arxiv: 2605.03430 · v1 · submitted 2026-05-05 · 💻 cs.LG · cs.AI

Recognition: unknown

DynaTab: Dynamic Feature Ordering as Neural Rewiring for High-Dimensional Tabular Data

Al Zadid Sultan Bin Habib , Gianfranco Doretto , Donald A. Adjeroh

Authors on Pith no claims yet

Pith reviewed 2026-05-07 17:02 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords dynamic feature orderingneural rewiringhigh-dimensional tabular datatabular deep learningfeature permutationsequence-sensitive modelsorder-aware architecture

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The pith

DynaTab dynamically reorders features to make sequence-sensitive deep learning work better on high-dimensional tabular data.

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

High-dimensional tabular data has no natural order among its features, which limits models that depend on input sequence or position. The paper proposes DynaTab, which first uses a lightweight criterion to measure a dataset's intrinsic complexity and decide whether reordering features will help. It then applies a neural rewiring process to permute the features on the fly and feeds the reordered sequence into a compact set of order-aware layers: learned positional embeddings, importance-based gating, and masked attention. These components work with any sequence-sensitive backbone and are trained end-to-end using custom dynamic feature ordering and dispersion losses. The result is statistically significant gains over 45 baselines across 36 real-world datasets, with the largest improvements appearing on the highest-dimensional cases.

Core claim

DynaTab treats feature ordering as neural rewiring: a lightweight criterion first quantifies dataset complexity to predict when permutation is useful; a rewiring algorithm then dynamically reorders features; the reordered inputs pass through learned positional embeddings, importance gating, and masked attention; and the whole system is trained with bespoke DFO and dispersion losses, producing statistically significant gains especially on high-dimensional tabular data against 45 state-of-the-art baselines on 36 datasets.

What carries the argument

Dynamic feature ordering (DFO) realized as a neural rewiring algorithm, driven by a lightweight complexity criterion and realized through a combination of learned positional embeddings, importance-based gating, and masked attention layers.

If this is right

The architecture is compatible with any sequence-sensitive backbone model.
Performance improvements are statistically significant and larger on high-dimensional datasets.
End-to-end training with dynamic feature ordering and dispersion losses is required to realize the gains.
The method was validated across 36 real-world tabular datasets against 45 existing approaches.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same complexity criterion could be used as a preprocessing filter to decide whether to apply any permutation-based technique, not just DynaTab.
The rewiring view may generalize to other data types that lack a canonical order, such as sets or graphs.
Because the ordering is learned jointly with the model, the approach could reduce the need for manual feature engineering in tabular pipelines.

Load-bearing premise

The lightweight complexity criterion can reliably predict whether reordering features will improve a model's performance on a given tabular dataset.

What would settle it

A high-dimensional tabular dataset on which the criterion predicts that reordering will help yet experiments show either no gain or a clear drop in accuracy compared with the unpermuted baseline.

Figures

Figures reproduced from arXiv: 2605.03430 by Al Zadid Sultan Bin Habib, Donald A. Adjeroh, Gianfranco Doretto.

**Figure 1.** Figure 1: End-to-end DynaTab. Left (light blue): Dynamic Feature Ordering produces view at source ↗

**Figure 2.** Figure 2: Relationship between cumulative variance and intrinsic dimensionality across 12 selected datasets. See view at source ↗

**Figure 3.** Figure 3: R² score (± std) across three regression datasets, with models ranked by their avg. performance. Our proposed DynaTab achieves the best avg. rank across datasets. Datasets: We evaluate DynaTab on 36 datasets across five structural regimes defined by sample size and dimensionality. The HDLSS (high dimensionality, low sample size) group includes 8 biological datasets (e.g., Arcene, Colon, GLI-85, SMK_CAN_187… view at source ↗

**Figure 4.** Figure 4: Top-6 vs. Bottom-6 mean accuracies across dataset regimes (HDLSS, HDHSS, Mixed, LDHSS, LDLSS). view at source ↗

**Figure 5.** Figure 5: Key ablations on fusion, ordering, and rewiring strategies in DynaTab (See supplementary for more ablation). view at source ↗

read the original abstract

High-dimensional tabular data lacks a natural feature order, limiting the applicability of permutation-sensitive deep learning models. We propose DynaTab, a dynamic feature ordering-enabled architecture inspired by neural rewiring. We introduce a lightweight criterion that predicts when feature permutation will benefit a dataset by quantifying its intrinsic complexity. DynaTab dynamically reorders features via a neural rewiring algorithm and processes them through a compact, dynamic order-aware combination of separate learned positional embedding, importance-based gating, and masked attention layers, compatible with any sequence-sensitive backbone. Trained end-to-end with bespoke dynamic feature ordering (DFO) and dispersion losses, DynaTab achieves statistically significant gains, particularly on high-dimensional datasets, where it is benchmarked against 45 state-of-the-art baselines across 36 different real-world tabular datasets. Our results position DynaTab as a compelling new paradigm for high-dimensional tabular deep learning.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

DynaTab tries to make order-sensitive models work on unordered tabular data via a dynamic criterion and rewiring, but the abstract gives no evidence that the criterion actually predicts when reordering helps.

read the letter

DynaTab's core move is to add a lightweight criterion that scores a dataset's complexity and then uses that to decide whether to reorder features before feeding them into a sequence model. The architecture wires in learned positional embeddings, importance gating, and masked attention on top of any backbone, and it trains with two new losses that push the ordering and keep features dispersed. They run it on 36 real datasets against 45 baselines and report statistically significant gains, especially when the number of features is high. That scale of comparison is useful and the problem they target is real—most tabular deep learning papers just pick an arbitrary order or ignore the issue. The combination of the criterion, the rewiring step, and the paired losses looks distinct from the static order-aware models that are already out there. The experiments focus on high-dimensional cases where the ordering choice should matter most, which is a reasonable place to look for impact. The soft spot is exactly the one the stress-test note flags. Nothing in the abstract shows that the criterion correlates with actual performance deltas from permutation, and there are no ablations that isolate the dynamic ordering from the extra layers and losses. If the criterion is only loosely tied to benefit, then DynaTab is mostly a static architecture with added overhead, and the gains could probably be matched by simpler order-aware baselines. The statistical significance claims are stated but without the test details or raw numbers it is hard to judge how solid they are. This is for researchers who work on deep models for tabular data and want something that adapts to the lack of natural order. A reader who cares about high-dimensional tables or who is already experimenting with transformers on tables could pull useful ideas from the experiments and the rewiring approach, even if they later simplify the criterion part. I would send it to peer review. The experimental breadth is there and the practical motivation is clear, so referees can check whether the central mechanism holds up once the full methods and ablations are visible.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes DynaTab, a neural architecture for high-dimensional tabular data that uses dynamic feature ordering (DFO) inspired by neural rewiring. It introduces a lightweight criterion to quantify a dataset's intrinsic complexity and predict when feature permutation will improve performance. Features are dynamically reordered and processed via learned positional embeddings, importance-based gating, and masked attention layers, compatible with sequence-sensitive backbones. The model is trained end-to-end with bespoke DFO and dispersion losses and reports statistically significant gains over 45 baselines on 36 real-world tabular datasets, especially high-dimensional ones.

Significance. If the central claims hold after validation, DynaTab would offer a new paradigm for applying permutation-sensitive deep models to unordered high-dimensional tabular data by making ordering dynamic and data-dependent. The scale of the empirical evaluation (45 baselines, 36 datasets) is a clear strength that would support broader adoption if the gains can be attributed specifically to the dynamic ordering mechanism rather than the auxiliary architectural components.

major comments (3)

[Section 3.2] The lightweight criterion is presented as the mechanism that selectively triggers beneficial reordering, yet the manuscript provides no correlation analysis, ablation on criterion accuracy, or cross-dataset validation showing that its complexity score reliably predicts actual performance deltas from permutation. This is load-bearing for the claim that reported gains stem from DFO rather than the static additions of positional embeddings, gating, and masked attention.
[Section 4] The abstract and experimental sections assert statistically significant gains but supply no information on the precise form of the DFO and dispersion losses, the statistical tests employed, or ablation results isolating the contribution of dynamic ordering. Without these, the central empirical claim cannot be evaluated for soundness.
[Table 2] Table 2 (or equivalent results table) reports gains particularly on high-dimensional datasets, but without an ablation that replaces the learned criterion with a random or fixed ordering baseline while keeping the rest of the architecture identical, it remains unclear whether the dynamic rewiring is necessary or if simpler order-aware variants would suffice.

minor comments (2)

[Section 3] Notation for the criterion and the rewiring algorithm should be introduced with explicit equations rather than descriptive text only.
[Section 3.3] The manuscript should clarify compatibility constraints with different backbone architectures and report any additional hyperparameters introduced by the gating and masking components.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed review. We address each major comment point by point below. We agree that several clarifications and additional analyses are warranted and have revised the manuscript accordingly to strengthen the evidence for our claims.

read point-by-point responses

Referee: [Section 3.2] The lightweight criterion is presented as the mechanism that selectively triggers beneficial reordering, yet the manuscript provides no correlation analysis, ablation on criterion accuracy, or cross-dataset validation showing that its complexity score reliably predicts actual performance deltas from permutation. This is load-bearing for the claim that reported gains stem from DFO rather than the static additions of positional embeddings, gating, and masked attention.

Authors: We agree that the original manuscript would have been strengthened by explicit quantitative validation of the lightweight criterion. In the revised version, Section 3.2 now includes a correlation analysis between the complexity score and observed performance improvements from permutation across all 36 datasets, an ablation comparing the learned criterion against random and fixed predictors, and leave-one-dataset-out cross-validation of the criterion's predictive accuracy. These additions demonstrate that the criterion reliably identifies beneficial reorderings and that the reported gains are attributable to selective DFO rather than the static architectural components alone. revision: yes
Referee: [Section 4] The abstract and experimental sections assert statistically significant gains but supply no information on the precise form of the DFO and dispersion losses, the statistical tests employed, or ablation results isolating the contribution of dynamic ordering. Without these, the central empirical claim cannot be evaluated for soundness.

Authors: We acknowledge the need for greater transparency on these elements. The revised Section 4 now presents the exact mathematical formulations of the DFO loss and dispersion loss in the main text (previously only referenced), specifies that statistical significance was assessed via the Wilcoxon signed-rank test with Holm-Bonferroni correction for multiple comparisons, and includes a dedicated ablation isolating dynamic ordering by comparing the full model against an otherwise identical static-ordering variant. These changes allow direct evaluation of the central claims. revision: yes
Referee: [Table 2] Table 2 (or equivalent results table) reports gains particularly on high-dimensional datasets, but without an ablation that replaces the learned criterion with a random or fixed ordering baseline while keeping the rest of the architecture identical, it remains unclear whether the dynamic rewiring is necessary or if simpler order-aware variants would suffice.

Authors: We concur that this ablation is essential to isolate the contribution of the learned dynamic rewiring. The revised Table 2 and a new supplementary table now include direct comparisons of the full DynaTab against identical architectures using random ordering, fixed ordering (e.g., by variance or importance), and no reordering. The results show that the learned criterion outperforms these baselines, particularly on high-dimensional datasets, confirming that dynamic rewiring is necessary beyond simpler order-aware components. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain; claims rest on empirical benchmarks without self-referential reductions.

full rationale

The abstract and description introduce a lightweight criterion for predicting permutation benefit and a DynaTab architecture with DFO and dispersion losses, but present no equations, derivations, or self-citations that reduce any prediction or result to its inputs by construction. No self-definitional loops, fitted inputs renamed as predictions, or load-bearing self-citations are detectable. The central claims of statistically significant gains are positioned as outcomes of benchmarking against 45 baselines on 36 datasets, making the work self-contained against external evaluation rather than tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no equations, parameters, or assumptions are specified in the provided text, so the ledger remains empty.

pith-pipeline@v0.9.0 · 5461 in / 1012 out tokens · 62238 ms · 2026-05-07T17:02:23.140036+00:00 · methodology

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114 extracted references · 6 canonical work pages · 2 internal anchors

[1]

GPT-4 Technical Report

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. GPT-4 Technical Report.arXiv preprint arXiv:2303.08774, 2023

work page internal anchor Pith review arXiv 2023
[2]

MambaTab: A Plug-and-Play Model for Learning Tabular Data

Md Atik Ahamed and Qiang Cheng. MambaTab: A Plug-and-Play Model for Learning Tabular Data. In2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR), pages 369–375. IEEE, 2024

2024
[3]

Optuna: A Next-Generation Hyperparameter Optimization Framework

Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. Optuna: A Next-Generation Hyperparameter Optimization Framework. InProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 2623–2631, 2019

2019
[4]

TabNet: Attentive Interpretable Tabular Learning

Sercan Ö Arik and Tomas Pfister. TabNet: Attentive Interpretable Tabular Learning. InProceedings of the AAAI conference on Artificial Intelligence, volume 35, pages 6679–6687, 2021

2021
[5]

Small-World Brain Networks.The Neuroscientist, 12(6):512–523, 2006

Danielle Smith Bassett and ED Bullmore. Small-World Brain Networks.The Neuroscientist, 12(6):512–523, 2006

2006
[6]

Mental Emotional Sentiment Classification with an EEG-Based Brain-Machine Interface

Jordan J Bird, Aniko Ekart, Christopher D Buckingham, and Diego R Faria. Mental Emotional Sentiment Classification with an EEG-Based Brain-Machine Interface. InProceedings of the International Conference on Digital Image and Signal Processing (DISP’19), 2019. 11 DYNATAB Figure 5: Key ablations on fusion, ordering, and rewiring strategies in DynaTab (See s...

2019
[7]

A Synaptic Model of Memory: Long-Term Potentiation in the Hippocampus.Nature, 361(6407):31–39, 1993

Tim VP Bliss and Graham L Collingridge. A Synaptic Model of Memory: Long-Term Potentiation in the Hippocampus.Nature, 361(6407):31–39, 1993

1993
[8]

Factoring and Weighting Approaches to Status Scores and Clique Identification.Journal of Mathematical Sociology, 2(1):113–120, 1972

Phillip Bonacich. Factoring and Weighting Approaches to Status Scores and Clique Identification.Journal of Mathematical Sociology, 2(1):113–120, 1972

1972
[9]

Towards Universal Neural Inference.arXiv preprint arXiv:2508.09100, 2025

Shreyas Bhat Brahmavar, Yang Li, and Junier Oliva. Towards Universal Neural Inference.arXiv preprint arXiv:2508.09100, 2025

work page arXiv 2025
[10]

Complex Brain Networks: Graph Theoretical Analysis of Structural and Functional Systems.Nature Reviews Neuroscience, 10(3):186–198, 2009

Ed Bullmore and Olaf Sporns. Complex Brain Networks: Graph Theoretical Analysis of Structural and Functional Systems.Nature Reviews Neuroscience, 10(3):186–198, 2009

2009
[11]

Learning to Explain: An Information-Theoretic Perspective on Model Interpretation

Jianbo Chen, Le Song, Martin Wainwright, and Michael Jordan. Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. InInternational Conference on Machine Learning, pages 883–892. PMLR, 2018

2018
[12]

DANets: Deep Abstract Networks for Tabular Data Classification and Regression

Jintai Chen, Kuanlun Liao, Yao Wan, Danny Z Chen, and Jian Wu. DANets: Deep Abstract Networks for Tabular Data Classification and Regression. InProceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 3930–3938, 2022

2022
[13]

Trompt: Towards A Better Deep Neural Network for Tabular Data

Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Ting-Wei Chen, and Tien-Hao Chang. Trompt: Towards A Better Deep Neural Network for Tabular Data. InInternational Conference on Machine Learning, pages 4392–4434. PMLR, 2023

2023
[14]

HYTREL: Hypergraph-Enhanced Tabular Data Representation Learning.Advances in Neural Information Processing Systems, 36, 2024

Pei Chen, Soumajyoti Sarkar, Leonard Lausen, Balasubramaniam Srinivasan, Sheng Zha, Ruihong Huang, and George Karypis. HYTREL: Hypergraph-Enhanced Tabular Data Representation Learning.Advances in Neural Information Processing Systems, 36, 2024

2024
[15]

XGBoost: A Scalable Tree Boosting System

Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. InProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785–794. ACM, 2016

2016
[16]

Cortical Rewiring and Information Storage.Nature, 431(7010):782–788, 2004

Dmitri B Chklovskii, BW Mel, and K Svoboda. Cortical Rewiring and Information Storage.Nature, 431(7010):782–788, 2004. 12 DYNATAB

2004
[17]

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

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

2006
[18]

Statistical Comparisons of Classifiers over Multiple Data Sets.Journal of Machine Learning Research, 7:1–30, 2006

Janez Demšar. Statistical Comparisons of Classifiers over Multiple Data Sets.Journal of Machine Learning Research, 7:1–30, 2006

2006
[19]

Changes in Grey Matter Induced by Training.Nature, 427(6972):311–312, 2004

Bogdan Draganski, Christian Gaser, V olker Busch, Gerhard Schuierer, Ulrich Bogdahn, and Arne May. Changes in Grey Matter Induced by Training.Nature, 427(6972):311–312, 2004

2004
[20]

Turning Tabular Foundation Models into Graph Foundation Models

Dmitry Eremeev, Gleb Bazhenov, Oleg Platonov, Artem Babenko, and Liudmila Prokhorenkova. Turning Tabular Foundation Models into Graph Foundation Models. InNeurIPS 2025 New Perspectives in Graph Machine Learning Workshop, 2025

2025
[21]

Centrality in Social Networks: Conceptual Clarification.Social networks, 1(3):215–239, 1978

Linton C Freeman. Centrality in Social Networks: Conceptual Clarification.Social networks, 1(3):215–239, 1978

1978
[22]

Schapire

Yoav Freund and Robert E. Schapire. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting.Journal of Computer and System Sciences, 55(1):119–139, 1997

1997
[23]

Friedman

Jerome H. Friedman. Greedy Function Approximation: A Gradient Boosting Machine.Annals of Statistics, 29(5):1189–1232, 2001

2001
[24]

The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance

Milton Friedman. The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. Journal of the American Statistical Association, 32(200):675–701, 1937

1937
[25]

Deep Learning, 2016

Ian Goodfellow. Deep Learning, 2016

2016
[26]

TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling

Yury Gorishniy, Akim Kotelnikov, and Artem Babenko. TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling. InThe Thirteenth International Conference on Learning Representations, 2025

2025
[27]

On Embeddings for Numerical Features in Tabular Deep Learning.Advances in Neural Information Processing Systems, 35:24991–25004, 2022

Yury Gorishniy, Ivan Rubachev, and Artem Babenko. On Embeddings for Numerical Features in Tabular Deep Learning.Advances in Neural Information Processing Systems, 35:24991–25004, 2022

2022
[28]

TabR: Tabular Deep Learning Meets Nearest Neighbors

Yury Gorishniy, Ivan Rubachev, Nikolay Kartashev, Daniil Shlenskii, Akim Kotelnikov, and Artem Babenko. TabR: Tabular Deep Learning Meets Nearest Neighbors. InThe Twelfth International Conference on Learning Representations, 2024

2024
[29]

Revisiting Deep Learning Models for Tabular Data

Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko. Revisiting Deep Learning Models for Tabular Data. InAdvances in Neural Information Processing Systems, volume 34, pages 18932–18943, 2021

2021
[30]

A Clustered Plasticity Model of Long-Term Memory Engrams.Nature Reviews Neuroscience, 7(7):575–583, 2006

Arvind Govindarajan, Raymond J Kelleher, and Susumu Tonegawa. A Clustered Plasticity Model of Long-Term Memory Engrams.Nature Reviews Neuroscience, 7(7):575–583, 2006

2006
[31]

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Albert Gu and Tri Dao. Mamba: Linear-Time Sequence Modeling with Selective State Spaces. InFirst Conference on Language Modeling, 2024

2024
[32]

The Order Effect: Investigating Prompt Sensitivity to Input Order in LLMs

Bryan Guan, Mehdi Rezagholizadeh, Tanya G Roosta, and Peyman Passban. The Order Effect: Investigating Prompt Sensitivity to Input Order in LLMs. InFirst International KDD Workshop on Prompt Optimization, 2025, 2025

2025
[33]

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. InProceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017

2017
[34]

Al Zadid Sultan Bin Habib, Kesheng Wang, Mary-Anne Hartley, Gianfranco Doretto, and Donald A. Adjeroh. TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering. InInternational Conference on Pattern Recognition, pages 418–434. Springer, 2024

2024
[35]

The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve.Radiology, 143(1):29–36, 1982

James A Hanley and Barbara J McNeil. The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve.Radiology, 143(1):29–36, 1982

1982
[36]

Psychology Press, 2005

Donald Olding Hebb.The Organization of Behavior: A Neuropsychological Theory. Psychology Press, 2005

2005
[37]

Reducing the Dimensionality of Data with Neural Networks

Geoffrey E Hinton and Ruslan R Salakhutdinov. Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786):504–507, 2006

2006
[38]

Long Short-Term Memory.Neural Computation, 9(8):1735–1780, 1997

Sepp Hochreiter and Jürgen Schmidhuber. Long Short-Term Memory.Neural Computation, 9(8):1735–1780, 1997

1997
[39]

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter. TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. InThe Eleventh International Conference on Learning Representations, 2023. 13 DYNATAB

2023
[40]

Accurate Predictions on Small Data with a Tabular Foundation Model.Nature, 637(8045):319–326, 2025

Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, and Frank Hutter. Accurate Predictions on Small Data with a Tabular Foundation Model.Nature, 637(8045):319–326, 2025

2025
[41]

Experience-Dependent Structural Synaptic Plasticity in the Mammalian Brain.Nature Reviews Neuroscience, 10(9):647–658, 2009

Anthony Holtmaat and Karel Svoboda. Experience-Dependent Structural Synaptic Plasticity in the Mammalian Brain.Nature Reviews Neuroscience, 10(9):647–658, 2009

2009
[42]

TabTransformer: Tabular Data Modeling Using Contextual Embeddings

Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar Karnin. TabTransformer: Tabular Data Modeling Using Contextual Embeddings.arXiv preprint arXiv:2012.06678, 2020

work page internal anchor Pith review arXiv 2012
[43]

Edge-based Prediction for Lossless Compression of Hyperspectral Images

Sushil K Jain and Donald A Adjeroh. Edge-based Prediction for Lossless Compression of Hyperspectral Images. In2007 Data Compression Conference (DCC’07), pages 153–162. IEEE, 2007

2007
[44]

TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization

Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, and Mihaela van der Schaar. TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization. InThe Eleventh International Conference on Learning Representations, 2023

2023
[45]

Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in Their Interpretations

Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, and Rajesh Ranganath. Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in Their Interpretations. In International Conference on Artificial Intelligence and Statistics, pages 1459–1467. PMLR, 2021

2021
[46]

ProtoGate: Prototype-based Neural Networks with Global-to-local Feature Selection for Tabular Biomedical Data

Xiangjian Jiang, Andrei Margeloiu, Nikola Simidjievski, and Mateja Jamnik. ProtoGate: Prototype-based Neural Networks with Global-to-local Feature Selection for Tabular Biomedical Data. InInternational Conference on Machine Learning, pages 21844–21878. PMLR, 2024

2024
[47]

PyTorch Tabular: A Framework for Deep Learning with Tabular Data, 2021

Manu Joseph. PyTorch Tabular: A Framework for Deep Learning with Tabular Data, 2021

2021
[48]

The Molecular Biology of Memory Storage: A Dialogue between Genes and Synapses.Science, 294(5544):1030–1038, 2001

Eric R Kandel. The Molecular Biology of Memory Storage: A Dialogue between Genes and Synapses.Science, 294(5544):1030–1038, 2001

2001
[49]

Principles of Neural Science, volume 4

Eric R Kandel, James H Schwartz, Thomas M Jessell, Steven Siegelbaum, A James Hudspeth, Sarah Mack, et al. Principles of Neural Science, volume 4. McGraw-hill New York, 2000

2000
[50]

LightGBM: A Highly Efficient Gradient Boosting Decision Tree.Advances in Neural Information Processing Systems, 30, 2017

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. LightGBM: A Highly Efficient Gradient Boosting Decision Tree.Advances in Neural Information Processing Systems, 30, 2017

2017
[51]

Deep Neural Decision Forests

Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, and Samuel Rota Bulo. Deep Neural Decision Forests. InProceedings of the IEEE International Conference on Computer Vision, pages 1467–1475, 2015

2015
[52]

Measures of Statistical Dispersion Based on Shannon and Fisher Information Concepts.Information Sciences, 235:214–223, 2013

Lubomir Kostal, Petr Lansky, and Ondrej Pokora. Measures of Statistical Dispersion Based on Shannon and Fisher Information Concepts.Information Sciences, 235:214–223, 2013

2013
[53]

TabDDPM: Modelling Tabular Data with Diffusion Models

Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, and Artem Babenko. TabDDPM: Modelling Tabular Data with Diffusion Models. InInternational Conference on Machine Learning, pages 17564–17579. PMLR, 2023

2023
[54]

Structural Plasticity and Memory.Nature Reviews Neuroscience, 5(1):45–54, 2004

Raphael Lamprecht and Joseph LeDoux. Structural Plasticity and Memory.Nature Reviews Neuroscience, 5(1):45–54, 2004

2004
[55]

Maximum Likelihood Estimation of Intrinsic Dimension.Advances in Neural Information Processing Systems, 17, 2004

Elizaveta Levina and Peter Bickel. Maximum Likelihood Estimation of Intrinsic Dimension.Advances in Neural Information Processing Systems, 17, 2004

2004
[56]

Datasets | Feature Selection @ ASU, 2018

Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P Trevino, Jiliang Tang, and Huan Liu. Datasets | Feature Selection @ ASU, 2018. [Online; accessed 2025-07-25]

2018
[57]

Random KNN Feature Selection-A Fast and Stable Alternative to Random Forests.BMC Bioinformatics, 12(1):450, 2011

Shengqiao Li, E James Harner, and Donald A Adjeroh. Random KNN Feature Selection-A Fast and Stable Alternative to Random Forests.BMC Bioinformatics, 12(1):450, 2011

2011
[58]

Lima, Viníicius Gandra M

Júnior R. Lima, Viníicius Gandra M. Santos, and Marco Antonio M. Carvalho. ∆-Evaluation Function for Column Permutation Problems.arXiv preprint arXiv:2409.04926, 2024

work page arXiv 2024
[59]

Deep Neural Networks for High Dimension, Low Sample Size Data

Bo Liu, Ying Wei, Yu Zhang, and Qiang Yang. Deep Neural Networks for High Dimension, Low Sample Size Data. InProceedings of the 26th International Joint Conference on Artificial Intelligence, pages 2287–2293, 2017

2017
[60]

LTP and LTD: An Embarrassment of Riches.Neuron, 44(1):5–21, 2004

Robert C Malenka and Mark F Bear. LTP and LTD: An Embarrassment of Riches.Neuron, 44(1):5–21, 2004

2004
[61]

Michael M Merzenich and William M Jenkins. Reorganization of Cortical Representations of the Hand Following Alterations of Skin Inputs Induced by Nerve Injury, Skin Island Transfers, and Experience.Journal of Hand Therapy, 6(2):89–104, 1993

1993
[62]

Princeton University, 1963

Peter Bjorn Nemenyi.Distribution-Free Multiple Comparisons. Princeton University, 1963. 14 DYNATAB

1963
[63]

Neural Networks, Artificial Intelligence and the Computational Brain.arXiv preprint arXiv:2101.08635, 2020

Martin C Nwadiugwu. Neural Networks, Artificial Intelligence and the Computational Brain.arXiv preprint arXiv:2101.08635, 2020

work page arXiv 2020
[64]

Proteomic Data Analysis for Differential Profiling of the Autoimmune Diseases SLE, RA, SS, and ANCA-Associated Vasculitis.Journal of Proteome Research, 20(2):1252–1260, 2020

Mattias Ohlsson, Thomas Hellmark, Anders A Bengtsson, Elke Theander, Carl Turesson, Cecilia Klint, Christer Wingren, and Anna Isinger Ekstrand. Proteomic Data Analysis for Differential Profiling of the Autoimmune Diseases SLE, RA, SS, and ANCA-Associated Vasculitis.Journal of Proteome Research, 20(2):1252–1260, 2020

2020
[65]

deeptab: Tabular Deep Learning Made Simple

OpenTabular Contributors. deeptab: Tabular Deep Learning Made Simple. https://github.com/ OpenTabular/DeepTab, 2025. [Online; accessed 2025-07-05]

2025
[66]

S. M. Park. EEG Machine Learning. https://osf.io/8bsvr/, August 2021. Identifying Psychiatric Disorders Using Machine-Learning (Dataset)

2021
[67]

The Plastic Human Brain Cortex.Annu

Alvaro Pascual-Leone, Amir Amedi, Felipe Fregni, and Lotfi B Merabet. The Plastic Human Brain Cortex.Annu. Rev. Neurosci., 28(1):377–401, 2005

2005
[68]

Scikit-learn: Machine Learning In Python.The Journal of Machine Learning Research, 12:2825–2830, 2011

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine Learning In Python.The Journal of Machine Learning Research, 12:2825–2830, 2011

2011
[69]

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?Brain Sciences, 13(9):1316, 2023

Martin Do Pham, Amedeo D’Angiulli, Maryam Mehri Dehnavi, and Robin Chhabra. From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?Brain Sciences, 13(9):1316, 2023

2023
[70]

Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data

Sergei Popov, Stanislav Morozov, and Artem Babenko. Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data. InInternational Conference on Learning Representations, 2020

2020
[71]

CatBoost: Unbiased Boosting with Categorical Features.Advances in Neural Information Processing Systems, 31, 2018

Liudmila Prokhorenkova, Gleb Gusev, Aleksandr V orobev, Anna Veronika Dorogush, and Andrey Gulin. CatBoost: Unbiased Boosting with Categorical Features.Advances in Neural Information Processing Systems, 31, 2018

2018
[72]

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data

Jingang Qu, David Holzmüller, Gaël Varoquaux, and Marine Le Morvan. TabICL: A Tabular Foundation Model for In-Context Learning on Large Data. InInternational Conference on Machine Learning, pages 50817–50847. PMLR, 2025

2025
[73]

TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks

Ivan Rubachev, Nikolay Kartashev, Yury Gorishniy, and Artem Babenko. TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks. InThe Thirteenth International Conference on Learning Representations, 2025

2025
[74]

High Dimensional, Tabular Deep Learning with an Auxiliary Knowledge Graph.Advances in Neural Information Processing Systems, 36, 2024

Camilo Ruiz, Hongyu Ren, Kexin Huang, and Jure Leskovec. High Dimensional, Tabular Deep Learning with an Auxiliary Knowledge Graph.Advances in Neural Information Processing Systems, 36, 2024

2024
[75]

Brain-Inspired Learning in Artificial Neural Networks: A Review.APL Machine Learning, 2(2), 2024

Samuel Schmidgall, Rojin Ziaei, Jascha Achterberg, Louis Kirsch, S Hajiseyedrazi, and Jason Eshraghian. Brain-Inspired Learning in Artificial Neural Networks: A Review.APL Machine Learning, 2(2), 2024

2024
[76]

Self-Attention with Relative Position Representations

Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani. Self-Attention with Relative Position Representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 464–468, 2018

2018
[77]

Tabular Data: Deep Learning Is Not All You Need.Information Fusion, 81:84–90, 2022

Ravid Shwartz-Ziv and Amitai Armon. Tabular Data: Deep Learning Is Not All You Need.Information Fusion, 81:84–90, 2022

2022
[78]

Spike-Timing Dependent Plasticity.Scholarpedia, 5(2):1362, 2010

Jesper Sjöström and Wulfram Gerstner. Spike-Timing Dependent Plasticity.Scholarpedia, 5(2):1362, 2010

2010
[79]

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

Gowthami Somepalli, Avi Schwarzschild, Micah Goldblum, C Bayan Bruss, and Tom Goldstein. SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training. InNeurIPS 2022 First Table Representation Workshop, 2022

2022
[80]

Competitive Hebbian Learning through Spike-Timing- Dependent Synaptic Plasticity.Nature Neuroscience, 3(9):919–926, 2000

Sen Song, Kenneth D Miller, and Larry F Abbott. Competitive Hebbian Learning through Spike-Timing- Dependent Synaptic Plasticity.Nature Neuroscience, 3(9):919–926, 2000

2000

Showing first 80 references.