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Título:
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Learning a local symmetry with neural networks
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Autores:
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Decelle, A. ;
Martín Mayor, Víctor ;
Seoane, B.
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Tipo de documento:
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texto impreso
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Editorial:
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American Physical Society, 2019-11-06
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Dimensiones:
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application/pdf
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Nota general:
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info:eu-repo/semantics/openAccess
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Idiomas:
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Palabras clave:
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Estado = Publicado
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Materia = Ciencias: Física
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Tipo = Artículo
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Resumen:
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We explore the capacity of neural networks to detect a symmetry with complex local and non-local patterns: the gauge symmetry Z(2). This symmetry is present in physical problems from topological transitions to quantum chromodynamics, and controls the computational hardness of instances of spin-glasses. Here, we show how to design a neural network, and a dataset, able to learn this symmetry and to find compressed latent representations of the gauge orbits. Our method pays special attention to system-wrapping loops, the so-called Polyakov loops, known to be particularly relevant for computational complexity.
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En línea:
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https://eprints.ucm.es/58091/1/Mart%C3%ADnMayorV%20Libre%2059.pdf
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