A Brief Survey on the Approximation Theory for Sequence Modelling
Haotian Jiang, Qianxiao Li, Zhong Li, Shida Wang
TL;DR
This survey reviews recent advances in the approximation theory of sequence modelling in machine learning, classifying results by classical paradigms and discussing future research directions.
Contribution
It provides a comprehensive classification of existing approximation results for sequence models and outlines future directions for theoretical development.
Findings
Classifies sequence modelling results using classical approximation paradigms
Highlights insights gained from approximation theory in sequence models
Suggests future research directions for theoretical understanding
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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Taxonomy
TopicsMachine Learning and Algorithms · Constraint Satisfaction and Optimization · Matrix Theory and Algorithms