Why is normalization more effective for neural networks?
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Normalization is more effective for neural networks because it helps accelerate training and improve generalization . It achieves this by ensuring that any change in the input, such as shifting or scaling, results in a corresponding change in the system response . Normalization methods provide a systematic and mathematically informed way to choose learning rate hyperparameters, guaranteeing statistically robust behavior as the network size increases . Additionally, normalization helps stabilize optimization algorithms and enables them to explore a continuum of minima, leading to better performance . Experimental results show that normalization-equivariant neural networks provide better conditioning, generalization across noise levels, and improved prediction performance .
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| Papers (4) | Insight |
|---|---|
| Normalization is not explicitly discussed in the provided paper. | |
08 Jun 2023 | Normalization is more effective for neural networks because it ensures that any change in the input, such as scaling or shifting, results in a corresponding change in the system response, leading to better conditioning and improved generalization capabilities. |
| Normalization is more effective for neural networks because it reduces the variance of the network's output and improves test accuracy, especially when the normalization factor is equal to one (mean-field scaling). | |
| Normalization is more effective for neural networks because it accelerates training and improves generalization by addressing optimization issues and providing a unified approach to normalizing activation methods. |
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