Methods › General › Deep Tabular Learning › FT-Transformer

FT-Transformer

10 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

FT-Transformer (Feature Tokenizer + Transformer) is a simple adaptation of the Transformer architecture for the tabular domain. The model (Feature Tokenizer component) transforms all features (categorical and numerical) to tokens and runs a stack of Transformer layers over the tokens, so every Transformer layer operates on the feature level of one object. (This model is similar to AutoInt). In the Transformer component, the [CLS] token is appended to T. Then L Transformer layers are applied. PreNorm is used for easier optimization and good performance. The final representation of the [CLS] token is used for prediction.

Source: Revisiting Deep Learning Models for Tabular Data

Papers archive 2025-07-28

10 shown of 10, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

LLM Embeddings for Deep Learning on Tabular Data
17 Feb 2025 · 0 repositories · arXiv:2502.11596
Representation Learning on Out of Distribution in Tabular Data
14 Feb 2025 · 0 repositories · arXiv:2502.10095
Application of Tabular Transformer Architectures for Operating System Fingerprinting
13 Feb 2025 · 1 repository · arXiv:2502.09084
A Survey on Deep Tabular Learning
15 Oct 2024 · 0 repositories · arXiv:2410.12034
Efficient Feature Interactions with Transformers: Improving User Spending Propensity Predictions in Gaming
25 Sep 2024 · 0 repositories · arXiv:2409.17077
Mamba for Scalable and Efficient Personalized Recommendations
11 Sep 2024 · 0 repositories · arXiv:2409.17165
Cross-Table Pretraining towards a Universal Function Space for Heterogeneous Tabular Data
1 Jun 2024 · 0 repositories · arXiv:2406.00281
Self-supervised learning of multi-omics embeddings in the low-label, high-data regime
16 Nov 2023 · 0 repositories · arXiv:2311.09962
XTab: Cross-table Pretraining for Tabular Transformers
10 May 2023 · 1 repository · arXiv:2305.06090

Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
Revisiting Deep Learning Models for Tabular Data
22 Jun 2021 · 11 repositories · arXiv:2106.11959

Syntology ran 4 of 22 samples · 18 unverified

Tasks archive 2025-07-28

19 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

Task	Papers
Deep Learning	2
Self-Supervised Learning	2
AutoML	1
CPU	1
Computational Efficiency	1
Contrastive Learning	1
Denoising	1
Federated Learning	1
Mamba	1
Management	1
Music Recommendation	1
Recommendation Systems	1
Representation Learning	1
State Space Models	1
Survey	1
Transfer Learning	1
feature selection	1
tabular-classification	1
tabular-regression	1

Usage over time archive 2025-07-28

Papers per year the archive tags with this method, by the paper's archive date (10 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Deep Tabular Learning

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