Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning a Local Symmetry with Neural-Networks   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 QCD, 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.

36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex