Neural Discovery of Permutation Subgroups

09/11/2023
by   Pavan Karjol, et al.
0

We consider the problem of discovering subgroup H of permutation group S_n. Unlike the traditional H-invariant networks wherein H is assumed to be known, we present a method to discover the underlying subgroup, given that it satisfies certain conditions. Our results show that one could discover any subgroup of type S_k (k ≤ n) by learning an S_n-invariant function and a linear transformation. We also prove similar results for cyclic and dihedral subgroups. Finally, we provide a general theorem that can be extended to discover other subgroups of S_n. We also demonstrate the applicability of our results through numerical experiments on image-digit sum and symmetric polynomial regression tasks.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/30/2022

Involutory permutation automorphisms of binary linear codes

We investigate the properties of binary linear codes of even length whos...
research
12/11/2020

A New Neural Network Architecture Invariant to the Action of Symmetry Subgroups

We propose a computationally efficient G-invariant neural network that a...
research
09/06/2023

A Unified Framework for Discovering Discrete Symmetries

We consider the problem of learning a function respecting a symmetry fro...
research
09/05/2021

The local-global property for G-invariant terms

For some Maltsev conditions Σ it is enough to check if a finite algebra ...
research
02/18/2020

A Computationally Efficient Neural Network Invariant to the Action of Symmetry Subgroups

We introduce a method to design a computationally efficient G-invariant ...
research
10/29/2020

Capacity-achieving codes: a review on double transitivity

Recently it was proved that if a linear code is invariant under the acti...
research
04/02/2021

Permutation-Invariant Subgraph Discovery

We introduce Permutation and Structured Perturbation Inference (PSPI), a...

Please sign up or login with your details

Forgot password? Click here to reset