Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Heuristic for Efficient Reduction in Hidden Layer Combinations For
  Feedforward Neural Networks   In this paper, we describe the hyper-parameter search problem in the field of
machine learning and present a heuristic approach in an attempt to tackle it.
In most learning algorithms, a set of hyper-parameters must be determined
before training commences. The choice of hyper-parameters can affect the final
model's performance significantly, but yet determining a good choice of
hyper-parameters is in most cases complex and consumes large amount of
computing resources. In this paper, we show the differences between an
exhaustive search of hyper-parameters and a heuristic search, and show that
there is a significant reduction in time taken to obtain the resulting model
with marginal differences in evaluation metrics when compared to the benchmark
case.

11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat