Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Genetic Algorithm based Kernel-size Selection Approach for a
  Multi-column Convolutional Neural Network   Deep neural network-based architectures give promising results in various
domains including pattern recognition. Finding the optimal combination of the
hyper-parameters of such a large-sized architecture is tedious and requires a
large number of laboratory experiments. But, identifying the optimal
combination of a hyper-parameter or appropriate kernel size for a given
architecture of deep learning is always a challenging and tedious task. Here,
we introduced a genetic algorithm-based technique to reduce the efforts of
finding the optimal combination of a hyper-parameter (kernel size) of a
convolutional neural network-based architecture. The method is evaluated on
three popular datasets of different handwritten Bangla characters and digits.
The implementation of the proposed methodology can be found in the following
link: https://github.com/DeepQn/GA-Based-Kernel-Size.

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The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess