Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Optimize with Unsupervised Learning: Training Deep Neural
  Networks for URLLC   Learning the optimized solution as a function of environmental parameters is
effective in solving numerical optimization in real time for time-sensitive
applications. Existing works of learning to optimize train deep neural networks
(DNN) with labels, and the learnt solution are inaccurate, which cannot be
employed to ensure the stringent quality of service. In this paper, we propose
a framework to learn the latent function with unsupervised deep learning, where
the property that the optimal solution should satisfy is used as the
"supervision signal" implicitly. The framework is applicable to both functional
and variable optimization problems with constraints. We take a variable
optimization problem in ultra-reliable and low-latency communications as an
example, which demonstrates that the ultra-high reliability can be supported by
the DNN without supervision labels.

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The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph