Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2019 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1904.07637 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aurélien Decelle | 1 | 0 | 0.34 |
| Victor Martin-Mayor | 2 | 0 | 0.68 |
| B. Seoane | 3 | 11 | 2.78 |