论文标题
连接二元性和机器学习
Connecting Dualities and Machine Learning
论文作者
论文摘要
二元性广泛用于量子场理论和弦理论，以高精度获得相关函数。在这里，我们介绍示例，其中双重数据表示在监督分类，链接机器学习和理论物理中的典型任务中有用。然后，我们讨论如何在神经网络的潜在维度中执行这种有益的表示。我们发现，基于特征分离，相对于所需表示的特征匹配对损失的额外贡献，并且在“简单”相关函数上的良好性能可以导致已知和未知的双重表示。这是计算机可以找到二元性的第一个概念证明。我们讨论了基于离散的傅立叶变换和ISING模型的示例如何连接到理论物理学中的其他二元性，例如Seiberg二元性。
Dualities are widely used in quantum field theories and string theory to obtain correlation functions at high accuracy. Here we present examples where dual data representations are useful in supervised classification, linking machine learning and typical tasks in theoretical physics. We then discuss how such beneficial representations can be enforced in the latent dimension of neural networks. We find that additional contributions to the loss based on feature separation, feature matching with respect to desired representations, and a good performance on a `simple' correlation function can lead to known and unknown dual representations. This is the first proof of concept that computers can find dualities. We discuss how our examples, based on discrete Fourier transformation and Ising models, connect to other dualities in theoretical physics, for instance Seiberg duality.
