Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Encouraging an Appropriate Representation Simplifies Training of Neural
  Networks   A common assumption about neural networks is that they can learn an
appropriate internal representations on their own, see e.g. end-to-end
learning. In this work we challenge this assumption. We consider two simple
tasks and show that the state-of-the-art training algorithm fails, although the
model itself is able to represent an appropriate solution. We will demonstrate
that encouraging an appropriate internal representation allows the same model
to solve these tasks. While we do not claim that it is impossible to solve
these tasks by other means (such as neural networks with more layers), our
results illustrate that integration of domain knowledge in form of a desired
internal representation may improve the generalization ability of neural
networks.

12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph