS 'learns' a structured ordering. Please either remove the learnability claim, provide an analysis of W's role, or propose a differentiable relaxation that allows the ordering to be trained.
  3. [Algorithm 2, lines 5–7 vs. Section 3.1, Eqs. (3)–(5)] GEDS is applied independently to each minibatch in Algorithm 2, so the permutation π and the target β change at every step. This is inconsistent with the dataset-level optimal permutation described in Eqs. (3)–(5), and it means the sequencing loss in Eq. (20) aligns the model to a moving target. Please clarify whether πGEDS is intended as a fixed precomputed permutation or as a per-batch adaptive ordering, and study the effect of using a fixed training-set permutation instead.
  4. [Section 4.E, Table 3] The ablation row 'iStructTab w/o feature sequencing' drops accuracy to 80.80%, which shows that the ordering matters, but it does not establish that GEDS's specific ordering is responsible. The table lacks a comparison against the original column order, random permutation, or simple statistical orderings under the same OEMT backbone. Without these controls, the observed gains cannot be attributed to GEDS rather than to the mere act of imposing a deterministic order. Please add such comparisons.
  5. [Section 4.D1, Table 1, CheXpert row] On CheXpert, iStructTab (76.15%) underperforms STiL (88.60%) by 12.45 points, which is the largest gap between the top two methods on any dataset. The paper's headline 'best overall' claim rests on rank aggregation, but this substantial per-dataset deficit is not discussed. Please add a comment on why the method underperforms on this benchmark and whether the average-rank summary masks a systematic weakness in certain settings.
minor comments (6)
  1. [Algorithm 1 and Algorithm 2] Algorithm 1 returns only πGEDS, but Algorithm 2 line 5 unpacks '(π, s) ← GEDS(F)'. Please make the return values consistent.
  2. [Section 4.G heading] The heading 'G. Inference Level Ablation on Calibration and Robustness' is duplicated verbatim in the text; please remove the duplicate.
  3. [Section 4.D3 and Figure 2] The asterisk on iStructTab* in Figure 2 and the efficiency plots is never explained. Please define what the asterisk indicates.
  4. [Section 4.E, Table 3] The row label 'iStructTab (ResNet-50 + GEDS)' omits OEMT, while other rows mention memory tokens and sequencing loss. Please clarify exactly which components are included in each ablation variant.
  5. [Supplementary Section I] The Turing-style human-model evaluation reports no human entries; the section is a protocol description rather than a completed experiment. Please state this explicitly or move the material to future work.
  6. [Code availability] The code URL in the footnote (github.com/zadid6pretam/iStructTab) differs from the URL in Table B.5 (github.com/annonym414/multimodal-tabular-anon). Please unify these references.

Circularity Check

1 steps flagged · score 2.0 of 10

Minor self-referential sequencing target; no evidence that the main accuracy results reduce to fitted quantities.

  1. fitted input called prediction [Sec. 3.2, Eqs. (19)-(20), Algorithm 2 lines 5-8]
    "The vector β∈R m explicitly defines the target sequence, with elements βπ(i) = 1− i−1 m−1 linearly ranking features from most to least important according to the optimal permutation π. ... In this work, the optimal permutation π is given by πGEDS from Algorithm 1."

    The sequencing target β is a deterministic transform of πGEDS, and πGEDS is produced by GEDS from the same feature matrix F that the model processes (Algorithm 2: (π,s)←GEDS(F)). The sequencing loss Ltotal = LCE + λFS‖s′−β‖²₂ therefore trains the network to reproduce an ordering derived from its own input; the 'predicted' ordering scores s′ are measured against a self-constructed target, not an external ground truth. This is a self-referential consistency regularizer rather than an independent supervisory signal. It does not leak test labels into training, and the classification loss LCE is trained separately, so the reported accuracy improvements are not forced by this loop. The circularity is confined to the auxiliary sequencing objective.

full rationale

The central empirical claim—that iStructTab achieves the best average rank and regret across six benchmarks—is supported by test-set comparisons against external and re-implemented baselines, and the classification loss is trained against ground-truth labels. The main derivation chain is not circular in the sense of fitting a parameter and then predicting that same fitted quantity as an accuracy result. One minor self-referential element exists: the sequencing-loss target β is defined from πGEDS, which is itself computed from the model's input features, making the 'sequencing prediction' a consistency check against a self-derived ordering rather than an independent target. This does not invalidate the benchmark results because the auxiliary loss is only a regularizer and the label-based objective is separate. The paper also contains a self-citation to the authors' prior TabSeq work as motivation for the importance of feature ordering, but the present paper's own ablation (Table 3: 'iStructTab w/o feature sequencing' drops to 80.80-82.98%) independently supports that premise, so the self-citation is not load-bearing. The larger concern—that the GEDS ordering score is not shown to minimize the CPP dispersion D(π) of Eq. (1)—is an evidentiary gap or correctness risk, not a circularity, since no result is assumed by construction in that step. Overall, no significant circularity is present beyond the minor self-referential sequencing target.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper pulls most of its architecture from prior work (ResNet, Linformer, memory tokens, graph convolution), and its main ledger entries are the unproven heuristics: that mean/variance descriptors plus cosine-similarity graph convolution produce an ordering that reduces the CPP dispersion objective, and that GEDS's learnable matrix W is actually learnable. The empirical comparison also imports baseline numbers from other papers, adding a comparability assumption. No new physical or theoretical entities are introduced, so 'order-aware memory tokens' and the GEDS score have no independent falsifiable handle beyond the reported benchmarks.

free parameters (4)
  • lambda_FS (feature-sequencing loss weight) = 0.0576 to 0.178 on Pet/Pok/CheX/HAM; ~1e-5 on DVM and DeepLesion
    Tuned per dataset with Optuna (Table A.1). Controls how strongly the model must match the GEDS-derived rank target β; the paper reports that removing the term costs 2-3 accuracy points on DVM.
  • GEDS graph-convolution weight W = not reported (initialized, effectively frozen)
    Eq. 9 describes W as learnable, but Algorithm 2's loss (Eq. 20) uses only the model's predicted s', and the hard argsort in Eq. 11 blocks gradients to W, so the final ordering depends on an untrained initialization.
  • Efficiency metric weights w = (0.40, 0.25, 0.20, 0.15)
    Hand-chosen in Sec. 4.D3 to emphasize computation; they drive the headline Eff=1.00 for iStructTab versus 0.40 for CHARMS.
  • OEMT architecture hyperparameters = d_model 128-512, heads 2-8, depth 1-3, k_OEMT 32-256, k_Lin 16-64
    Standard per-dataset Optuna tuning (Table A.1); included for completeness as they affect reported accuracy.
assumptions (5)
  • ad hoc to paper Mean and variance are sufficient per-feature descriptors for computing a useful sequence
    Eqs. 4-5 fix the descriptor as [μ, σ^2]; no justification is given for dropping higher moments or label information.
  • ad hoc to paper Cosine similarity between normalized descriptors captures inter-feature dispersion structure
    Eq. 7 builds the affinity graph from descriptor dot products; the connection to the dispersion weights w_ij in Eq. 1 is never specified.
  • ad hoc to paper Ascending L2 norms of refined descriptors approximate the CPP optimum
    Algorithm 1 steps 7-9 sort by score; the paper never shows that this minimizes D(π) nor measures D(π) empirically, despite the CPP framing of Eq. 1.
  • domain assumption Imported baseline numbers are directly comparable
    Table 1 marks DVM/HAM results for TIP, STiL, MMCL, DAFT etc. as taken from their papers; comparability requires identical splits and protocol, which the paper claims but does not demonstrate.
  • standard math CPP is NP-hard, so heuristic sequencing is justified
    Sec. 1 cites Johnson and Garey; this is background, not contested.

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Pith. "Pith review of iStructTab: Structured Feature Sequencing for Multimodal Learning of Image and Tabular Data." pith.science (2026). https://pith.science/paper/GXEZAT7D

@misc{pith2026260804348,
  author       = {Pith},
  title        = {Pith review of: iStructTab: Structured Feature Sequencing for Multimodal Learning of Image and Tabular Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GXEZAT7D}},
  note         = {Machine review of arXiv:2608.04348}
}
read the original abstract

Multimodal learning of images and tabular data is often impaired by ineffective representations, resulting in redundancy, dispersion, and generalization problems. To tackle this challenge, we introduce Graph-Enhanced Descriptor Sequencing (GEDS), a structured feature sequencing algorithm grounded in principles from the Column Permutation Problem (CPP). GEDS refines statistical descriptors of the features through similarity graph-based computations, systematically determining an effective feature sequencing. We incorporate GEDS within an order-aware efficient transformer framework, utilizing order-aware memory tokens that explicitly adhere to the derived feature sequencing via a dedicated loss function. Experimental results across multimodal benchmarks demonstrate that iStructTab effectively minimizes feature dispersion, improving predictive performance and robustness, and highlighting the significance of structured feature sequencing in multimodal learning.

Figures

Figures reproduced from arXiv: 2608.04348 by the authors.

Figure 1
Figure 1. Overview of the proposed iStructTab architecture. We first extract modality-specific representations from [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Comparison of multimodal image-tabular methods on the DVM dataset: (a) Weighted efficiency ranking [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. t-SNE visualization of fused embeddings produced by iStructTab on two representative medical imaging-based [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Inference level ablations of iStructTab on HAM10000 in the style of STiL [ [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

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Works this paper leans on

81 extracted references · 57 canonical work pages

  1. [1]

    In: SIGKDD

    Akiba, T., et al.: Optuna: A Next-Generation Hyperparameter Optimization Framework. In: SIGKDD. pp. 2623–2631 (2019)

    2019

  2. [2]

    In: AAAI

    Arik, S.Ö., Pfister, T.: TabNet: Attentive Interpretable Tabular Learning. In: AAAI. vol. 35, pp. 6679–6687 (2021)

    2021

  3. [3]

    https://www.kaggle.com/datasets/ashery/chexpert, accessed: Jun

    Ashery: Chexpert-v1.0-small. https://www.kaggle.com/datasets/ashery/chexpert, accessed: Jun. 12, 2025

    work page 2025

  4. [4]

    arXiv preprint arXiv:2106.15147 (2021)

    Bahri, D., et al.: SCARF: Self-Supervised Contrastive Learning Using Random Feature Corruption. arXiv preprint arXiv:2106.15147 (2021)

    arXiv 2021

  5. [5]

    IEEE Transactions on Pattern Analysis and Machine Intelligence41(2), 423–443 (2018)

    Baltrušaitis, T., et al.: Multimodal Machine Learning: A Survey and Taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence41(2), 423–443 (2018)

    work page 2018

  6. [6]

    In: Computer Graphics Forum

    Behrisch, M., et al.: Matrix Reordering Methods for Table and Network Visualization. In: Computer Graphics Forum. vol. 35, pp. 693–716. Wiley Online Library (2016)

    work page 2016

  7. [7]

    arXiv preprint arXiv:2508.09100 (2025)

    Brahmavar, S.B., et al.: Towards Universal Neural Inference. arXiv preprint arXiv:2508.09100 (2025)

    arXiv 2025

  8. [8]

    In: ICML

    Chen, D., et al.: Structure-Aware Transformer for Graph Representation Learning. In: ICML. pp. 3469–3489. PMLR (2022)

    work page 2022

Show all 81 references
  1. [9]

    In: ICML

    Chen, T., et al.: A Simple Framework for Contrastive Learning of Visual Representations. In: ICML. pp. 1597–1607. PmLR (2020)

    2020

  2. [10]

    In: IJCAI

    Chen, X., et al.: Multimodal Inference with Incremental Tabular Attributes. In: IJCAI. IJCAI Organization (2025)

    2025

  3. [11]

    Skin Imaging Collaboration (ISIC)

    Codella, N., et al.: Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the Int. Skin Imaging Collaboration (ISIC). arXiv preprint arXiv:1902.03368 (2019)

    2019 arXiv

  4. [12]

    Journal of Machine Learning Research 7(Jan), 1–30 (2006)

    Demšar, J.: Statistical Comparisons of Classifiers Over Multiple Data Sets. Journal of Machine Learning Research 7(Jan), 1–30 (2006)

    2006

  5. [13]

    https://www.kaggle.com/datasets/ bhavyadhingra00020/complete-pokemon-dataset-9th-gen-img-tabular, accessed: June

    Dhingra, B.: Complete Pokémon Dataset 9th Gen (Image + Tabular). https://www.kaggle.com/datasets/ bhavyadhingra00020/complete-pokemon-dataset-9th-gen-img-tabular, accessed: June. 12, 2025

    2025

  6. [14]

    In: Proc

    Ding, C., He, X.: K-Nearest-Neighbor Consistency in Data Clustering: Incorporating Local Information into Global Optimization. In: Proc. of the 2004 ACM Symposium on Applied Computing. pp. 584–589 (2004)

    2004

  7. [15]

    In: ICLR (2021), openReview: rGqJrXb0YB

    Dosovitskiy, A., et al.: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In: ICLR (2021), openReview: rGqJrXb0YB

    2021

  8. [16]

    In: ECCV

    Du, S., et al.: TIP: Tabular-Image Pre-Training for Multimodal Classification with Incomplete Data. In: ECCV. pp. 478–496. Springer (2024)

    2024

  9. [17]

    In: CVPR

    Du, S., et al.: STiL: Semi-Supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classification. In: CVPR. pp. 15549–15559 (2025)

    2025

  10. [18]

    In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd Int

    Duanmu, H., et al.: Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning with Integrative Imaging, Molecular and Demographic Data. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd Int. Con...

    2020

  11. [19]

    arXiv preprint arXiv:2110.07875 (2021) 10 ISTRUCTTAB

    Dwivedi, V .P., et al.: Graph Neural Networks with Learnable Structural and Positional Representations. arXiv preprint arXiv:2110.07875 (2021) 10 ISTRUCTTAB

    2021 arXiv

  12. [20]

    In: ICLR (2025)

    Eisenberg, R., et al.: COPER: Correlation-based Permutations for Multi-View Clustering. In: ICLR (2025)

    2025

  13. [21]

    arXiv preprint arXiv:2508.20906 (2025)

    Eremeev, D., et al.: Turning Tabular Foundation Models into Graph Foundation Models. arXiv preprint arXiv:2508.20906 (2025)

    2025 arXiv

  14. [22]

    NeurIPS26(2013)

    Fogel, F., et al.: Convex Relaxations for Permutation Problems. NeurIPS26(2013)

    2013

  15. [23]

    Journal of the American Statistical Association32(200), 675–701 (1937)

    Friedman, M.: The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. Journal of the American Statistical Association32(200), 675–701 (1937)

    1937

  16. [24]

    NeurIPS34, 18932–18943 (2021)

    Gorishniy, Y ., et al.: Revisiting Deep Learning Models for Tabular Data. NeurIPS34, 18932–18943 (2021)

    2021

  17. [25]

    NeurIPS35, 24991–25004 (2022)

    Gorishniy, Y ., et al.: On Embeddings for Numerical Features in Tabular Deep Learning. NeurIPS35, 24991–25004 (2022)

    2022

  18. [26]

    In: ICLR (2024)

    Gorishniy, Y ., et al.: TabR: Tabular Deep Learning Meets Nearest Neighbors. In: ICLR (2024)

    2024

  19. [27]

    In: ICLR (2025)

    Gorishniy, Y ., et al.: TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling. In: ICLR (2025)

    2025

  20. [28]

    NeurIPS33, 21271–21284 (2020)

    Grill, J.B., et al.: Bootstrap Your Own Latent-A New Approach to Self-Supervised Learning. NeurIPS33, 21271–21284 (2020)

    2020

  21. [29]

    Grinsztajn, L., et al.: Why Do Tree-Based Models Still Outperform Deep Learning on Typical Tabular Data? NeurIPS35, 507–520 (2022)

    2022

  22. [30]

    In: ICML

    Guo, C., et al.: On Calibration of Modern Neural Networks. In: ICML. pp. 1321–1330. PMLR (2017)

    2017

  23. [31]

    In: ICPR

    Habib, A.Z.S.B., et al.: TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering. In: ICPR. pp. 418–434. Springer (2024)

    2024

  24. [32]

    In: CVPR

    Hager, P., et al.: Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data. In: CVPR. pp. 23924–23935 (2023)

    2023

  25. [33]

    NeurIPS31 (2018)

    Han, B., et al.: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels. NeurIPS31 (2018)

    2018

  26. [34]

    In: CVPR

    He, K., et al.: Deep Residual Learning for Image Recognition. In: CVPR. pp. 770–778 (2016)

    2016

  27. [35]

    arXiv preprint arXiv:2207.01848 (2022)

    Hollmann, N., et al.: TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. arXiv preprint arXiv:2207.01848 (2022)

    2022 arXiv

  28. [36]

    Nature637(8045), 319–326 (2025)

    Hollmann, N., et al.: Accurate Predictions on Small Data with a Tabular Foundation Model. Nature637(8045), 319–326 (2025)

    2025

  29. [37]

    https://kaggle.com/competitions/ petfinder-adoption-prediction(2018), kaggle

    Howard, A., et al.: PetFinder.my Adoption Prediction. https://kaggle.com/competitions/ petfinder-adoption-prediction(2018), kaggle

    2018

  30. [38]

    In: Proc

    Huang, J., et al.: DVM-CAR: A Large-Scale Automotive Dataset for Visual Marketing Research and Applications. In: Proc. of the IEEE Int. Conf. on Big Data (BigData). pp. 4130–4137. IEEE (2022)

    2022

  31. [39]

    arXiv preprint arXiv:2012.06678 (2020)

    Huang, X., Khetan, A., Cvitkovic, M., Karnin, Z.: TabTransformer: Tabular Data Modeling Using Contextual Embeddings. arXiv preprint arXiv:2012.06678 (2020)

    2020 arXiv

  32. [40]

    In: AAAI

    Irvin, J., et al.: CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. In: AAAI. vol. 33, pp. 590–597 (2019)

    2019

  33. [41]

    In: CVPR

    Iscen, A., et al.: Learning with Neighbor Consistency for Noisy Labels. In: CVPR. pp. 4672–4681 (2022)

    2022

  34. [42]

    In: ICLR (2023)

    Jeffares, A., et al.: TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization. In: ICLR (2023)

    2023

  35. [43]

    In: ICML (2024)

    Jiang, J.P., et al.: Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images. In: ICML (2024)

    2024

  36. [44]

    WH Freeman (1979)

    Johnson, D.S., Garey, M.R.: Computers and Intractability: A Guide to the Theory of NP-Completeness. WH Freeman (1979)

    1979

  37. [45]

    NeurIPS30(2017)

    Ke, G., et al.: LightGBM: A Highly Efficient Gradient Boosting Decision Tree. NeurIPS30(2017)

    2017

  38. [46]

    XRDS: Crossroads, The ACM Magazine for Students23(2), 66–67 (2016)

    Khot, T.: Visualizing High-Dimensional Data. XRDS: Crossroads, The ACM Magazine for Students23(2), 66–67 (2016)

    2016

  39. [47]

    arXiv preprint arXiv:1609.02907 (2016)

    Kipf, T.N., Welling, M.: Semi-Supervised Classification with Graph Convolutional Networks. arXiv preprint arXiv:1609.02907 (2016)

    2016 arXiv

  40. [48]

    Statistical Analysis and Data Mining: The ASA Data Science Journal3(2), 70–91 (2010) 11 ISTRUCTTAB

    Liiv, I.: Seriation and Matrix Reordering Methods: An Historical Overview. Statistical Analysis and Data Mining: The ASA Data Science Journal3(2), 70–91 (2010) 11 ISTRUCTTAB

    2010

  41. [49]

    arXiv preprint arXiv:2409.04926 (2024)

    Lima, J.R., et al.: A ∆-Evaluation Function for Column Permutation Problems. arXiv preprint arXiv:2409.04926 (2024)

    2024 arXiv

  42. [50]

    arXiv preprint arXiv:2506.00813 (2025)

    Luo, J., et al.: TIME: TabPFN-Integrated Multimodal Engine for Robust Tabular-Image Learning. arXiv preprint arXiv:2506.00813 (2025)

    2025 arXiv

  43. [51]

    Journal of Machine Learning Research9(Nov), 2579–2605 (2008)

    Maaten, L.v.d., Hinton, G.: Visualizing Data Using t-SNE. Journal of Machine Learning Research9(Nov), 2579–2605 (2008)

    2008

  44. [52]

    https://www.kaggle.com/datasets/kmader/ nih-deeplesion-subset, accessed: Jan

    Mader, K.S.: NIH DeepLesion Subset. https://www.kaggle.com/datasets/kmader/ nih-deeplesion-subset, accessed: Jan. 22, 2025

    2025

  45. [53]

    https://www.kaggle.com/datasets/kmader/ skin-cancer-mnist-ham10000, accessed: Jan

    Mader, K.S.: Skin Cancer MNIST: HAM10000. https://www.kaggle.com/datasets/kmader/ skin-cancer-mnist-ham10000, accessed: Jan. 25, 2025

    2025

  46. [54]

    Princeton University (1963)

    Nemenyi, P.B.: Distribution-Free Multiple Comparisons. Princeton University (1963)

    1963

  47. [55]

    In: ICML

    Ngiam, J., et al.: Multimodal Deep Learning. In: ICML. vol. 11, pp. 689–696 (2011)

    2011

  48. [56]

    NeurIPS31(2018)

    Prokhorenkova, L., et al.: CatBoost: Unbiased Boosting with Categorical Features. NeurIPS31(2018)

    2018

  49. [57]

    In: ICML

    QU, J., et al.: TabICL: A Tabular Foundation Model for In-Context Learning on Large Data. In: ICML. PMLR (2025)

    2025

  50. [58]

    In: ICLR (2020)

    Rae, J.W., et al.: Compressive Transformers for Long-Range Sequence Modelling. In: ICLR (2020)

    2020

  51. [59]

    In: NeurIPS 2022 First Table Representation Workshop (2022)

    Somepalli, G., et al.: SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training. In: NeurIPS 2022 First Table Representation Workshop (2022)

    2022

  52. [60]

    arXiv preprint arXiv:2408.06291 (2024)

    Thielmann, A.F., et al.: Mambular: A Sequential Model for Tabular Deep Learning. arXiv preprint arXiv:2408.06291 (2024)

    2024 arXiv

  53. [61]

    Scientific Data5(1), 1–9 (2018)

    Tschandl, P., et al.: The HAM10000 Dataset, A Large Collection of Multi-Source Dermatoscopic Images of Common Pigmented Skin Lesions. Scientific Data5(1), 1–9 (2018)

    2018

  54. [62]

    NeurIPS30(2017)

    Vaswani, A., et al.: Attention is All You Need. NeurIPS30(2017)

    2017

  55. [63]

    NeurIPS33, 2232–2244 (2020)

    Veli ˇckovi´c, P., et al.: Pointer Graph Networks. NeurIPS33, 2232–2244 (2020)

    2020

  56. [64]

    NeurIPS28(2015)

    Vinyals, O., et al.: Pointer Networks. NeurIPS28(2015)

    2015

  57. [65]

    Wang, S., Li, B.Z., Khabsa, M., Fang, H., Ma, H.: Linformer: Self-attention with linear complexity (2020)

    2020

  58. [66]

    In: 2013 5th Int

    Wang, T., Guan, S.U.: Feature Ordering for Neural Incremental Attribute Learning Based on Fisher’s Linear Discriminant. In: 2013 5th Int. Conf. on Intel. Hum.-Machine Sys. and Cyb. vol. 2, pp. 507–510. IEEE (2013)

    2013

  59. [67]

    Mathematical Problems in Engineering2014(1), 365101 (2014)

    Wang, T., et al.: EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classifica- tion. Mathematical Problems in Engineering2014(1), 365101 (2014)

    2014

  60. [68]

    Wang, T., et al.: Linear Feature Sensibility for Output Partitioning in Ordered Neural Incremental Attribute Learning. In: Int. Conf. on Intel. Sci. and Big Data Eng. pp. 373–383. Springer (2015)

    2015

  61. [69]

    Symmetry7(1), 53–66 (2015)

    Wang, T., et al.: Output Effect Evaluation Based on Input Features in Neural Incremental Attribute Learning for Better Classification Performance. Symmetry7(1), 53–66 (2015)

    2015

  62. [70]

    In: 2015 IEEE 7th Int

    Wang, T., et al.: Regression Based on Neural Incremental Attribute Learning with Correlation-based Feature Ordering. In: 2015 IEEE 7th Int. Conf. on Cybernetics and Intelligent Systems (CIS) and IEEE Conf. on Robotics, Automation and Mechatronics (RAM). pp. 109–113. IEEE (2015)

    2015

  63. [71]

    In: CVPR

    Wei, H., et al.: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization. In: CVPR. pp. 13726–13735 (2020)

    2020

  64. [72]

    In: CVPR

    Wei, Z., et al.: Stronger Fewer & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation. In: CVPR. pp. 28619–28630 (2024)

    2024

  65. [73]

    NeuroImage 260, 119505 (2022)

    Wolf, T.N., et al.: DAFT: A Universal Module to Interweave Tabular Data and 3D Images in CNNs. NeuroImage 260, 119505 (2022)

    2022

  66. [74]

    In: CVPR

    Xia, X., et al.: Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples. In: CVPR. pp. 1833–1843 (2023)

    2023

  67. [75]

    NeurIPS32(2019)

    Xu, L., et al.: Modeling Tabular Data Using Conditional GAN. NeurIPS32(2019)

    2019

  68. [76]

    In: CVPR

    Yan, K., et al.: Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database. In: CVPR. pp. 9261–9270 (2018)

    2018

  69. [77]

    In: AAAI

    Yang, T., et al.: Graph Pointer Neural Networks. In: AAAI. vol. 36, pp. 8832–8839 (2022) 12 ISTRUCTTAB

    2022

  70. [78]

    NeurIPS 33, 11033–11043 (2020)

    Yoon, J., et al.: VIME: Extending the Success of Self-and Semi-Supervised Learning to Tabular Domain. NeurIPS 33, 11033–11043 (2020)

    2020

  71. [79]

    NeurIPS37, 18205–18224 (2024)

    Yu, Y ., et al.: Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise. NeurIPS37, 18205–18224 (2024)

    2024

  72. [80]

    NeurIPS30(2017)

    Zaheer, M., et al.: Deep Sets. NeurIPS30(2017)

    2017

  73. [81]

    OEMT heads

    Zhu, B., et al.: XTab: Cross-Table Pretraining for Tabular Transformers. In: ICML. pp. 43181–43204. PMLR (2023) 13 ISTRUCTTAB Supplementary Material iStructTab: Structured Feature Sequencing for Multimodal Learning of Image and Tabular Data This supplementary document supports...

    2023

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.