A Brief Survey on the Approximation Theory for Sequence Modelling

Haotian Jiang; Qianxiao Li; Shida Wang; Zhong Li

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A Brief Survey on the Approximation Theory for Sequence Modelling

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arxiv 2302.13752 v1 pith:S6QCJ25G submitted 2023-02-27 cs.LG

Haotian Jiang , Qianxiao Li , Zhong Li , Shida Wang This is my paper

classification cs.LG

keywords approximationmodellingsequencetheoryresultssurveyarchitecturesbrief

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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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