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Table of Contents
Abstract
Introduction
Deep Neural Networks (DNNs) Success
Tabular Data Challenges
Deep Learning for Tabular Data
Motivation for Deep Learning in Tabular Data
Proposed TabNet Architecture
Related Work
Feature Selection
Tree-based Learning
Integration of DNNs into DTs
Self-supervised Learning
Experiments
Instance-wise Feature Selection
Tabular Self-supervised Learning
Conclusion
Summary
TabNet is a novel deep tabular data learning architecture that uses sequential attention for feature selection, enabling interpretability and efficient learning. The paper discusses the challenges with traditional deep learning architectures for tabular data and proposes TabNet as a solution. It outperforms other models on various datasets and provides insights into feature attributions. The document also introduces self-supervised learning for tabular data, showing significant performance improvements. Experimental results and discussions on feature selection, self-supervised learning, and model performance are presented.