Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ensemble of Convolutional Neural Networks Trained with Different
  Activation Functions   Activation functions play a vital role in the training of Convolutional
Neural Networks. For this reason, to develop efficient and performing functions
is a crucial problem in the deep learning community. Key to these approaches is
to permit a reliable parameter learning, avoiding vanishing gradient problems.
The goal of this work is to propose an ensemble of Convolutional Neural
Networks trained using several different activation functions. Moreover, a
novel activation function is here proposed for the first time. Our aim is to
improve the performance of Convolutional Neural Networks in small/medium size
biomedical datasets. Our results clearly show that the proposed ensemble
outperforms Convolutional Neural Networks trained with standard ReLU as
activation function. The proposed ensemble outperforms with a p-value of 0.01
each tested stand-alone activation function; for reliable performance
comparison we have tested our approach in more than 10 datasets, using two
well-known Convolutional Neural Network: Vgg16 and ResNet50. MATLAB code used
here will be available at https://github.com/LorisNanni.

17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
9
The categories of this paper are: nlin