Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Compact Neural Networks Using Ordinary Differential Equations
  as Activation Functions   Most deep neural networks use simple, fixed activation functions, such as
sigmoids or rectified linear units, regardless of domain or network structure.
We introduce differential equation units (DEUs), an improvement to modern
neural networks, which enables each neuron to learn a particular nonlinear
activation function from a family of solutions to an ordinary differential
equation. Specifically, each neuron may change its functional form during
training based on the behavior of the other parts of the network. We show that
using neurons with DEU activation functions results in a more compact network
capable of achieving comparable, if not superior, performance when is compared
to much larger networks.

8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th