Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training Feedforward Neural Networks with Standard Logistic Activations
  is Feasible   Training feedforward neural networks with standard logistic activations is
considered difficult because of the intrinsic properties of these sigmoidal
functions. This work aims at showing that these networks can be trained to
achieve generalization performance comparable to those based on hyperbolic
tangent activations. The solution consists on applying a set of conditions in
parameter initialization, which have been derived from the study of the
properties of a single neuron from an information-theoretic perspective. The
proposed initialization is validated through an extensive experimental
analysis.

4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin