Deep Tabular Learning

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General • 29 methods

Consists of tabular data learning approaches that use deep learning architectures for learning on tabular data. According to the taxonomy in V.Borisov et al. (2021), deep learning approaches for tabular data can be categorized into:

Regularization models
Transformer-based models: TabNet, TabTransformer, SAINT, ARM-Net,...
Hybrid models (fully differentiable and partly differentiable): Wide&Deep, TabNN, NON, Boost-GNN, NODE,...
Data encoding methods (single-dimensional encoding and multi-dimensional encoding): VIME, SCARF,...

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Method	Year	Papers
BiLSTM	2001	586
NON Network On Network for Tabular Data Classification in Real-world Applications	2020	264
NODE Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data	2019	29
TabNet TabNet: Attentive Interpretable Tabular Learning	2019	14
TAPAS TAPAS: Weakly Supervised Table Parsing via Pre-training	2020	9
Wide&Deep Wide & Deep Learning for Recommender Systems	2016	7
ODL Online Deep Learning: Learning Deep Neural Networks on the Fly	2017	7
TABPFN TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second	2022	7
MATE MATE: Multi-view Attention for Table Transformer Efficiency	2021	6
SCARF SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption	2021	5
Hierarchical MTL Deep multi-task learning with low level tasks supervised at lower layers	2016	5
SAINT SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training	2021	4
TabTransformer TabTransformer: Tabular Data Modeling Using Contextual Embeddings	2020	3
TaBERT TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data	2020	3
DCN-V2 DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems	2020	3
FT-Transformer Revisiting Deep Learning Models for Tabular Data	2021	3
GRANDE GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data	2023	3
AutoInt AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks	2018	2
DNN2LR DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data	2020	2
TURL TURL: Table Understanding through Representation Learning	2020	2
TABBIE TABBIE: Pretrained Representations of Tabular Data	2021	1
VIME VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain	2020	1
GrowNet Gradient Boosting Neural Networks: GrowNet	2020	1
TabNN TabNN: A Universal Neural Network Solution for Tabular Data	2019	1
Boost-GNN Boost then Convolve: Gradient Boosting Meets Graph Neural Networks	2021	1
ARM-Net ARM-Net: Adaptive Relation Modeling Network for Structured Data	2021	1
StruBERT StruBERT: Structure-aware BERT for Table Search and Matching	2022	1

Deep Tabular Learning Edit

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Deep Tabular Learning

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