Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Learning-Based Framework for Line-Spectra Super-resolution   We propose a learning-based approach for estimating the spectrum of a
multisinusoidal signal from a finite number of samples. A neural-network is
trained to approximate the spectra of such signals on simulated data. The
proposed methodology is very flexible: adapting to different signal and noise
models only requires modifying the training data accordingly. Numerical
experiments show that the approach performs competitively with classical
methods designed for additive Gaussian noise at a range of noise levels, and is
also effective in the presence of impulsive noise.

27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph