Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Empirical analysis of non-linear activation functions for Deep Neural
  Networks in classification tasks   We provide an overview of several non-linear activation functions in a neural
network architecture that have proven successful in many machine learning
applications. We conduct an empirical analysis on the effectiveness of using
these function on the MNIST classification task, with the aim of clarifying
which functions produce the best results overall. Based on this first set of
results, we examine the effects of building deeper architectures with an
increasing number of hidden layers. We also survey the impact of using, on the
same task, different initialisation schemes for the weights of our neural
network. Using these sets of experiments as a base, we conclude by providing a
optimal neural network architecture that yields impressive results in accuracy
on the MNIST classification task.

24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph