75 citations · 231 across the 8 of their papers we have counts for
Showing 2018Show all
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cond-mat.stat-mech2018★ 62 cited
Learning multiple order parameters with interpretable machines
Ke Liu, Jonas Greitemann, Lode Pollet
Machine-learning techniques are evolving into a subsidiary tool for studying phase transitions in many-body systems. However, most studies are tied to situations involving only one…
cond-mat.stat-mech2018★ 75 cited
Probing hidden spin order with interpretable machine learning
Jonas Greitemann, Ke Liu, Lode Pollet
The search of unconventional magnetic and nonmagnetic states is a major topic in the study of frustrated magnetism. Canonical examples of those states include various spin liquids…