Solving In-Table Prediction Problems by Deep Neural Networks with Performance Evaluation Using Synthetic Data
cs.LG
Submitted: 2026-09-01
Updated: 2026-09-01
Terminology
Sources
- TabNet: Attentive Interpretable Tabular Learning
- SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption
- Improving Missing Data Imputation with Deep Generative Models
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- CatBoost: gradient boosting with categorical features support
- Revisiting Deep Learning Models for Tabular Data
- Masked Autoencoders Are Scalable Vision Learners
- TabTransformer: Tabular Data Modeling Using Contextual Embeddings
- Well-tuned Simple Nets Excel on Tabular Datasets
- MisGAN: Learning from Incomplete Data with Generative Adversarial Networks
- Handling Incomplete Heterogeneous Data using VAEs
- Revisiting Pretraining Objectives for Tabular Deep Learning
- SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
- A Survey on Deep Tabular Learning
- SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning
- TransTab: Learning Transferable Tabular Transformers Across Tables
- SwitchTab: Switched Autoencoders Are Effective Tabular Learners
- TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
- Missing Value Imputation Based on Deep Generative Models
- XTab: Cross-table Pretraining for Tabular Transformers
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