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Vocabulary for Universal Approximation: A Linguistic Perspective of Mapping Compositions
May 24, 2024, 4:47 a.m. | Yongqiang Cai
cs.LG updates on arXiv.org arxiv.org
Abstract: In recent years, deep learning-based sequence modelings, such as language models, have received much attention and success, which pushes researchers to explore the possibility of transforming non-sequential problems into a sequential form. Following this thought, deep neural networks can be represented as composite functions of a sequence of mappings, linear or nonlinear, where each composition can be viewed as a \emph{word}. However, the weights of linear mappings are undetermined and hence require an infinite number …
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