论文标题
ML4CO-KIDA：数据集聚合中的知识继承
ML4CO-KIDA: Knowledge Inheritance in Dataset Aggregation
论文作者
论文摘要
组合优化（ML4CO）神经2021竞赛的机器学习旨在通过用机器学习模型替换关键的启发式组件来改善最新的组合优化求解器。在双重任务上，我们设计模型以做出分支决策以促进双重界限的速度更快。我们提出了一种知识继承方法，以从数据集聚合过程中概括不同模型的知识，名为KIDA。我们的改进克服了基线图形网络网络方法的一些缺陷。此外，我们在双重任务上赢得了$ 1 $ \ textsuperscript {st}。我们希望该报告可以为开发人员和研究人员提供有用的经验。该代码可在https://github.com/megvii-research/neurips2021-ml4co-kida上找到。
The Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition aims to improve state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning models. On the dual task, we design models to make branching decisions to promote the dual bound increase faster. We propose a knowledge inheritance method to generalize knowledge of different models from the dataset aggregation process, named KIDA. Our improvement overcomes some defects of the baseline graph-neural-networks-based methods. Further, we won the $1$\textsuperscript{st} Place on the dual task. We hope this report can provide useful experience for developers and researchers. The code is available at https://github.com/megvii-research/NeurIPS2021-ML4CO-KIDA.
