Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Auto Deep Compression by Reinforcement Learning Based Actor-Critic
  Structure   Model-based compression is an effective, facilitating, and expanded model of
neural network models with limited computing and low power. However,
conventional models of compression techniques utilize crafted features [2,3,12]
and explore specialized areas for exploration and design of large spaces in
terms of size, speed, and accuracy, which usually have returns Less and time is
up. This paper will effectively analyze deep auto compression (ADC) and
reinforcement learning strength in an effective sample and space design, and
improve the compression quality of the model. The results of compression of the
advanced model are obtained without any human effort and in a completely
automated way. With a 4- fold reduction in FLOP, the accuracy of 2.8% is higher
than the manual compression model for VGG-16 in ImageNet.

15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph