Summary of A Survey on Deep Tabular Learning, by Shriyank Somvanshi et al.
A Survey on Deep Tabular Learning
by Shriyank Somvanshi, Subasish Das, Syed Aaqib Javed, Gian Antariksa, Ahmed Hossain
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper reviews the evolution of deep learning models for tabular data, which is commonly used in industries like healthcare, finance, and transportation. Tabular data lacks spatial structure and has heterogeneous features, making it challenging for deep learning models. The survey covers various architectures, including fully connected networks (FCNs), attention-based models like TabNet and SAINT, hybrid models that combine transformers with multi-layer perceptrons (MLPs), and graph-based models. These models aim to improve scalability, efficiency, and interpretability while handling categorical and numerical data. Additionally, the paper discusses diffusion-based models for generating synthetic data, pre-trained language models for tabular tasks, and future research directions on scalability, generalization, and interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to use deep learning for a type of data called “tabular data”. Tabular data is used in many industries like healthcare and finance. The problem with tabular data is that it’s hard to work with because it’s made up of different types of information and doesn’t have a special structure. The paper shows how different models, like TabNet and SAINT, can help solve this problem. These models are designed to make deep learning more efficient and easier to understand while still being able to handle the different types of data. |
Keywords
» Artificial intelligence » Attention » Deep learning » Diffusion » Generalization » Synthetic data
