Deep Tabular Learning
AutoInt
Introduced by Song et al. in AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural NetworksAutoInt is a deep tabular learning method that models high-order feature interactions of input features. AutoInt can be applied to both numerical and categorical input features. Specifically, both the numerical and categorical features are mapped into the same low-dimensional space. Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space. With different layers of the multi-head self-attentive neural networks, different orders of feature combinations of input features can be modeled.
Source: AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Click-Through Rate Prediction | 2 | 40.00% |
| Recommendation Systems | 2 | 40.00% |
| Feature Engineering | 1 | 20.00% |
Usage Over Time
Components
| Component | Type |
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|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
