New research bounds transformer attention distribution for improved training stability

Researchers have developed a new method to analyze the local Lipschitz constant of transformer self-attention blocks, revealing its dependence on attention map distributions. This work introduces JaSMin, a regularizer designed to control this constant and enhance transformer training stability. The findings also shed light on how attention map distributions influence gradient dynamics. AI

IMPACT Provides theoretical insights that could lead to more stable and efficient transformer training.

RANK_REASON The cluster contains a new academic paper detailing theoretical advancements in transformer architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Hugging Face
• IArxiv
• Jasmin
• Nikolay Yudin
• Softmax
• transformers

• paper
• infra

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