Summary of Universal Approximation Theorem For Neural Networks with Inputs From a Topological Vector Space, by Vugar Ismailov
Universal approximation theorem for neural networks with inputs from a topological vector space
by Vugar Ismailov
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE); 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 explores feedforward neural networks that can handle a wider range of inputs, including sequences, matrices, and functions, by drawing from topological vector spaces. The authors prove a universal approximation theorem for these TVS-FNNs, showing their ability to approximate any continuous function defined on this expanded input space. The study highlights the potential applications of such neural networks in processing diverse data types, improving their capacity for learning and generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at special kinds of computer programs called feedforward neural networks that can take in different types of information, like lists or math problems. They show that these networks are really good at copying any continuous pattern they see, which is important because it means they can learn from a lot more data than before. |
Keywords
» Artificial intelligence » Generalization
