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.

本文提出了一种基于无监督深度学习的学习优化的框架，用于解决数值优化问题，可同时应用于函数和变量优化问题，并以超高可靠性通信为例，证明了深度学习可实现无监督学习下的变量优化保障极高的可靠性通信。

无监督学习优化：为 URLLC 训练深度神经网络

Learning to Optimize with Unsupervised Learning: Training Deep Neural  Networks for URLLC

We consider the problem of generating automatic code given sample
input-output pairs. We train a neural network to map from the current state and
the outputs to the program's next statement. The neural network optimizes
multiple tasks concurrently: the next operation out of a set of high level
commands, the operands of the next statement, and which variables can be
dropped from memory. Using our method we are able to create programs that are
more than twice as long as existing state-of-the-art solutions, while improving
the success rate for comparable lengths, and cutting the run-time by two orders
of magnitude. Our code, including an implementation of various literature
baselines, is publicly available at this https URL

本文研究给定样例输入输出对自动生成代码的问题，使用神经网络从当前状态和输出映射到程序的下一个语句，同时优化多个任务：从一组高级命令选择下一个操作，确定下一个语句的操作数以及可以从内存中删除哪些变量。使用该方法，我们能够创建比现有最先进的解决方案长两倍以上的程序，同时提高了相似长度的成功率，缩短了两个数量级的运行时间。我们的代码，包括各种文献基线的实现，可以在此 https URL 公开获取。