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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Aug 10·5 min read
Stability of Transformers under Layer Normalization
TL;DR
A principled study on layer normalization in Transformers reveals its critical role in enhancing training stability. The findings provide explicit bounds on hidden state growth and insights into gradient backpropagation, guiding future architectural designs.
✦ Why It Matters
Evaluate your Transformer architectures by applying the insights on layer normalization placement for improved stability.
Key Takeaways
- Analyze layer normalization placement to enhance training stability.
- Use derived bounds to assess hidden state growth in Transformers.
- Consider the impact of normalization on gradient backpropagation.
- Apply findings to guide architectural modifications in future designs.
How It Works
The study provides a theoretical framework that quantifies the effects of layer normalization on both forward and backward stability in Transformers. By deriving explicit bounds on hidden state growth, it clarifies how different placements of layer normalization can lead to either regular or pathological training behaviors.
Additionally, the analysis of gradient backpropagation reveals how normalization affects learning dynamics across layers.