玻尔兹曼机能发现集群更新吗？
Can Boltzmann Machines Discover Cluster Updates ?
摘要：
玻尔兹曼机是一种物理生成模型，在机器学习中有着广泛的应用。它们可以从输入数据集中学习概率分布，并相应地生成新的样本。将玻尔兹曼机应用于物理学，由于其灵活性和有效性，玻尔兹曼机是加速物理系统的蒙特卡罗模拟的理想推荐系统。更有趣的是，我们证明了玻尔兹曼机的生成采样甚至可以发现未知的簇蒙特卡罗算法。创造力来自玻尔兹曼机器的潜在表现，它学会调解复杂的相互作用并识别物理系统的集群。我们证明了这些发现与经典的伊辛模型的具体例子和没有四个自旋元格相互作用。我们的研究结果支持了一种新的研究范式，即智能机器旨在创造或激发人类发现创新算法。
英文摘要：
Boltzmann machines are physics informed generative models with wide applications in machine learning. They can learn the probability distribution from an input dataset and generate new samples accordingly. Applying them back to physics, the Boltzmann machines are ideal recommender systems to accelerate Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann Machines can even discover unknown cluster Monte Carlo algorithms. The creative power comes from the latent representation of the Boltzmann machines, which learn to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four spin plaquette interactions. Our results endorse a fresh research paradigm where intelligent machines are designed to create or inspire human discovery of innovative algorithms.
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。
