Neural Scaling Laws of Transformer Operator Network
This work develops a theoretical framework for characterizing the approximation and generalization errors of transformer-based operator learning, and derives a universal approximation error of transformer-based operator learning for H\"older-regular operators".
Hao-Ran Yan, Zhong-Jie Shi, Yuan-Zhe Xi et al.
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