论文标题
关于旋转配置的神经网络流
On the neural network flow of spin configurations
论文作者
论文摘要
我们研究了2-D Ising Ferromagnet中自旋构型的所谓神经网络流。该流程是由旋转构型的连续重建产生的，旋转构型由人工神经网络（如受限的玻尔兹曼机器或自动编码器）获得。最近有报道说，该流量可能在系统的临界温度下具有固定点，甚至允许计算临界指数。在这里，我们专注于完全连接的自动编码器产生的流程，我们反驳说该流程通过直接测量物理可观察物来收敛到系统的临界点的说法，并表明该流量很大程度上取决于网络超级标准。我们探索网络度量，重建误差，并将其与所谓的数据的固有维度相关联，以阐明流动的原点和属性。
We study the so-called neural network flow of spin configurations in the 2-d Ising ferromagnet. This flow is generated by successive reconstructions of spin configurations, obtained by an artificial neural network like a restricted Boltzmann machine or an autoencoder. It was reported recently that this flow may have a fixed point at the critical temperature of the system, and even allow the computation of critical exponents. Here we focus on the flow produced by a fully-connected autoencoder, and we refute the claim that this flow converges to the critical point of the system by directly measuring physical observables, and showing that the flow strongly depends on the network hyperparameters. We explore the network metric, the reconstruction error, and we relate it to the so called intrinsic dimension of data, to shed light on the origin and properties of the flow.
