Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Disentangled Representation for Ising Model   Finding disentangled representation plays a predominant role in the success
of modern deep learning applications, but the results lack a straightforward
explanation. Here we apply the information bottleneck method to find as an
example the disentangled low-dimensional representation of the Ising model. Our
results reveal a deep connection between the disentangled features and the
physical order parameters of the model. The widely-used Bernoulli decoder is
found to be learning a mean-field Hamiltonian at fixed temperature, which
motivates us to propose a modified architecture $\beta^2$-VAE to enforce
thermal fluctuations in the learned samples under generation. Our framework may
be extended to other more complex models to reveal potential connection between
their order parameters and disentangled low-dimensional representations.

21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th