The classical XY model is a lattice model of statistical mechanics notable for its universality in the rich hierarchy of the optical, laser and condensed matter systems. We show how to build complex structures for machine learning based on the XY model's nonlinear blocks. The final target is to reproduce the deep learning architectures, which can perform complicated tasks usually attributed to such architectures: speech recognition, visual processing, or other complex classification types with high quality. We developed the robust and transparent approach for the construction of such models, which has universal applicability (i.e. does not strongly connect to any particular physical system), allows many possible extensions while at the same time preserving the simplicity of the methodology.
翻译:古典XY模型是一个统计力学的套装模型,其普遍性在光学、激光和浓缩物质系统的丰富层次上值得注意。我们展示了如何在XY模型的非线性区块的基础上为机器学习建立复杂的结构。最终目标是复制深层次的学习结构,这些结构可以履行通常属于这类结构的复杂任务:语音识别、视觉处理或其他高质量的复杂分类类型。我们为构建这种模型制定了稳健和透明的方法,这种模型具有普遍适用性(即不与任何特定的物理系统紧密连接),允许许多可能的扩展,同时保持方法的简单性。