Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual
  Information   GMI-based end-to-end learning is shown to be highly nonconvex. We apply
gradient descent initialized with Gray-labeled APSK constellations directly to
the constellation coordinates. State-of-the-art constellations in 2D and 4D are
found providing reach increases up to 26\% w.r.t. to QAM.

24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph