Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Universal approximation properties of shallow quadratic neural networks   In this paper we propose a new class of neural network functions which are
linear combinations of compositions of activation functions with quadratic
functions, replacing standard affine linear functions, often called neurons. We
show the universality of this approximation and prove convergence rates results
based on the theory of wavelets and statistical learning. We investigate the
efficiency of our new approach numerically for simple test cases, by showing
that it requires less numbers of neurons that standard affine linear neural
networks. Similar observations are made when comparing deep (multi-layer)
networks.

18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an