Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Computationally Efficient Neural Network Invariant to the Action of
  Symmetry Subgroups   We introduce a method to design a computationally efficient $G$-invariant
neural network that approximates functions invariant to the action of a given
permutation subgroup $G \leq S_n$ of the symmetric group on input data. The key
element of the proposed network architecture is a new $G$-invariant
transformation module, which produces a $G$-invariant latent representation of
the input data. This latent representation is then processed with a multi-layer
perceptron in the network. We prove the universality of the proposed
architecture, discuss its properties and highlight its computational and memory
efficiency. Theoretical considerations are supported by numerical experiments
involving different network configurations, which demonstrate the effectiveness
and strong generalization properties of the proposed method in comparison to
other $G$-invariant neural networks.

24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics