Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Network-based Quantization for Network Automation   Deep Learning methods have been adopted in mobile networks, especially for
network management automation where they provide means for advanced machine
cognition. Deep learning methods utilize cutting-edge hardware and software
tools, allowing complex cognitive algorithms to be developed. In a recent
paper, we introduced the Bounding Sphere Quantization (BSQ) algorithm, a
modification of the k-Means algorithm, that was shown to create better
quantizations for certain network management use-cases, such as anomaly
detection. However, BSQ required a significantly longer time to train than
k-Means, a challenge which can be overcome with a neural network-based
implementation. In this paper, we present such an implementation of BSQ that
utilizes state-of-the-art deep learning tools to achieve a competitive training
speed.

21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin