Year: 2023
Author: Haotian Jiang, Qianxiao Li, Zhong Li, Shida Wang
Journal of Machine Learning, Vol. 2 (2023), Iss. 1 : pp. 1–30
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.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jml.221221
Journal of Machine Learning, Vol. 2 (2023), Iss. 1 : pp. 1–30
Published online: 2023-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 30
Keywords: Approximation theory Sequence modelling Machine learning Deep learning Dynamics.