Summary of On the Principles Of Relu Networks with One Hidden Layer, by Changcun Huang
On the Principles of ReLU Networks with One Hidden Layer
by Changcun Huang
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 The paper systematically studies the first problem by constructing universal function-approximation solutions, aiming to understand the mechanism of simple feedforward neural networks trained with the back-propagation algorithm. It’s shown that training solutions for one-dimensional inputs can be completely understood and those for higher-dimensional inputs can be well interpreted. These results pave the way for thoroughly revealing the black box of two-layer ReLU networks, advancing our understanding of deep ReLU networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to figure out how simple neural networks work when they’re trained using a special algorithm called back-propagation. Right now, we don’t fully understand why these networks produce certain answers or how to control their training process. The researchers in this study want to change that by showing that even the simplest networks can be understood if you look at them from a specific angle. They found that for some types of inputs, it’s possible to completely understand how the network produces its answers. This discovery could help us better understand more complex neural networks and make them work better. |
Keywords
» Artificial intelligence » Relu
