  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 of words. In this article, we investigate the finite case and
constructively prove the existence of a finite \emph{vocabulary} $V=\{\phi_i:
\mathbb{R}^d \to \mathbb{R}^d | i=1,...,n\}$ with $n=O(d^2)$ for the universal
approximation. That is, for any continuous mapping $f: \mathbb{R}^d \to
\mathbb{R}^d$, compact domain $\Omega$ and $\varepsilon>0$, there is a sequence
of mappings $\phi_{i_1}, ..., \phi_{i_m} \in V, m \in \mathbb{Z}_+$, such that
the composition $\phi_{i_m} \circ ... \circ \phi_{i_1} $ approximates $f$ on
$\Omega$ with an error less than $\varepsilon$. Our results demonstrate an
unusual approximation power of mapping compositions and motivate a novel
compositional model for regular languages.
