Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DropNeuron: Simplifying the Structure of Deep Neural Networks   Deep learning using multi-layer neural networks (NNs) architecture manifests
superb power in modern machine learning systems. The trained Deep Neural
Networks (DNNs) are typically large. The question we would like to address is
whether it is possible to simplify the NN during training process to achieve a
reasonable performance within an acceptable computational time. We presented a
novel approach of optimising a deep neural network through regularisation of
net- work architecture. We proposed regularisers which support a simple
mechanism of dropping neurons during a network training process. The method
supports the construction of a simpler deep neural networks with compatible
performance with its simplified version. As a proof of concept, we evaluate the
proposed method with examples including sparse linear regression, deep
autoencoder and convolutional neural network. The valuations demonstrate
excellent performance.
  The code for this work can be found in
http://www.github.com/panweihit/DropNeuron

29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin