论文标题
机器学习辅助的蒙特卡洛在抽样计算方面失败了
Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems
论文作者
论文摘要
最近已经提出了几种策略，以提高使用机器学习工具提高蒙特卡洛抽样效率。在这里，我们通过考虑一系列问题，这些问题在足够低的温度下使用常规的局部蒙特卡洛（Monte Carlo）来挑战这些方法。特别是，我们在随机图上研究了抗磁POTTS模型，该模型将在零温度下降低到随机图的着色。我们测试了几种机器学习辅助的蒙特卡洛方法，我们发现它们都失败了。因此，我们的工作为未来的智能采样算法提供了良好的基准。
Several strategies have been recently proposed in order to improve Monte Carlo sampling efficiency using machine learning tools. Here, we challenge these methods by considering a class of problems that are known to be exponentially hard to sample using conventional local Monte Carlo at low enough temperatures. In particular, we study the antiferromagnetic Potts model on a random graph, which reduces to the coloring of random graphs at zero temperature. We test several machine-learning-assisted Monte Carlo approaches, and we find that they all fail. Our work thus provides good benchmarks for future proposals for smart sampling algorithms.
