Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Evolving Deep Neural Networks   The success of deep learning depends on finding an architecture to fit the
task. As deep learning has scaled up to more challenging tasks, the
architectures have become difficult to design by hand. This paper proposes an
automated method, CoDeepNEAT, for optimizing deep learning architectures
through evolution. By extending existing neuroevolution methods to topology,
components, and hyperparameters, this method achieves results comparable to
best human designs in standard benchmarks in object recognition and language
modeling. It also supports building a real-world application of automated image
captioning on a magazine website. Given the anticipated increases in available
computing power, evolution of deep networks is promising approach to
constructing deep learning applications in the future.

14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex