Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepSwarm: Optimising Convolutional Neural Networks using Swarm
  Intelligence   In this paper we propose DeepSwarm, a novel neural architecture search (NAS)
method based on Swarm Intelligence principles. At its core DeepSwarm uses Ant
Colony Optimization (ACO) to generate ant population which uses the pheromone
information to collectively search for the best neural architecture.
Furthermore, by using local and global pheromone update rules our method
ensures the balance between exploitation and exploration. On top of this, to
make our method more efficient we combine progressive neural architecture
search with weight reusability. Furthermore, due to the nature of ACO our
method can incorporate heuristic information which can further speed up the
search process. After systematic and extensive evaluation, we discover that on
three different datasets (MNIST, Fashion-MNIST, and CIFAR-10) when compared to
existing systems our proposed method demonstrates competitive performance.
Finally, we open source DeepSwarm as a NAS library and hope it can be used by
more deep learning researchers and practitioners.

10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph