Summary of Protonam: Prototypical Neural Additive Models For Interpretable Deep Tabular Learning, by Guangzhi Xiong et al.
ProtoNAM: Prototypical Neural Additive Models for Interpretable Deep Tabular Learning
by Guangzhi Xiong, Sanchit Sinha, Aidong Zhang
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 A novel deep learning approach, called Prototypical Neural Additive Model (ProtoNAM), is proposed to improve the analysis of tabular data. This method combines generalized additive models with neural networks, enabling flexible modeling of complex relationships while maintaining explainability. A hierarchical shape function modeling technique is also introduced, allowing for the discovery of feature patterns and providing transparency into the learning process. Compared to existing approaches, ProtoNAM outperforms neural network-based generalized additive models and provides additional insights into learned feature patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ProtoNAM is a new way to analyze data that helps us understand how different features affect our results. It combines two powerful tools: neural networks and generalized additive models. This combination allows us to learn complex relationships between features while still being able to see what’s happening inside the model. The method also includes a special technique to help discover patterns in the data, making it more transparent and helpful. |
Keywords
» Artificial intelligence » Deep learning » Neural network
