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Supervised and Unsupervised Deep Learning Applied to theMajority Vote Model

Francisco Welington de Sousa Lima, José Silva Neto, David Santana Marques Alencar, Lenilson Torres Brito, Gladstone Alencar Alves, A. Macedo-Filho, Ronan Silva Ferreira, T. F. A. Alves

Published Jan 1, 2025• 0 citations
Publisher
DOI
10.2139/ssrn.5561664

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The transfer learning of a neural network is one of its most outstanding aspects, and has given supervised learning with neural networks a prominent place in data science. Here we explore this feature in the context of strongly interacting many-body systems. By means of case studies, we show the potential of this deep learning technique to detect phases and their transitions in frustrated spin systems, using dense and convolutional neural networks. In addition, we explore a recently-introduced technique, which is at the middle point of supervised and unsupervised learning. It consists in evaluating the performance of a neural network which has been deliberately 'confused' during its training. To properly demonstrate the capability of the 'confusion' and transfer learning techniques, we apply them to a paradigmatic model of frustrated magnetism in two dimensions, to determine its phase diagram and compare it with high-performance Monte Carlo simulations.
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