论文标题
在神经网络中学习和优化黑框组合求解器
Learning and Optimization of Blackbox Combinatorial Solvers in Neural Networks
论文作者
论文摘要
神经网络内的黑盒求解器的使用是一个相对较新的领域，旨在通过包括经过验证的有效求解器来提高神经网络性能。现有工作创建了将这些求解器作为组件学习网络的方法，同时将它们视为黑框。这项工作不仅通过使用时间成本正则化来优化求解器本身的性能，从而通过优化求解器本身的性能来改善现有技术。此外，我们提出了一种学习黑框参数的方法，例如要使用哪个BlackBox求解器或特定求解器的启发式函数。我们通过介绍Hyper-Blackbox的想法来做到这一点，该盒子是一个或多个内部黑盒周围的黑框。
The use of blackbox solvers inside neural networks is a relatively new area which aims to improve neural network performance by including proven, efficient solvers for complex problems. Existing work has created methods for learning networks with these solvers as components while treating them as a blackbox. This work attempts to improve upon existing techniques by optimizing not only over the primary loss function, but also over the performance of the solver itself by using Time-cost Regularization. Additionally, we propose a method to learn blackbox parameters such as which blackbox solver to use or the heuristic function for a particular solver. We do this by introducing the idea of a hyper-blackbox which is a blackbox around one or more internal blackboxes.
