Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The observer-assisted method for adjusting hyper-parameters in deep
  learning algorithms   This paper presents a concept of a novel method for adjusting
hyper-parameters in Deep Learning (DL) algorithms. An external agent-observer
monitors a performance of a selected Deep Learning algorithm. The observer
learns to model the DL algorithm using a series of random experiments.
Consequently, it may be used for predicting a response of the DL algorithm in
terms of a selected quality measurement to a set of hyper-parameters. This
allows to construct an ensemble composed of a series of evaluators which
constitute an observer-assisted architecture. The architecture may be used to
gradually iterate towards to the best achievable quality score in tiny steps
governed by a unit of progress. The algorithm is stopped when the maximum
number of steps is reached or no further progress is made.

12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con