Summary of Hybrid Deep Additive Neural Networks, by Gyu Min Kim and Jeong Min Jeon
Hybrid deep additive neural networks
by Gyu Min Kim, Jeong Min Jeon
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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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 research paper introduces novel deep neural networks that incorporate the idea of additive regression, offering an alternative to traditional linear combination-based models. By combining architectural elements from Kolmogorov-Arnold networks and simpler activation functions, these new networks demonstrate improved performance and reduced parameter requirements. The authors derive universal approximation properties and showcase their effectiveness through simulations and a real-world application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates new types of neural networks that work differently than the ones we use now. These new networks are based on adding things together instead of combining them linearly, which makes them more powerful and uses fewer parameters. The authors show that these new networks can do better than the old ones in some situations, and they also explain how they work mathematically. |
Keywords
* Artificial intelligence * Regression
