Standard Transformers achieve optimal rates in nonparametric regression

A new arXiv paper demonstrates that standard Transformer models can achieve optimal rates in nonparametric regression tasks when approximating Hölder functions. The research provides a theoretical foundation for the effectiveness of Transformers in areas like large language models and computer vision. The study also introduces metrics to characterize Transformer structures, which could aid future research into their generalization and optimization errors. AI

IMPACT Provides theoretical justification for the capabilities of Transformer models in AI applications.

RANK_REASON Academic paper published on arXiv detailing theoretical properties of Transformer models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• computer vision
• Hölder functions
• large language models
• nonparametric regression
• Transformer
• Yanming Lai

• paper
• other

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