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About Rezero Is All You Need: Fast Convergence at Large Depth: Authors Contributed Equally, Ordered by Last Name
This document summarizes the ReZero technique for improving training of deep neural networks. ReZero addresses the problem of vanishing or exploding gradients in deep networks by introducing a residual connection for each layer's input and output, with a single trainable parameter that modulates the layer transformation. This simple approach enables training very deep networks of thousands of layers, outperforming more complex normalization and initialization schemes. When applied to Transformers, ReZero reduces training time by 56% to reach a target performance level. The key advantages are that ReZero is widely applicable, enables deeper learning, and accelerates convergence compared to regular residual networks.
Detailed Information
| Author: | ['NiranjanAryan'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 14 |
| Language: | English |
| Format: | |
| Price: | FREE |
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