Summary of Transformers with Stochastic Competition For Tabular Data Modelling, by Andreas Voskou et al.
Transformers with Stochastic Competition for Tabular Data Modelling
by Andreas Voskou, Charalambos Christoforou, Sotirios Chatzis
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The novel stochastic deep learning model presented in this paper specifically targets tabular data, which is often overlooked despite its significance across various industries. The Transformer-based architecture is adapted to suit tabular data by introducing strategic modifications and leveraging two forms of stochastic competition. This approach promotes generalization capacity through sparsity and stochasticity via “Local Winner Takes All” units. Additionally, a novel embedding layer selects among alternative linear embedding layers through stochastic competition. The model’s effectiveness is validated on publicly available datasets, demonstrating high performance and marking a significant advancement in applying deep learning to tabular data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new type of artificial intelligence (AI) model can work well with tables of numbers. This is important because tables are used in many areas, such as business and science. The old way of using computers for this was called gradient boosted decision trees (GBDT). But now, special AI models that use deep learning are getting better at working with tables. The new model has a special design that helps it learn from the unique features of table data. It does this by using two types of random competition to help the model make good choices. This new model is tested on many well-known datasets and shows great results, making it an important step forward for using AI with tabular data. |
Keywords
» Artificial intelligence » Deep learning » Embedding » Generalization » Transformer
