TY - JOUR
T1 - A Brief Survey on the Approximation Theory for Sequence Modelling
AU - Jiang , Haotian
AU - Li , Qianxiao
AU - Li , Zhong
AU - Wang , Shida
JO - Journal of Machine Learning
VL - 1
SP - 1
EP - 30
PY - 2023
DA - 2023/03
SN - 2
DO - http://doi.org/10.4208/jml.221221
UR - https://global-sci.org/intro/article_detail/jml/21511.html
KW - Approximation theory, Sequence modelling, Machine learning, Deep learning, Dynamics.
AB - <p style="text-align: justify;">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.</p>