Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 3D Steerable CNNs: Learning Rotationally Equivariant Features in
  Volumetric Data   We present a convolutional network that is equivariant to rigid body motions.
The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to
represent data, and equivariant convolutions to map between such
representations. These SE(3)-equivariant convolutions utilize kernels which are
parameterized as a linear combination of a complete steerable kernel basis,
which is derived analytically in this paper. We prove that equivariant
convolutions are the most general equivariant linear maps between fields over
R^3. Our experimental results confirm the effectiveness of 3D Steerable CNNs
for the problem of amino acid propensity prediction and protein structure
classification, both of which have inherent SE(3) symmetry.

9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th