May 9, 2024, 4:42 a.m. | Dian Xu, Shanshan Wang, Feng Gao, Wei Li, Jianmin Shen

cs.LG updates on

arXiv:2311.14725v2 Announce Type: replace-cross
Abstract: Machine learning has recently achieved remarkable success in studying phase transitions. It is generally believed that the latent variables of unsupervised learning can capture the information related to phase transitions, which is usually achieved through the so-called order parameter. In most models, for instance the Ising, the order parameters are simply the particle number densities. The percolation, the simplest model which can generate a phase transition, however, has a unique order parameter which is not …

abstract arxiv cond-mat.stat-mech cs.lg information machine machine learning particle scaling studying success the information through transitions type unsupervised unsupervised learning variables

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