A new paper from Kelvin Kan explores the stability of deep Transformers during training, focusing on the placement of layer normalization. The research provides theoretical insights into how different placements affect the growth of hidden states and the backpropagation of gradients. This analysis offers guidance for optimizing Transformer architectures and scaling residual steps for improved stability and performance. AI
IMPACT Provides theoretical guidance for improving the stability and performance of Transformer models.
RANK_REASON Academic paper published on arXiv detailing theoretical analysis of model architecture. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- IArxiv
- Kelvin Kan
- layer normalization
- ScienceCast
- transformers
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