Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Do Deep Nets Really Need to be Deep?   Currently, deep neural networks are the state of the art on problems such as
speech recognition and computer vision. In this extended abstract, we show that
shallow feed-forward networks can learn the complex functions previously
learned by deep nets and achieve accuracies previously only achievable with
deep models. Moreover, in some cases the shallow neural nets can learn these
deep functions using a total number of parameters similar to the original deep
model. We evaluate our method on the TIMIT phoneme recognition task and are
able to train shallow fully-connected nets that perform similarly to complex,
well-engineered, deep convolutional architectures. Our success in training
shallow neural nets to mimic deeper models suggests that there probably exist
better algorithms for training shallow feed-forward nets than those currently
available.

9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg