Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimating Nonlinear Dynamics with the ConvNet Smoother   Estimating the state of a dynamical system from a series of noise-corrupted
observations is fundamental in many areas of science and engineering. The most
well-known method, the Kalman smoother (and the related Kalman filter), relies
on assumptions of linearity and Gaussianity that are rarely met in practice. In
this paper, we introduced a new dynamical smoothing method that exploits the
remarkable capabilities of convolutional neural networks to approximate complex
non-linear functions. The main idea is to generate a training set composed of
both latent states and observations from an ensemble of simulators and to train
the deep network to recover the former from the latter. Importantly, this
method only requires the availability of the simulators and can therefore be
applied in situations in which either the latent dynamical model or the
observation model cannot be easily expressed in closed form. In our simulation
studies, we show that the resulting ConvNet smoother has almost optimal
performance in the Gaussian case even when the parameters are unknown.
Furthermore, the method can be successfully applied to extremely non-linear and
non-Gaussian systems. Finally, we empirically validate our approach via the
analysis of measured brain signals.

23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th