Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Application of Monte Carlo Stochastic Optimization (MOST) to Deep
  Learning   In this paper, we apply the Monte Carlo stochastic optimization (MOST)
proposed by the authors to a deep learning of XOR gate and verify its
effectiveness. Deep machine learning based on neural networks is one of the
most important keywords driving innovation in today's highly advanced
information society. Therefore, there has been active research on large-scale,
high-speed, and high-precision systems. For the purpose of efficiently
searching the optimum value of the objective function, the author divides the
search region of a multivariable parameter constituting the objective function
into two by each parameter, numerically finds the integration of the two
regions by the Monte Carlo method, compares the magnitude of the integration
value, and judges that there is an optimum point in a small region. In the
previous paper, we examined the problem of the benchmark in the optimization
method. This method is applied to neural networks of XOR gate, and compared
with the results of weight factor optimization by Adam and genetic algorithm.
As a result, it was confirmed that it converged faster than the existing
method.

13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol