论文标题
评估神经组合优化中的课程学习策略
Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization
论文作者
论文摘要
神经组合优化（NCO）旨在设计与问题无关，有效的神经网络的策略来解决组合问题。该领域最近通过成功调整最初是为机器翻译设计的架构而经历了增长。即使结果令人鼓舞，在准确性和效率方面，NCO模型和经典确定性求解器之间仍然存在很大的差距。当前方法的缺点之一是对多个问题大小的培训效率低下。课程学习策略已被证明有助于提高多任务设置的性能。在这项工作中，我们专注于设计基于课程的培训程序，该程序可以帮助现有体系结构同时在各种问题上实现竞争性能。我们对几种培训程序进行了系统的研究，并利用获得的见解来激励采用心理启发的方法来改善经典课程方法。
Neural combinatorial optimization (NCO) aims at designing problem-independent and efficient neural network-based strategies for solving combinatorial problems. The field recently experienced growth by successfully adapting architectures originally designed for machine translation. Even though the results are promising, a large gap still exists between NCO models and classic deterministic solvers, both in terms of accuracy and efficiency. One of the drawbacks of current approaches is the inefficiency of training on multiple problem sizes. Curriculum learning strategies have been shown helpful in increasing performance in the multi-task setting. In this work, we focus on designing a curriculum learning-based training procedure that can help existing architectures achieve competitive performance on a large range of problem sizes simultaneously. We provide a systematic investigation of several training procedures and use the insights gained to motivate application of a psychologically-inspired approach to improve upon the classic curriculum method.
