Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Analysis of State-of-the-art Activation Functions For Supervised Deep
  Neural Network   This paper provides an analysis of state-of-the-art activation functions with
respect to supervised classification of deep neural network. These activation
functions comprise of Rectified Linear Units (ReLU), Exponential Linear Unit
(ELU), Scaled Exponential Linear Unit (SELU), Gaussian Error Linear Unit
(GELU), and the Inverse Square Root Linear Unit (ISRLU). To evaluate,
experiments over two deep learning network architectures integrating these
activation functions are conducted. The first model, basing on Multilayer
Perceptron (MLP), is evaluated with MNIST dataset to perform these activation
functions. Meanwhile, the second model, likely VGGish-based architecture, is
applied for Acoustic Scene Classification (ASC) Task 1A in DCASE 2018
challenge, thus evaluate whether these activation functions work well in
different datasets as well as different network architectures.

36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex