Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning Etudes in Conformal Field Theories   We demonstrate that various aspects of Conformal Field Theory are amenable to
machine learning. Relatively modest feed-forward neural networks are able to
distinguish between scale and conformal invariance of a three-point function
and identify a crossing-symmetric four-point function to nearly a hundred
percent accuracy. Furthermore, neural networks are also able to identify
conformal blocks appearing in a putative CFT four-point function and predict
the values of the corresponding OPE coefficients. Neural networks also
successfully classify primary operators by their quantum numbers under discrete
symmetries in the CFT from examining OPE data. We also demonstrate that neural
networks are able to learn the available OPE data for scalar correlation
function in the 3d Ising model and predict the twists of higher-spin operators
that appear in scalar OPE channels by regression.

28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
1
The categories of this paper are: cs