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.
翻译:我们调查了机器学习中序列建模近似理论的当前发展情况。我们特别强调通过古典近似范式的透镜对各种模型结构的现有结果进行分类,并从这些结果中获得洞察力。我们还概述了未来建立序列建模理论的研究方向。</s>