Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting publication productivity for authors: Shallow or deep
  architecture?   Academic administrators and funding agencies must predict the publication
productivity of research groups and individuals to assess authors' abilities.
However, such prediction remains an elusive task due to the randomness of
individual research and the diversity of authors' productivity patterns. We
applied two kinds of approaches to this prediction task: deep neural network
learning and model-based approaches. We found that a neural network cannot give
a good long-term prediction for groups, while the model-based approaches cannot
provide short-term predictions for individuals. We proposed a model that
integrates the advantages of both data-driven and model-based approaches, and
the effectiveness of this method was validated by applying it to a high-quality
dblp dataset, demonstrating that the proposed model outperforms the tested
data-driven and model-based approaches.

22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas