Core Viewpoint - The article emphasizes the growing importance of tabular data in AI applications across various sectors, including finance, healthcare, education, recommendation systems, and scientific research [1]. Group 1: Background and Importance of Tabular Data - Tabular data is fundamentally a structured representation of information, offering inherent advantages in organizing and expressing complex data relationships [3]. - The rise of deep learning has led to significant advancements in fields like computer vision and natural language processing, making the application of deep neural networks (DNN) to tabular data a research hotspot [6]. Group 2: Deep Learning Approaches to Tabular Data - The research categorizes deep learning methods for tabular data into three types: specialized methods, transferable methods, and general methods, reflecting the evolution of deep learning technology and the enhancement of model generalization capabilities [7][19]. - Specialized methods are the earliest and most widely used, focusing on obtaining high-quality representations from feature and sample levels [9]. - Transferable methods leverage pre-trained models to improve learning efficiency and reduce reliance on computational resources and data scale [12]. - General methods extend the generalization ability of pre-trained tabular models to various heterogeneous downstream tasks without additional fine-tuning [19]. Group 3: Challenges in Tabular Data Learning - Tabular data presents unique challenges, including feature heterogeneity, lack of spatial or sequential structure, low-quality and missing data, and the importance of feature engineering [22][23][25][26]. - The presence of class imbalance in many tabular datasets can lead to biased predictions, necessitating specific strategies for model training [27]. - Scalability to large datasets poses additional challenges, particularly as dimensionality increases, raising the risk of overfitting [28]. Group 4: Evaluation and Benchmarking - The article discusses the importance of robust evaluation methods for tabular models, highlighting the need for diverse benchmark datasets to assess model performance across different tasks and feature types [36]. - Performance evaluation metrics for classification tasks include accuracy, AUC, and F1 score, while regression tasks typically use MSE, MAE, and R² [32][33]. - Recent research emphasizes the need for comprehensive benchmarks that include semantically rich datasets to enhance the evaluation of tabular models [38][39].