Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Compact Neural Representation Using Attentive Network Pruning   Deep neural networks have evolved to become power demanding and consequently
difficult to apply to small-size mobile platforms. Network parameter reduction
methods have been introduced to systematically deal with the computational and
memory complexity of deep networks. We propose to examine the ability of
attentive connection pruning to deal with redundancy reduction in neural
networks as a contribution to the reduction of computational demand. In this
work, we describe a Top-Down attention mechanism that is added to a Bottom-Up
feedforward network to select important connections and subsequently prune
redundant ones at all parametric layers. Our method not only introduces a novel
hierarchical selection mechanism as the basis of pruning but also remains
competitive with previous baseline methods in the experimental evaluation. We
conduct experiments using different network architectures on popular benchmark
datasets to show high compression ratio is achievable with negligible loss of
accuracy.

31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex