关于重复模拟退火算法的几点注记
Some Remarks on Replicated Simulated Annealing
摘要：
最近，作者介绍了训练离散权重神经网络的想法，使用经典模拟退火和从统计物理文献中已知的副本退火之间的混合。除此之外，他们声称他们的方法能够找到鲁棒的配置。在本文中，我们分析了这种所谓的“复制模拟退火”算法。特别是，我们明确的标准，以保证其收敛性，并研究当它成功地从配置采样。我们还使用合成和真实的数据库进行实验。
英文摘要：
Recently authors have introduced the idea of training discrete weights neural networks using a mix between classical simulated annealing and a replica ansatz known from the statistical physics literature. Among other points, they claim their method is able to find robust configurations. In this paper, we analyze this so-called "replicated simulated annealing" algorithm. In particular, we explicit criteria to guarantee its convergence, and study when it successfully samples from configurations. We also perform experiments using synthetic and real data bases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
