Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Auto-Sizing Neural Networks: With Applications to n-gram Language Models   Neural networks have been shown to improve performance across a range of
natural-language tasks. However, designing and training them can be
complicated. Frequently, researchers resort to repeated experimentation to pick
optimal settings. In this paper, we address the issue of choosing the correct
number of units in hidden layers. We introduce a method for automatically
adjusting network size by pruning out hidden units through $\ell_{\infty,1}$
and $\ell_{2,1}$ regularization. We apply this method to language modeling and
demonstrate its ability to correctly choose the number of hidden units while
maintaining perplexity. We also include these models in a machine translation
decoder and show that these smaller neural models maintain the significant
improvements of their unpruned versions.

9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th