论文标题
学习微不足道的流程
Learning trivializing flows
论文作者
论文摘要
最近引入了机器学习技术，尤其是使晶格规定进行抽样的归一化流，对提高传统HMC算法的采样效率的希望给人带来了一些希望。天真地使用归一化流量已显示导致体积的缩放不佳。在本次演讲中，我们建议使用相关长度给出的局部归一化流。即使这些转化的天气很小，当与HMC算法结合使用时，也会导致算法高接受，并且与HMC相比，自相关时间降低。在2D中的$ ϕ^{4} $理论中执行了几个缩放测试。
The recent introduction of machine learning techniques, especially normalizing flows, for the sampling of lattice gauge theories has shed some hope on improving the sampling efficiency of the traditional HMC algorithm. Naive use of normalizing flows has been shown to lead to bad scaling with the volume. In this talk we propose using local normalizing flows at a scale given by the correlation length. Even if naively these transformations have a small acceptance, when combined with the HMC algorithm lead to algorithms with high acceptance, and also with reduced autocorrelation times compared with HMC. Several scaling tests are performed in the $ϕ^{4}$ theory in 2D.
