Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to imitate stochastic time series in a compositional way by
  chaos   This study shows that a mixture of RNN experts model can acquire the ability
to generate sequences combining multiple primitive patterns by means of
self-organizing chaos. By training of the model, each expert learns a primitive
sequence pattern, and a gating network learns to imitate stochastic switching
of the multiple primitives via a chaotic dynamics, utilizing a sensitive
dependence on initial conditions. As a demonstration, we present a numerical
simulation in which the model learns Markov chain switching among some
Lissajous curves by a chaotic dynamics. Our analysis shows that by using a
sufficient amount of training data, balanced with the network memory capacity,
it is possible to satisfy the conditions for embedding the target stochastic
sequences into a chaotic dynamical system. It is also shown that reconstruction
of a stochastic time series by a chaotic model can be stabilized by adding a
negligible amount of noise to the dynamics of the model.

4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
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The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph