Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discovering Nonlinear Relations with Minimum Predictive Information
  Regularization   Identifying the underlying directional relations from observational time
series with nonlinear interactions and complex relational structures is key to
a wide range of applications, yet remains a hard problem. In this work, we
introduce a novel minimum predictive information regularization method to infer
directional relations from time series, allowing deep learning models to
discover nonlinear relations. Our method substantially outperforms other
methods for learning nonlinear relations in synthetic datasets, and discovers
the directional relations in a video game environment and a heart-rate vs.
breath-rate dataset.

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The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph