Summary of The Sampling Complexity Of Learning Invertible Residual Neural Networks, by Yuanyuan Li et al.
The sampling complexity of learning invertible residual neural networks
by Yuanyuan Li, Philipp Grohs, Philipp Petersen
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 investigates the limitations of determining feedforward ReLU neural networks to within high uniform accuracy using point samples. Specifically, it reveals that the curse of dimensionality affects the number of samples needed to achieve this level of accuracy, making these networks unsuitable for applications where guaranteed accuracy is crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers found that it’s hard to make sure a special kind of neural network (called feedforward ReLU) is really accurate if you only have a few test points. This means these networks aren’t great for things like self-driving cars or medical equipment, where accuracy matters most. |
Keywords
» Artificial intelligence » Neural network » Relu
