Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Communicate: Channel Auto-encoders, Domain Specific
  Regularizers, and Attention   We address the problem of learning efficient and adaptive ways to communicate
binary information over an impaired channel. We treat the problem as
reconstruction optimization through impairment layers in a channel autoencoder
and introduce several new domain-specific regularizing layers to emulate common
channel impairments. We also apply a radio transformer network based attention
model on the input of the decoder to help recover canonical signal
representations. We demonstrate some promising initial capacity results from
this architecture and address several remaining challenges before such a system
could become practical.

7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con