Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Making Sense of Hidden Layer Information in Deep Networks by Learning
  Hierarchical Targets   This paper proposes an architecture for deep neural networks with hidden
layer branches that learn targets of lower hierarchy than final layer targets.
The branches provide a channel for enforcing useful information in hidden layer
which helps in attaining better accuracy, both for the final layer and hidden
layers. The shared layers modify their weights using the gradients of all cost
functions higher than the branching layer. This model provides a flexible
inference system with many levels of targets which is modular and can be used
efficiently in situations requiring different levels of results according to
complexity. This paper applies the idea to a text classification task on 20
Newsgroups data set with two level of hierarchical targets and a comparison is
made with training without the use of hidden layer branches.

9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom