Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discovering dependencies in complex physical systems using Neural
  Networks   In todays age of data, discovering relationships between different variables
is an interesting and a challenging problem. This problem becomes even more
critical with regards to complex dynamical systems like weather forecasting and
econometric models, which can show highly non-linear behavior. A method based
on mutual information and deep neural networks is proposed as a versatile
framework for discovering non-linear relationships ranging from functional
dependencies to causality. We demonstrate the application of this method to
actual multivariable non-linear dynamical systems. We also show that this
method can find relationships even for datasets with small number of
datapoints, as is often the case with empirical data.

26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom