Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using accumulation to optimize deep residual neural nets   Residual Neural Networks [1] won first place in all five main tracks of the
ImageNet and COCO 2015 competitions. This kind of network involves the creation
of pluggable modules such that the output contains a residual from the input.
The residual in that paper is the identity function. We propose to include
residuals from all lower layers, suitably normalized, to create the residual.
This way, all previous layers contribute equally to the output of a layer. We
show that our approach is an improvement on [1] for the CIFAR-10 dataset.

2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph