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About 6114 Weight Normalization A Simple Reparameterization To Accelerate Training of Deep Neural Networks
Weight normalization is a simple reparameterization technique to accelerate training of neural networks. It decouples the length and direction of weight vectors by representing each weight vector as the product of a parameter vector and a scale parameter. This improves the conditioning of optimization and speeds up convergence. Weight normalization provides much of the benefits of batch normalization but can be applied to more models as it does not introduce dependencies between examples. It has lower computational overhead than batch normalization. The authors demonstrate its effectiveness on image recognition, generative modeling, and reinforcement learning tasks.
Detailed Information
| Author: | ['Jose Miguel Alves'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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