Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantisation and Pruning for Neural Network Compression and
  Regularisation   Deep neural networks are typically too computationally expensive to run in
real-time on consumer-grade hardware and low-powered devices. In this paper, we
investigate reducing the computational and memory requirements of neural
networks through network pruning and quantisation. We examine their efficacy on
large networks like AlexNet compared to recent compact architectures:
ShuffleNet and MobileNet. Our results show that pruning and quantisation
compresses these networks to less than half their original size and improves
their efficiency, particularly on MobileNet with a 7x speedup. We also
demonstrate that pruning, in addition to reducing the number of parameters in a
network, can aid in the correction of overfitting.

28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con