Summary of Large Deviations Of Gaussian Neural Networks with Relu Activation, by Quirin Vogel
Large Deviations of Gaussian Neural Networks with ReLU activation
by Quirin Vogel
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Probability (math.PR)
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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 presents a significant extension to existing work on large deviation principles for deep neural networks with Gaussian weights. The authors generalize earlier findings by considering activation functions that grow linearly, which is more representative of common practical applications. The results simplify previous expressions for the rate function and provide power-series expansions for the popular ReLU (Rectified Linear Unit) activation function. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning has made huge progress in recent years, but understanding how neural networks behave in unusual situations is crucial for their reliable use. This paper helps with that by showing how deep nets with special types of connections (called weights and activations) work in extreme conditions. It’s important because it makes predictions about what will happen when these conditions occur. |
Keywords
» Artificial intelligence » Deep learning » Relu
