Summary of Universal Approximation Property Of Banach Space-valued Random Feature Models Including Random Neural Networks, by Ariel Neufeld et al.
Universal approximation property of Banach space-valued random feature models including random neural networks
by Ariel Neufeld, Philipp Schmocker
First submitted to arxiv on: 13 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Probability (math.PR); 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 This paper introduces a novel approach to machine learning called Banach space-valued extension of random feature learning. This technique reduces computational complexity by randomly initializing feature maps and only training the linear readout. The authors prove a universal approximation result, derive approximation rates, and provide an algorithm for learning elements in Banach spaces using random neural networks. They also analyze the training costs of approximating functions with random feature models and demonstrate their advantages over deterministic counterparts through numerical examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is getting better at doing big tasks like image recognition and speech recognition. One way it does this is by using something called “random features”. This paper takes that idea and makes it work in a new way, which can help with things like image recognition and speech recognition too. They also showed that sometimes, using random features is actually better than using the normal way of doing things. |
Keywords
* Artificial intelligence * Machine learning
