A Brief Survey on the Approximation Theory for Sequence Modelling

Haotian Jiang, Qianxiao Li, Zhong Li, Shida Wang
2/27/2023
cs.LG

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Abstract

We survey current developments in the approximation theory of sequence modelling in machine learning. Particular emphasis is placed on classifying existing results for various model architectures through the lens of classical approximation paradigms, and the insights one can gain from these results. We also outline some future research directions towards building a theory of sequence modelling.

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Cite this paper

@article{jiang2023brief,
  title  = {A Brief Survey on the Approximation Theory for Sequence Modelling},
  author = {Haotian Jiang and Qianxiao Li and Zhong Li and Shida Wang},
  year   = {2023},
  eprint = {2302.13752},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.13752v1}
}

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