Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Symmetry-Based Disentangled Representation Learning requires Interaction
  with Environments   Finding a generally accepted formal definition of a disentangled
representation in the context of an agent behaving in an environment is an
important challenge towards the construction of data-efficient autonomous
agents. Higgins et al. recently proposed Symmetry-Based Disentangled
Representation Learning, a definition based on a characterization of symmetries
in the environment using group theory. We build on their work and make
observations, theoretical and empirical, that lead us to argue that
Symmetry-Based Disentangled Representation Learning cannot only be based on
static observations: agents should interact with the environment to discover
its symmetries. Our experiments can be reproduced in Colab and the code is
available on GitHub.

29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph