Summary of Deep Learning with Tabular Data: a Self-supervised Approach, by Tirth Kiranbhai Vyas
Deep Learning with Tabular Data: A Self-supervised Approach
by Tirth Kiranbhai Vyas
First submitted to arxiv on: 26 Jan 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 In this paper, researchers propose a novel approach for training tabular data using the TabTransformer model with self-supervised learning. Unlike traditional GBDT models, the TabTransformer is optimized specifically for tabular data and leverages self-attention mechanisms to capture intricate relationships among features. The self-supervised learning approach eliminates the need for labelled data, allowing the model to learn from unlabelled data by creating surrogate supervised tasks. The goal is to find the most effective representation of categorical and numerical features using the TabTransformer. A comparative analysis is also conducted to examine the performance of the TabTransformer against baseline models such as MLP and supervised TabTransformer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to train tabular data using a special kind of AI model called TabTransformer. Tabular data is like a table with numbers and words, and traditional machine learning models don’t always work well with this type of data. The TabTransformer is different because it’s designed specifically for tabular data and can capture complex relationships between the columns in the table. This paper shows how to use the TabTransformer without needing labelled training data, which makes it easier to use in some situations. |
Keywords
* Artificial intelligence * Machine learning * Self attention * Self supervised * Supervised
